Research on IEEE 754 Standard Single Precision Floating Point Multipliers Designed using Urdhva Triyagbhyam Sutra of Vedic Mathematics

Duplication of the coasting element numbers is the big activity in automated signal handling. So the exhibition of drifting problem multipliers count on a primary undertaking in any computerized plan. Coasting factor numbers are spoken to utilizing IEEE 754 modern day in single precision(32-bits), Double precision(sixty four-bits) and Quadruple precision(128-bits) organizations. Augmentation of those coasting component numbers can be completed via using Vedic generation. Vedic arithmetic encompass sixteen wonderful calculations or Sutras. Urdhva Triyagbhyam Sutra is most usually applied for growth of twofold numbers. This paper indicates the compare of tough work finished via exceptional specialists in the direction of the plan of IEEE 754 ultra-modern-day unmarried accuracy skimming thing multiplier the usage of Vedic technological statistics. Record phrases—Floating component multiplier, unmarried accuracy, Sutra, Urdhva Triyagbhyam, Vedic arithmetic

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I. INTRODUCTION
Multiplication of floating point binary numbers is the most important operation in DSP applications. IEEE 754 standard provides formats for the FP numbers. IEEE 754 standard format for single precision(32-bit) FP number consist of a Sign unit(1-bit), Exponent unit(8-bits) and Mantissa unit(8-bits) as shown in fig.1.

Fig.1: IEEE 754 Single Precision Format
MSB's Of 32-piece numbers are XORed to create signal piece of the object. shape of the item is determined through which encompass examples of the information belongings utilizing deliver appearance in advance(CLA) viper and The 3x3 multiplier can be implemented for developing better request multipliers, as an instance, 6x6, 12x12 and 24x24 piece duplication.

II. PRECEDING PAINTINGS
Swapnil Suresh Mohite, Sanket Sanjay Nimbalar, Madhav Makarand Bhathande, Rashmi Rahul Kulkarni displayed the form of 32-piece skimming aspect multiplier using Urdhva Triyagbhyam sutra [1] which lessens the making ready delay. Code changed into written in VHDL using Xilinx ISE affiliation. via way of and big execution of planned multiplier is predicated on the presentation of mantissa multiplier unit. Mantissa multiplier became established using Urdhva Triyagbhyam sutra. 3x3 multiplier changed into carried out as essential multiplier. eight-piece CLA is carried out for which include eight-piece kind. Yield of the viper turn out to be one-sided to -127 to deliver the form of yield coasting element amount. The proposed multiplier circuit takes seventy one.239ns to perform increase of 32-piece gliding factor parallel numbers. This postponement is largely no longer as a brilliant deal as sales space multiplier.
Sneha khobragade, Mayur Dhait [2] mixed and checked IEEE 754 unmarried precision(32-bit) coasting point multiplier making use of VHDL on Xilinx programming. Urdhva Triyagbhyam calculation changed into carried out for structuring of mantissa multiplier. The proposed form treated flood, sub-modern-day-day and adjusting conditions. speedy turn out to be carried out with the aid of diminishing the supply unfold deferral with the beneficial useful resource of utilizing RCA on the identical time as planning 24x24 mantissa multiplier for 32-piece drifting issue multiplier. example unit end up deliberate the use of RCA.
Aniruddha kanhe, Shishir Kumar Das, Ankit Kumar Singh portrayed the shape and execution of IEEE 754, 32-piece coasting component multiplier [3] utilizing vedic mathematics. The Urdhva Triyagbhyam sutra have become performed for mantissa increase. increase have become finished through which encompass the best-sided eight-piece type, duplicating the standardized 24-piece mantissa and resultant turned into changed over in overabundance 127 piece organization. the instance computation unit changed into actualized the use of 8-piece RCA. sign piece became decided thru XORing the MSB's of the belongings of statistics. The multiplier modified into set up in Verilog HDL and recreated the usage of Modelsim check device. This form changed into orchestrated using Xilinx ISE12.1 device targeted on the Xilinx Vertex5. The plan uses lesser giant kind of LUT's, consequently diminishes the electricity usage. Soumya Havaldar, adequate S Gurumurthy [4] proposed the structure of multiplier for drifting thing numbers using vedic generation. This shape likewise oversees flood, sub-modern-day-day and adjusting. Configuration was coded in VHDL, reproduced and orchestrated utilising ISE14.6 tool focusing at the Xilinx VertexVI FPGA. This artwork infers that; the proposed plan consumes an awful lot much less location and excessive strolling fee because of vertical and for the duration of figuring making use of Urdhva Triyagbhyam sutra.
Pooja Hatwalne, Ameya Deshmukh, Tanmay Paliwal, Krupal Lambat proposed a plan and execution of unmarried exactness coasting point multiplier using VHDL [5]. 24-piece multiplier using Urdhva Triyagbhyam sutra of vedic arithmetic grow to be meant for mantissa figuring. eight-piece CLA viper emerge as applied for example estimation. The plan changed into combined and mimicked in Xilinx ISE14.7 centered on Spartan3 device. The proposed gliding point multiplier validated upgraded and better planning execution with all out postponement of 36.19ns. Sushma S Mahakalkar, Sanjay L Haridas [6] blended and checked IEEE 754 skimming aspect multipliers the usage of VHDL on Xilinx Virtex5 FPGA. The Urdhva Triyagbhyam sutra come to be carried out for mantissa computation. speedy became achieved by using diminishing the carry unfold deferral with the usage of CSA for the shape of 12x12 multiplier. The proposed plan confirmed speedy and lots less location even as contrasted with conventional multipliers. The deferral of proposed configuration changed into 4.94ns.
Pratheeksha Rai, Shailendra Kumar, Prof. S H Saeed proposed the plan of speedy gliding factor multiplier [7] the use of the calculations of antiquated Indian vedic mathematics. IEEE 754 famous gliding element multiplier grow to be established the use of vedic era. It takes out superfluous increase ventures with zeros, empowers the parallel age of middle of the street gadgets and scaled to higher piece levels utilizing Karatsuba sutra. Proposed multiplier became planned making use of Xilinx ISE device and reproduced the use of Modelsim10.2a, directed on Spartan3E. The spread deferral inside the improved vedic coasting element multiplier have become 4.788ns. Creators inferred that form unpredictability of proposed multiplier receives diminished for contributions of large variety of bits and measured quality gets extended. usage of Urdhva Triyagbhyam sutra reduced the device necessities and deferral for skimming detail multipliers. The proposed multiplier suggests progressed expertise as some distance as pace.
Irine Padma B T, Suchitra good enough portrayed the plan and execution of IEEE754 pipelined 32-piece gliding point multiplier [8] dependent on Urdhva Triyagbhyam sutra of vedic technological know-how. The proposed plan turn out to be actualized making use of VHDL and orchestrated for a Xilinx ISE12.1. The vedic growth approach have become picked for the execution of mantissa figuring unit. the example estimation unit changed into actualized using 8-piece Ripple deliver Adder. This art work infers that, idea of pipelining diminishes the amount of LUT's which decreases the device prerequisite.
Arish S, R adequate Sharma [9] proposed a efficient technique for IEEE 754 drifting point(FP) duplication with higher deferral and electricity. The charge increment in do away with and of a FP multiplier. ok Veeraraju, B Sujatha proposed the usage of a advanced 32-piece coasting factor multiplier using quick multipliers and adders [10]. The proposed IEEE 754 preferred gliding factor multiplier end up actualized in Verilog HDL utilising Xilinx10.1 ISE venture tool and targeted to Vertex5 FPGA. Urdhva Triyagbhyam sutra of vedic mathematics changed into implemented to form mantissa rely of drifting factor multiplier alternative in assessment to the Wallace tree multiplier. The proposed form showed a whole lot less postponement.
I V Vaibhav, ok V Saicharan, B Sravanthi, D Srinivasulu [11] exhibited IEEE 754 good sized, 32-piece gliding issue multiplier relying on vedic calculation. Urdhva Triyagbhyam sutra of vedic arithmetic end up applied to actualize 24x24 piece mantissa multiplier. The multiplier changed into deliberate in Verilog HDL and mimicked the usage of I-take a look at system. The plan became combined utilizing Xilinx12.1 tool focusing on the Xilinx Spartan3E FPGA. The proposed form used lesser considerable style of LUT's, which reduced the gadget necessity there via faded the electricity usage.
Kusuma Keerti proposed the plan of unmarried accuracy coasting factor multiplier [12] utilizing brief multipliers and quick adders. Vedic augmentation modified into implemented for mantissa duplication unit and observed to have 33% decrease in entryway encompass and 25% development in velocity whilst contrasted with skimming difficulty multipliers hooked up using Wallace tree with Kogge-stone viper. Proposed configuration have grow to be actualized in VHDL using Xilinx10.1 ISE duplicate equipment centered to Vertex4 FPGA with speed evaluation of -12.
Ajay A Raut, Dr. Pravin. ok. Dahole displayed the plan of IEEE 754 single exactness skimming element multiplier using vertically and transversely set of rules [13]. Proposed configuration modified into actualized in Verilog HDL and focused on Vertex5 FPGA in Xilinx13.1 ISE programming. The contributions to the multiplier changed into 32-piece coasting point numbers and end result become sixty 4-bits on the way to improve the blunder. Reenactment outcomes display that use of variety of LUT's was 1083 and range of strengthened IOB's modified into 128.  Comparative charts in terms of number of slices, LUT's, bonded IOB's and time delay is shown in Fig. 4 Delay(ns)

III. CONCLUSION
IEEE 754 today's single exactness gliding factor quantity configuration contains of sign(1-bit), Mantissa(23-bits) and Exponent(eight-bits) fields. the general execution of coasting factor multiplier commonly is based totally upon the exhibition of Mantissa depend unit. Mantissa rely unit will increase unsigned parallel numbers. Urdhva Triyagbhyam sutra changed into carried out for Mantissa augmentation. unique analysts proposed their systems for gliding aspect augmentation. those structures were belief about as an prolonged way as range of cuts, LUT's, reinforced IOB's and time delays. The proposed models can be better regarding device utilization and deferral with the aid of the use of further research.


Road train motion stability in BRT system

The peculiarities of organization and perspectives of mass passenger transportation in the city and beyond are considered with the use of "Bus Rapid Transport" (BRT) or Metrobus. Different aspects of study of motor vehicles (MV) controllability and stability are analyzed. It is substantiated that it is sufficient to consider the potential stability of the MV itself, in order to guarantee the stability of the "driver MV" system with a large reserve. A mathematical model of a three-axle bus train consisting of a bus and two trains (metrobus) is developed and the factors influencing the critical speed as the main index of the stability of its movement are determined. It is established that the increase of the critical speed of the metrobus can be achieved by increasing the base of the bus, the first and the second trailer, as well as the mass of the bus and the coefficients of resistance of the drive wheels of the bus driving axle and the trailers axles. At the same time, increasing the distance from the mass center to the bus rear axle, increasing the distance from the mass center to the point of the coupling of the bus with the first trailer, increasing the mass of trailers and the resistance of the resistance of the wheel drive of the bus axis lead to a decrease in the critical speed of the metrobus. This must be taken into account both when designing metrobuses, and when operating them. 1 Formulation of the problem Metrobus or "Bus Rapid Transport" (BRT) is the name of a rather complex system of bus traffic [1]. The BRT project involves the movement of buses on specially assigned and often fenced traffic lanes. The main advantage of the metrobus is its complete isolation on the road from other modes of transport. As a means of transportation, the latest generation of articulated buses equipped with engines up to 300 kW is selected. At that, in the salons of the metrobus the preference is given to the places for standing, as in the subway. Due to this, one double-articulated road train transports up to 200 passengers. * Corresponding author: juraj.gerlici@fstroj.uniza.sk Reviewers: Konrad Waluś, Josef Soukup © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/). MATEC Web of Conferences 254, 03007 (2019) https://doi.org/10.1051/matecconf/201925403007 MMS 2018 The record capacity metrobus, the AKIA superlong double-articulated metrobus, designed to carry nearly three hundred passengers, was presented at Busworld Turkey 2016 in Istanbul. The AKIA Ultra LF25 metrobus has a length of 25 meters and can carry a record (in this class) number of passengers, namely 290. There are 29 seats in it. This vehicle is specifically designed for the assigned lane of high-speed bus service BRT, which has been operating successfully in some of the largest cities in the world, including Istanbul. The technical characteristics of the metrobus are still few it is only known that it is equipped with a turbodiesel engine Mercedes-Benz of the environmental standard Euro 6. Superlong metrobus Ultra LF25 can be produced not only with a diesel but also with a hybrid power unit. Moreover, the trolleybus option is also possible. In the latter case, it is equipped with two traction electric motors with a capacity of 160 kW and with rods on the roof [2]. However, the world's largest metrobus was shown in Sweden. The Swedish automaker Scania introduced a double-articulated bus model, powered by natural gas. The model complies with Euro-6 standards. It was presented at the Busworld Latin America international exhibition in Medellin, Colombia. The bus chassis has a length of 28 meters. The body was manufactured by Brazilian company Busscar. The passenger compartment of the vehicle can accommodate up to 300 passengers. The BRT system has a number of undeniable benefits: High passenger capacity and efficient payment systems provide low-cost travel. High speed of movement allows the metrobus to transport a significant share of passenger traffic, which contributes to reducing the number of motor transport on the city roads and, accordingly, reducing emissions of waste gases. An expanded information system informs passengers about the schedules of the routes. Currently, more and more cities are choosing a system of the bus rapid transport for important reasons such as cost and convenience. The cost of construction of such a widegauge railroad transport, as a subway, is 10 times higher than that of BRT. Moreover, installing this system in the cities that choose the BRT system can take 2 years, and the construction of the subway can be lingered for decades. Today, metrobuses operate in several countries: USA, Brazil, Venezuela, Colombia, Guatemala, Canada, Mexico, Australia, New Zealand, Japan, Iran, Turkey, France, Czech Republic, etc. This list is quickly updated, as more cities are taking decisions in favor of the BRT system. In different cities and countries, the system may look differently: somewhere they build special stopping points with the platform, while others use small stop stations. There is no single standard anywhere, each city solves its problems in its own right, taking into account the geographical location of the city, the flow of passenger traffic, and the development of the city. But all agree on one thing BRT is an excellent assistant in solving ecological and transport problems [3]. But for its implementation it is necessary to solve a number of problems associated with the choice of the highway for the metrobus, the choice of rolling stock, etc. In the works [4, 5] the indicators of maneuverability of a double-articulated road train are determined both on rigid and elastic laterally oriented wheels. In particular, it is shown that the overall lane of such a train with a total length of 25 m is about 10 m, that is, in order to move, such train needs its own fenced lane, on which the metrobus can move with the maximum speed provided with both the traction capabilities of the leading link and the stability of the movement. 2 Analysis of literary sources In the works known today, issues of controllability and stability are considered in two aspects [6-15]: 2 MATEC Web of Conferences 254, 03007 (2019) https://doi.org/10.1051/matecconf/201925403007 MMS 2018 1. The research taking into account the characteristics of all elements of the "driver-car-road" system, which is considered as a closed-loop system of automatic control [14]. 2. The research of the stability and controllability of a vehicle (road train), in which the driver's influence is excluded. In the first group of works, the problem is most commonly considered in general, that is, it explores simultaneously both controllability and stability in this complex system of obtaining, processing and transmitting of information, the formation of control impacts, aimed at reducing the deviations of both the tractor unit and trailer cars from the desired process. In this case, the meaning used in the theory of automatic control is added to the concepts of controllability and stability. Studied in such a way, the controllability consider one or another characteristic of transient processes under the simplest typical control influences. Stability characterizes the behavior of the system in the transition mode and relates primarily to the system's own movements, generated by the initial conditions (perturbations) and its internal properties, but not by external influences. Stability is considered in relation to any process, both controlled and uncontrolled [14]. In the second group of works, the vehicle is considered isolated as an object of controlling, and the stability of the driver motor vehicle (MV) system is determined by the stability of the vehicle and the driver's psychophysical capabilities, as well as by the level and nature of the existing perturbations. As shown by the calculations performed for different types of MV, the presence of a closed control scheme can increase their critical speed of motion by 1.5-2 times and increase the number of the links, the stability of which can be secured [14]. Thus, if the stability of the MV is ensured, then the stability of the driver MV system is also provided with a large margin. Therefore, it is sufficient to consider the motion at the open-loop control, that is, the potential stability of the vehicle itself. Stability research is carried out in two main ways [15]: Numerical integration of the obtained equations of motion for given perturbations in their right-hand sides. Thus, the experimental indices are determined. Qualitative study of stability in solving equations of motion by one of the methods of academician Oleksandr Lyapunov or his followers, while finding the critical speed, which determines the conditions for the transition from stable motion to unstable. The use of either method requires the presence of differential equations of motion of a double-articulated road train. The purpose of the work is to develop a mathematical model of a double-articulated road train consisting of a bus and two trailers (metrobus) and to determine the factors that influence the critical speed as the main indicator of the stability of its movement. 3 Research results In the paper [8] the system of equations of a double-articulated trailed road train (Fig. 1) is presented in the form (where mi are the masses of individual parts of the road train; Мі are the moments of resistance to the rotation of individual parts of the road train; с is the distance from the rear axle of the truck tractor to the coupling point; а is the distance from the center of mass of the truck tractor to its front axle; b1і is the distance from the center of mass of the truck tractor to its middle and rear axles; di, li is the distance from the center of mass of individual modules to the front axle and the coupling points respectively; Y1, Y1i, Y2j, Y4 are the lateral forces acting on the wheels of the front, rear axle of the truck tractor, the first and the second trailers respectively; b2j, b4 is the distance from the center of mass of the first and the second trailers to their rear axles respectively): 3 MATEC Web of Conferences 254, 03007 (2019) https://doi.org/10.1051/matecconf/201925403007 MMS 2018

The record capacity metrobus, the AKIA superlong double-articulated metrobus, designed to carry nearly three hundred passengers, was presented at Busworld Turkey 2016 in Istanbul. The AKIA Ultra LF25 metrobus has a length of 25 meters and can carry a record (in this class) number of passengers, namely 290. There are 29 seats in it. This vehicle is specifically designed for the assigned lane of high-speed bus service BRT, which has been operating successfully in some of the largest cities in the world, including Istanbul. The technical characteristics of the metrobus are still few -it is only known that it is equipped with a turbodiesel engine Mercedes-Benz of the environmental standard Euro 6.
Superlong metrobus Ultra LF25 can be produced not only with a diesel but also with a hybrid power unit. Moreover, the trolleybus option is also possible. In the latter case, it is equipped with two traction electric motors with a capacity of 160 kW and with rods on the roof [2]. However, the world's largest metrobus was shown in Sweden. The Swedish automaker Scania introduced a double-articulated bus model, powered by natural gas. The model complies with Euro-6 standards. It was presented at the Busworld Latin America international exhibition in Medellin, Colombia. The bus chassis has a length of 28 meters. The body was manufactured by Brazilian company Busscar. The passenger compartment of the vehicle can accommodate up to 300 passengers.
The BRT system has a number of undeniable benefits: -High passenger capacity and efficient payment systems provide low-cost travel.
-High speed of movement allows the metrobus to transport a significant share of passenger traffic, which contributes to reducing the number of motor transport on the city roads and, accordingly, reducing emissions of waste gases.
-An expanded information system informs passengers about the schedules of the routes.
Currently, more and more cities are choosing a system of the bus rapid transport for important reasons such as cost and convenience. The cost of construction of such a widegauge railroad transport, as a subway, is 10 times higher than that of BRT. Moreover, installing this system in the cities that choose the BRT system can take 2 years, and the construction of the subway can be lingered for decades.
Today, metrobuses operate in several countries: USA, Brazil, Venezuela, Colombia, Guatemala, Canada, Mexico, Australia, New Zealand, Japan, Iran, Turkey, France, Czech Republic, etc. This list is quickly updated, as more cities are taking decisions in favor of the BRT system. In different cities and countries, the system may look differently: somewhere they build special stopping points with the platform, while others use small stop stations. There is no single standard anywhere, each city solves its problems in its own right, taking into account the geographical location of the city, the flow of passenger traffic, and the development of the city. But all agree on one thing -BRT is an excellent assistant in solving ecological and transport problems [3]. But for its implementation it is necessary to solve a number of problems associated with the choice of the highway for the metrobus, the choice of rolling stock, etc.
In the works [4,5] the indicators of maneuverability of a double-articulated road train are determined both on rigid and elastic laterally oriented wheels. In particular, it is shown that the overall lane of such a train with a total length of 25 m is about 10 m, that is, in order to move, such train needs its own fenced lane, on which the metrobus can move with the maximum speed provided with both the traction capabilities of the leading link and the stability of the movement.

Analysis of literary sources
In the works known today, issues of controllability and stability are considered in two aspects [6][7][8][9][10][11][12][13][14][15]: 1. The research taking into account the characteristics of all elements of the "driver-car-road" system, which is considered as a closed-loop system of automatic control [14]. 2. The research of the stability and controllability of a vehicle (road train), in which the driver's influence is excluded.
In the first group of works, the problem is most commonly considered in general, that is, it explores simultaneously both controllability and stability in this complex system of obtaining, processing and transmitting of information, the formation of control impacts, aimed at reducing the deviations of both the tractor unit and trailer cars from the desired process. In this case, the meaning used in the theory of automatic control is added to the concepts of controllability and stability. Studied in such a way, the controllability consider one or another characteristic of transient processes under the simplest typical control influences. Stability characterizes the behavior of the system in the transition mode and relates primarily to the system's own movements, generated by the initial conditions (perturbations) and its internal properties, but not by external influences. Stability is considered in relation to any process, both controlled and uncontrolled [14].
In the second group of works, the vehicle is considered isolated as an object of controlling, and the stability of the driver -motor vehicle (MV) system is determined by the stability of the vehicle and the driver's psychophysical capabilities, as well as by the level and nature of the existing perturbations. As shown by the calculations performed for different types of MV, the presence of a closed control scheme can increase their critical speed of motion by 1.5-2 times and increase the number of the links, the stability of which can be secured [14].
Thus, if the stability of the MV is ensured, then the stability of the driver -MV system is also provided with a large margin. Therefore, it is sufficient to consider the motion at the open-loop control, that is, the potential stability of the vehicle itself. Stability research is carried out in two main ways [15]: -Numerical integration of the obtained equations of motion for given perturbations in their right-hand sides.
-Thus, the experimental indices are determined.
-Qualitative study of stability in solving equations of motion by one of the methods of academician Oleksandr Lyapunov or his followers, while finding the critical speed, which determines the conditions for the transition from stable motion to unstable.
The use of either method requires the presence of differential equations of motion of a double-articulated road train. The purpose of the work is to develop a mathematical model of a double-articulated road train consisting of a bus and two trailers (metrobus) and to determine the factors that influence the critical speed as the main indicator of the stability of its movement.

Research results
In the paper [8] the system of equations of a double-articulated trailed road train ( Fig. 1) is presented in the form (where mi are the masses of individual parts of the road train; Мі are the moments of resistance to the rotation of individual parts of the road train; с is the distance from the rear axle of the truck tractor to the coupling point; а is the distance from the center of mass of the truck tractor to its front axle; b1і is the distance from the center of mass of the truck tractor to its middle and rear axles; di, li is the distance from the center of mass of individual modules to the front axle and the coupling points respectively; Y1, Y1i, Y2j, Y4 are the lateral forces acting on the wheels of the front, rear axle of the truck tractor, the first and the second trailers respectively; b2j, b4 is the distance from the center of mass of the first and the second trailers to their rear axles respectively): ; .

Fig. 1. The scheme of unsteady rotation of the metrobus
The stability criterion is the critical speed Vkr that depends on the design parameters of the metrobus and operational factors. The exceeding of the critical speed leads to the loss of stability of the motor vehicle. By setting the law of changing the angle of rotation of the controlled wheels of the bus, one can find, by numerical integration of the equations of motion, the values of the parameters of the metrobus movement when performing various maneuvers. However, to solve the stability problem, the conditions of the existence of the movement parametres are more important. If the metrobus was perturbed at some moment, then the initial conditions will change. The shape of the movement will also change, and unlike the original, it will also become perturbed. The comparison of the equation systems of unperturbed and perturbed conditions allows to obtain the differential equations of perturbed  in the function of the angle of rotation of the controlled wheels of the bus corresponding to the steady motion can be obtained from the system of equations of stationary motions of the metrobus with four kinematically independent elements on a circular trajectory of a sufficiently large radius, provided that V = const [16]. By the linearity of the lateral deviation forces in the function of the deviation angle we obtain: ( Taking into account the expressions that determine the lateral forces and the deviation angles of the axes of the road trains, the system of equations (1) 3  3  3   2 2  2  2  2  2  2  2  2  2  2  2  2  1  1  1   33   1  2  2  1 1  2  2  1  2  2  11   3  3  3   3 4  4 3  3  4  4  3  4  3  4  2  3  4  4  1 1 1 .
The roots of the characteristic equations can be determined by numerical methods. It should also be noted that the description of the motor vehicle movement, which is really a non-linear object, by linear equations is a substitution of one task by another, with which the former may not have anything in common (due to the non-consideration of the nonlinearity of deviation and the members of higher than the first order of motion equations).
Hence the following problem arises: to establish the necessary and sufficient conditions of stability at the first approximation. According to Oleksandr Lyapunov's theorem on the stability of the stable motion at the first approximation [15], if all the roots of the characteristic equation of the system of the first approximation of the perturbed motion equations have negative valid parts, then the unperturbed motion is stable and, moreover, asymptotically stable, no matter what the members of higher orders in the differential equations of perturbed motion are.
The conditions under which all the roots have negative valid parts are determined by the Lyenar-Shippar criterion [16]: in order for the characteristic equation to have all the roots with negative valid parts, it is necessary and sufficient that: 1. All the coefficients of the characteristic equation were positive. 2. The main diagonal minors of the Gurvits matrix, compiled for this characteristic equation, were positive.
These conditions are satisfied in the case of the affinity of the denominator ω (4), that is, the affinity of the denominator of the principal determinant of the system, which has the form / ( ).
To determine the coefficients  and , consider the column 2 ( 1, 5) i ai  in Table 1, which contains the coefficients at angular velocity . Each of them consists of two addend: in one of them the speed is in the numerator, in the second one -in the denominator.
then  and  in (5) will be defined as: The solution of the equation (5) is found using the Mapple 14 software. Taking into account the selected parameters of the road train, the speed of its movement, which has been steadily increasing, was set. In this case, the roots of the characteristic equation were found and the stability conditions were checked (5), (6). Thus, at the speed of 37.9 m/s, the roots of the characteristic equation were: However, at a much lower speed V*=34,6 m/s, a couple of complex conjugate roots 7,8 are transferred into a positive half-plane, which means the appearance of oscillatory instability when moving with velocities V>V*. The emergence of this type of instability is associated with fluctuations of the links, which lead to the stability loss of the entire system, i.e. the stability loss of the metrobus. It was shown in [17,18] that the critical speed of a road train is significantly influenced by its design parameters. Therefore, further by means of the solution of the system of differential equations of the metrobus movement, the influence of pressure in the tires of the wheels of trailers axles, the location of the trailers center of mass, distances from the center of mass of the bus с and the trailer с1 to the coupling points, the base of trailers and the mass of the bus and trailers on the magnitude of the critical speed of movement were analysed.
In order to extend the results of the study of the influence of constructive factors and modes of motion on the stability indicators to the study of double-articulated metrobuses, it was suggested to make calculations using dimensionless parameters. The range of change of dimensionless geometric parameters has been chosen taking into account the features of the structures of existing metrobuses and is within the range of 0.8...1.2. It was considered from the following geometric factors: where а is the distance from the bus center of mass to the front axle; с is the distance from the bus center of mass to the coupling point with the first trailer; с1 is the distance from the center of mass of the first trailer to the coupling point with the second trailer; L, L1, L2 are, respectively, the base of the bus, the first and the second trailer; D, D1, D2 are, respectively, the length of the bus, the first and the second trailer.
The results of calculating the influence of geometric factors on the overall traffic lane are shown in Fig. 2. a b Fig. 2. The influence of relative parameters of the bus (a) and trailers (b) on the critical speed of the metrobus Data analysis, Fig. 2, allows us to draw the following conclusions: -An increase in the base of the bus, the first and the second trailers leads to an increase in the critical speed of the metrobus. -An increase in the distance from the center of mass to the rear axle of the bus, an increase in the distance from the center of mass to the coupling point of the bus with the first trailer leads to a decrease in the critical speed of the metrobus, while an increase in the distance from the center of mass of the first trailer to the coupling point with the second trailer increases this speed. The influence of the mass factors of the metrobus links and the coefficients of resistance to the lateral deviation of the metrobus links is shown in Fig. 3, 4. The mass of the bus, trailers and coefficients of resistance to the lateral deviation of the link wheels varied from 0.8 to 1.2:  1   2  3  4  1  2  3  4   0 , , , , , , 0.8...1.2, k k k k k k k k k  (11) where: m1, m2, m3 are, respectively, the mass of the bus, the first and the second trailer, k1, k2, k3, k4, k0 are, respectively, the coefficients of resistance of the deviation of the wheels of the driving and guided axles of the bus and the axles of the first and the second trailers. Data analysis (Figures 3, 4) allows us to draw the following conclusions: -An increase in the critical speed of the metrobus is caused by an increase in the mass of the bus and the coefficients of resistance to the deviation of the wheels of the bus driving axle and the trailers axles.
-A decrease in the critical speed of the metrobus is caused by an increase in the mass of the trailers and the coefficient of the resistance to the deviation of the wheels of the bus controlled axle.
The obtained dependencies should be taken into account both when designing metrobuses, and when operating them.


A Blended Human-Robot Shared Control Framework to Handle Drift and Latency

Maximizing the utility of human-robot teams in disaster response and search and rescue (SAR) missions remains to be a challenging problem. This is due to the dynamic, uncertain nature of the environment and the variability in cognitive performance of the human operators. By having an autonomous agent share control with the operator, we can achieve near-optimal performance by augmenting the operator's input and compensate for the factors resulting in degraded performance. What this solution does not consider though is the human input latency and errors caused by potential hardware failures that can occur during task completion when operating in disaster response and SAR scenarios. In this paper, we propose the use of blended shared control (BSC) architecture to address these issues and investigate the architecture's performance in constrained, dynamic environments with a differential drive robot that has input latency and erroneous odometry feedback. We conduct a validation study (n=12) for our control architecture and then a user study (n=14) in 2 different environments that are unknown to both the human operator and the autonomous agent. The results demonstrate that the BSC architecture can prevent collisions and enhance operator performance without the need of a complete transfer of control between the human operator and autonomous agent.

I. INTRODUCTION
Shared control within the context of robotics has been studied as early as the late 1980s [1]- [7]. Early work was based on implementations where the robot control was transferred between teleoperation and autonomous modes with the goals of addressing transmission time-delays [2], developing context-aware navigation procedures, and characterizing efficiency in human-robot interaction [5]. Now, the research in shared control seeks to address the question: how can we treat human-robot teams as a whole? The collaborative architecture of shared control makes it ideal for applications where the human's control is augmented by the autonomous agent or vice-versa. As such, shared control is a topic of interest in assistive and service robotics [8]- [10].
In disaster response and SAR applications, human teleoperation of a robot is usually characterized by high latency, poor visual & sensory feedback, and limited time for task completion. Furthermore, the human operator must avoid collisions and account for dynamic changes in the environment. The challenges poised against the human operator lead to degraded performance and situational awareness [11]. On the other hand, autonomous agents alone have difficulty traversing these dynamic environments. Rather than having the agent and operator work separately, we can have humanrobot teams collaborate via a shared control architecture on a certain task, complementing one other's inefficiencies.
There has, however, been limited research on the application of model-based shared control in the context of robotics navigating in a constrained environment such as a factory or disaster environment, specifically when environmental disturbances such as latency (i.e. poor signal) and erroneous sensor feedback (i.e. damaged hardware) are not taken in consideration. Approaches towards semi-autonomy using reinforcement learning [12] and hierarchical reinforcement learning [13], [14] were made, however, with a focus on deciding when the autonomous agent should cede control to a human operator and not necessarily augmenting their input in real time. Only recently has [15] conducted an analysis on blending human and autonomous agent inputs using model-free deep reinforcement learning. [15] also consider an autonomous agent with delayed input, but do not consider erroneous feedback from the robot or delayed input from the operator, variables that we account for in our study.
While a machine learning approach could be taken to design a shared control architecture that addresses latency and noise, it is difficult to obtain enough training data and construct high fidelity simulators to collect said data in order to have a high performance learning algorithm [15]. Furthermore, it is also difficult to incorporate all physicsbased failure modes that can result in the drift, a focal point in this research. Thus, a model-based shared control architecture would be more fit to the task.
The lack of understanding of how shared control approaches degrade under latency and drift disturbances is a significant barrier for their adoption. The main contribution of this work is quantifying the degradation of performance with input latency and drift disturbances in combination with a linear blended shared control approach.
The paper is organized as follows. In section II we discuss the shared control architecture used and the overall methodology for validating the architecture in various environments. In section III, we formalize our blended shared control architecture and our choice of the blending parameter. IV details the experimental setup of the user study to quantify the performance of the approach in time delay and drift scenarios. Section V and VI analyzes the performance of the approach in two real world tasks: a doorway traversal and navigation in a constrained environment, respectively. Finally, Section VII highlights the future work necessary before shared control could be reliably used in real-world scenarios involving time delay and drift.

II. APPROACH & METHODOLOGY
The collaborative nature of blended shared control makes it a control architecture suitable for when the operator is impaired in some manner. If the operator's input would lead to a task failure or damage to the robot, the autonomous agent can "intervene" and prevent a failure or collision without totally relinquishing the operator's control. By allowing for a continuous transition back to the operator, the human-robot team can resume task completion with minimal downtime. This is critical in the context of a disaster response and SAR mission where the time to complete the task is essential.
Conventional approaches to shared control are usually done in the context of a known environment, where both the human operator and the autonomous agent are aware of the global map [16]. Obstacles are static and there is normally a single path the operator takes. Our shared control architecture is tested in an environment unknown to both human and autonomous operators with dynamic obstacles, with Figure 2 as an example. Furthermore, we exploit a robot with a defective IMU and weak wireless connection in a construction zone in an attempt to replicate a realistic situation.
In section IV, we validate the shared control architecture by simulating time delay and drift and compare the performance of the operator completing a navigation task with and without shared control. After validation, we observe the performance of the human-robot team when drift & delay are no longer simulated. The operator and autonomous agent are only aware of their immediate surroundings in the form of a local costmap. In doing so, we provide a broader insight into dynamic human-robot team collaboration and how one could enhance the dynamics of such a team for better task completion.
III. PROPOSED SHARED CONTROL ARCHITECTURE In our previous work [17], [18], we explored various schemes to consider and treat the human-robot team as a whole. This remains an open and exciting research area due to the challenges in modeling the uncertainties in human actions and hybrid nature of the system. In the following work, we consider the specific blended shared control approach presented in [17]. We leverage the kinematic model of the system. Instead of formulating the user input in the control law for the system, we formulate the user input as a constraint on the kinematic model. Our formulation can handle a system that is redundant with respect to the task space dimensions, so the redundancy can be taken advantage of to implement a primary and secondary task. We integrate the human input to implement the shared control implementation in one of these tasks depending on the specific application. Our proposed architecture is summarized in Figure 1.

A. Formalism
The primary task is a composition of both human and autonomous inputs described bẏ whereṗ p designates the primary task velocity that the lowlevel robot controller will attempt to follow.ṗ u andṗ a designate the user commanded input velocity and autonomous agent calculated velocity, respectively. The blending parameter α arbitrates the control of the system between user and autonomous input, and in our implementation is chosen as described in [19], shown in (2) where d is the distance to the goal and ∆ is the difference between the operator and autonomous agent's command, θ u and θ a respectively. d 0 and ∆ 0 are the maximum distances and commands, respectively, that the operator can deviate from. Due to the motion of differential drive robots being constrained to the floor plane, the inputs and outputs can be represented byẋ,ẏ,θ corresponding to the 3 degrees of freedom on the plane. A public release of the source code that implements this blended shared control framework can be found at https://github.com/piraka9011/jackal bsc. It can be used on most differential drive robots that use ROS.

IV. EXPERIMENTAL SETUP
In order to validate the blended shared control framework, we conducted two different studies. The first study, with 12 volunteers (ages from 18-24), asked users to teleoperate a Turtlebot around an obstacle and traverse a doorway. The task was considered complete if the Turtlebot passed a specific marker. Each volunteer performed 8 runs of the task: a manual run with no shared controller, a run with the shared controller, and 6 runs with the shared controller and either time delay or drift. The time delay here is the latency between when the user sends a teleoperation command and when the shared controller actually receives the command. Drift is simulated as a displacement in position between the odometry and map frames perceived by the robot, causing a difference in the calculated rotational velocity commanded to the robot. The order of the runs were randomized and the time delay and drift parameters are summarized in Table I. The environment was known to both the autonomous agent and operator. We use the distance the robot travels and the time for the operator to complete the task as performance metrics, shown in I. Figure 3 shows the setup for the doorway traversal experiment with a Turtlebot. The goal of the first study was to quantify the degradation of user performance as the simulated time delay and drift are varied and validate our shared control architecture. The time to complete the navigation task and distance traveled are recorded for each run, and used as metrics for comparing the performance of each run. The robot starts away from the door and needs to travel a short distance before turning left. Once the turn is complete, the robot needs to travel around the blue bucket shown in Figure   Fig. 3: The blended shared control experiment for doorway traversal shown near the obstacle.
3, and then through the door. The run ends when the robot reaches the location marked by a bin directly past the door.
The second study, with 14 volunteers (ages from 18-24), asked users to complete a complex navigation task in two different environments: a construction area with obstacles and a laboratory environment with tight corridors ( Figure  4). The users had to drive the robot from a predefined starting position to a predefined goal position. The path to the goal is not clear and can be achieved via multiple routes, however, a marker similar to that used in the initial study is used to indicate the goal to the operator. 7 tests were completed in the construction area and 7 in the lab environment. Obstacle locations changed across tests and walking people were present in the environments. Volunteers were given a likert-scale questionnaire after the experiment regarding their performance and ability to control the robot. This study did not have simulated time delay and drift. Instead, we used a Clearpath Jackal with a defective IMU and weak wireless connection that caused a delay in user input and visual feedback. A Kinect V2 was attached to the Jackal and was used as the source of visual feedback.
The autonomous agent is implemented as a Robot Operating System (ROS) [20] navigation stack that uses the dynamic window approach for local navigation [21]. The autonomous agent is only given a goal with respect to the map frame and navigates using the robot's internal odometry. The autonomous agent does not have a global map of the environment, generating a navigation plan with respect to it's surrounding obstacles only.
The time to complete the navigation task was recorded for the individuals in all scenarios. The volunteers could see the robot but were asked to use the GUI similar to Figure 2. Due to the noisy odometry, we had no accurate means of recording the distance traveled. Thus, we used only time to completion as a performance metric along with the likert-scale questionnaire. The speeds in both studies were set to a constant 1.0 m/s across all tests and the shared control parameters were chosen to be ∆ 0 = 3.0 m/s and d 0 = 15.0 m. Although we could not control for delay and odometry noise in the second study, the delay had a range of 0.5s-1.5s. This was measured using the difference

V. VALIDATION STUDY RESULTS
Here we present the results of our studies and our observations. For each scenario, we determine if there is a statistical difference in performance across trials by running a Wilcoxon Signed-Rank test. All hypotheses are tested at the 5% significance level (p < 0.05). Figures 6 and 5 summarize the results for the first study. To evaluate the statistical significance of operator performance, we formally define our hypotheses as follows.

A. Hypotheses
Null Hypothesis 1: The times to complete the doorway traversal task under the blended shared control scenario and the time delay scenarios come from the same distribution.
The null hypothesis is rejected for all three time delay values (0.5, 1.0, and 2.0 sec) with respective p-value's of 0.0269, 0.0269, and 4.88e-04. This result indicates that all time delay scenario data does not come from the same distribution as the blended shared control scenario, meaning the result is in fact statistically significant. Users took longer to complete the doorway traversal task with time delay disturbances than in the blended shared control scenario.
Null Hypothesis 2: The distances to complete the doorway traversal task under the blended shared control scenario and the time delay scenarios come from the same distribution.
The null hypothesis is accepted for the 0.5 and 1.0 second delay parameters, and rejected for 2.0 second delay with respective p-values of 0.33, 0.56, 0.0093. This result indicates that the distance metric is not affected as much by the time metric in the time delay scenarios. Not until the delay becomes quite large, does the change in distance traversed become statistically significant.
Null Hypothesis 3: The distances to complete the doorway traversal task under the blended shared control scenario and the drift scenarios come from the same distribution.
The null hypothesis is accepted for all drift scenarios (0.1, 0.3, 0.5 rad/sec) with p-value's of 0.62, 0.17, 0.10 respectively. This result indicates that the data is inconclusive and would require more study to reject the null hypotheses. The constraints posed by the specific path needed to complete the task likely contribute to this result.
Null Hypothesis 4: The times to complete the doorway traversal task under the blended shared control scenario and the drift scenarios come from the same distribution.
It is interesting to note the null hypothesis for time is accepted for the 0.1 rad/sec and rejected for 0.3 and 0.5 rad/sec scenarios with p-values 0.51, 0.034, and 4.88e-04. This result indicates that despite the insignificant change in distance to complete the task, the task did take longer to complete with the drift. Both the obstacle and doorway limited the paths the operators could take, and thus may have limited the variability in the distances.

B. Discussion
The results indicate that users took longer to complete the task with time-delay, as expected. However, it is interesting to note that qualitative testing with the time-delay scenario and no blended shared controller led to a robot that was almost impossible to accurately control through teleoperation. The blended shared controller enables the users to traverse the doorway, albeit at a slower pace, but nevertheless successfully. In the context of a search & rescue task, this latency would be detrimental to both task completion and the integrity of the robot, where multiple collisions could occur. Blending the autonomous agent's input with the operator's makes task completion feasible and the robot's behavior more reliable.

VI. DYNAMIC ENVIRONMENT RESULTS
Following a similar analysis to the validation study, we perform a Wilcoxon signed rank test on the times taken to complete the task. Since we cannot control for delay and drift, we formulate a hypothesis based on the times taken to complete the task manually and with shared control only. Null Hypothesis 5: The times to complete the navigation task under the blended shared control and the manual control scenarios in all environments come from the same distribution.

A. Hypotheses
The null hypothesis is accepted for both construction and lab environment navigation tasks with p-value's of 0.8125 and 0.0938 and respectively. Thus, we can conclude that navigation with shared control did not hamper the performance of the operator under manual control. A boxplot of the results are shown in Figure 7.  A post experiment survey was conducted to understand how operators felt about their performance and whether they were comfortable with the assistance of the shared controller. It also gave us insight as to whether users experienced delay during their runs or not. Results are summarized in Table II and Figure 8. Scores of 10 and 1 indicate that the responder strongly agrees or disagrees, respectively.

C. Discussion
All users successfully completed the task with no collisions. Most users took similar routes that corresponded with the navigation stack's route. This is most likely caused by the resistance user's felt from the autonomous agent when attempting to take a different route. When users did not take the suggested route, the navigation stack would recalculate the path and the autonomous agent would essentially comply with the user's decision. This behavior was interesting to observe when the autonomous agent suggested a route that the operator knew was erroneous. Clearly, there was a human-robot team dynamic occurring that warrants further investigation towards the choice of the shared control parameter.
One method that could limit the resistance between the human operator and autonomous agent is redefining the blending parameter α to consider only the distance to the goal and the euclidean distance to the nearest obstacle. This would mimic the behavior of a potential field based controller, potentially enhancing both the performance of the human-robot team and the feeling of the operator in control.
The survey suggests that although operators felt they were in control of the robot, they were frustrated in attempting to reach the goal. This is most likely caused by our choice of α which relinquishes control to the autonomous agent as the goal is approached. The autonomous agent would resist the operators' attempt to correct their heading as they approached the goal pose, with a limited effect on the behavior of the robot. Relaxing this constraint may allow the operator to more freely reach the desired goal pose, but it would minimize the autonomous agent's intervention should the operator stray away from the goal.
We cannot draw concrete conclusions from the survey regarding whether users felt they were able to perform to the best of their abilities. However, the wide range of responses indicates that some users saw the shared controller as an inhibitor of performance, while others saw it as an aid.
The most important observation is that despite there being an input delay due to the weak wireless connection, the survey suggests that operators experiencing no such delay. The blended shared controller would correct the robot's heading even when the operator's input lagged, enhancing teleoperation of the robot. This is very important in the context of situational awareness as it suggests that operators remained aware of their environment and could navigate the robot with ease. Complementing this with the fact that operators felt they were in control is a step forward in designing architectures for amicable human-robot teams.
VII. FUTURE WORK Future work will focus on improving the blended shared control algorithm through two key areas: the selection of α, either by changing the model or using reinforcement learning, and utilizing higher complexity systems. While the experiments show that our selection of α results in good performance, there may be better ways to define the parameter and its associated constants. Considerations should include how operators like the feel of the change in control authority over the system and the context in which it is used. The blending parameter may for example prioritize multiple goal poses if the task is trajectory based. The shared control architecture can also be extended through reinforcement learning, where an agent could learn the shared control parameter using a reward function based on the context it is used in.
Another area to explore is how the blended shared control architecture changes human-robot team dynamics with different environments and tasks (ex. exploration vs. goal locations). Further research also needs to be done on the feasibility of such an architecture with manipulation tasks in high degree-of-freedom systems, such as humanoid robots. Specifically, whether performance is enhanced when blended shared control is applied in joint space or end-effector space.

VIII. CONCLUSION
We presented an analysis on the performance of a blended human-robot shared control architecture with operator input latency and odometry drift. The architecture is initially validated in a study with simulated latency and drift with human operators. It is then tested in a second study in multiple dynamic environments with a robot that suffers from actual latency and drift. Quantitative results for both studies show improved operator performance in terms of time to task completion with the shared controller as opposed to no shared controller when latency and drift are present. Future investigations into learning the blending parameter for architecture abstraction and potentially enhanced performance of a human-robot team is suggested.


Estimate of traffic energy consumption from vehicle fleet distribution forecast

Transport policies worldwide foresee electric mobility as a way of mitigating greenhouse gas emissions and energy consumption for transportation. Market forecast scenarios predict different shares of electric vehicles. The paper deals with estimation of energy savings for different scenarios. It will be achieved by defining representative vehicles according to the fleet data and market forecasts and by applying the VSP (Vehicle-Specific Power) based model for microsimulation of traffic scenarios. The application of the methodology that takes into account electric and conventional vehicles will yield traffic energy consumption for different scenarios. Case studies will be presented showing impact of different EV shares and traffic volumes. To simulate the traffic conditions and energy consumption, a microsimulation model SUMO will be used.

Introduction
The focus on environmental protection, lowering of tailpipe emissions and increasing energy efficiency are among the most important goals of traffic policies as transportation is contributing to nearly a third of greenhouse gas emissions (GHG) [1]. The situation is similar in the EU, the US and elsewhere in the world [2,3].
The total GHG emissions from transport sector were 32.4% of total GHG emissions in Slovenia in 2016 4. The GHG emissions from road transportation present more than 99% of all emissions from transport in Slovenia 4. Different approaches are being undertaken to minimize traffic emissions, such as modal shift, development of public transport and more energy-efficient power-train technologies. Modal shift is often unsuccessful due to advantages of personal vehicles additionally promoted also by online hailing services in the metropolitan areas due to many possible journey schedules 5. Electric and hybrid electric vehicles are minimizing the impact of increasing traffic on the emissions and are becoming increasingly popular 6. Since major share is of the worldwide electricity production comes from fossil fuels, overall emissions may sometimes be comparable to the internal combustion vehicles 7. Study of CO2 emissions of electrical vehicle operation taking into consideration sources of electrical energy concluded that merely shifting to EVs while the electricity source are mainly fossil fuels such as in the case of China and Poland may not bring any reduction of GHG. The importance of increasing the share of renewable sources in the electric grid was also pointed out in a study analyzing benefits of EVs in China 8. GHG emissions of electric and plug-in hybrid vehicles during exploitation depends not only on source of electricity from the grid, but also on traffic and environmental conditions where they are used 9.
The VSP (Vehicle Specific Power) model was used to estimate instantaneous power for different internal combustion engine powered vehicles 10. It is a model that estimates instantaneous power at the wheels on the basis of physical variables, such as speed, acceleration, air drag and road grade. A modified version of the model that in addition to the power at the wheels considered also auxiliary devices power was used to estimate per-trip energy consumption using a traffic microsimulation model. The vehicle energy consumption estimates based on the VSP were used to evaluate energy impact of road construction and traffic management measures 11. There were other cases of using vehicle power-based models with similar formulation, such as VT-CPFM (Virginia Tech comprehensive Power-based Fuel Consumption Model) which was developed for internal combustion engines and that served as a basis for the VT-CPEM (Virginia Tech comprehensive Power-based Electric Vehicle Energy Consumption Model) [13][14][15]. It is also based on power at the wheels, that depends on the acceleration, road grade, air drag and rolling resistance parameters that depend on vehicle speed. In comparison to the VSP model, it is not massspecific and has slightly different formulation as due to square of speed term for the rolling resistance.
A backward, power-based model was also applied in a study analyzing impact of different vehicle following models on EV energy consumption [16]. It used a power flow model between the battery and the powertrain. A model for estimation of BEV driving range based on driving trajectories could also be seen as a backward model, although it is not power based, but instead introduces driving distance per state of charge parameter that is fitted to the measured data. There are several speed-based formulations presented and some of them are based on polynomial resembling the VSP model [17].
In this paper, use of traffic microsimulation with vehicle energy consumption model will be used to assess impact of increasing share of electric vehicles on traffic energy consumption. Energy consumption for different scenarios (congested urban, fluent urban, extra-urban) will be simulated.

Overview of traffic forecasts and policies
Traffic volume estimates can be obtained by forecasting based on various parameters such as current traffic conditions, planned changes in infrastructure, expected demographic and economic changes. Since there can be great amount of input variables, it is sometimes reasonable to use SVR (Support Vector Regression) models for estimating the traffic volume and double exponential smoothing to project future input values.
Since aim of this paper is not to discuss forecasting in-depth, but to evaluate impact of expected traffic EV shares on the total energy consumption, only an overview of the goals will be summarized.
The proposed "Strategy for the development of market for the establishment of appropriate infrastructure related to alternative fuels in the transport sector in the Republic of Slovenia" (Strategy of mobility) 18 foresees an increase in share of passenger electrical vehicles to 20% of all passenger vehicles by 2030 in the "optimal scenario". The share of electrical passenger vehicles have to be 50% of all new registered vehicles by 2030, with 33% share of batteries electrical vehicles (BEV) and 17% share of plug-in hybrid electric vehicles (PHEV). The strategy sets a goal for at least 200,000 electric vehicles to be introduced in Slovenia by 2030. The Slovenian government also adopted a decree establishing the infrastructure for alternative transport fuels 19. Slovenian Energy Agency published "Guidelines for the development of electro mobility in Slovenia" 20, The Guidelines foresee achievement of 100% of electricity mobility in personal and public transport by 2055. The Guidelines predict reduction of GHG emissions by 35% by 2035 in comparison to 2005. According to these objectives (and other non-traffic related) by 2035, a halving of the energy use of petroleum products is foreseen, and by 2055 their complete abandonment.

Impact of EVs on traffic energy consumption
The impact of EVs on traffic energy consumption can be assessed in different ways. This is mainly due to systems that are included in energy conversion analysis. For example, power required at the wheels is virtually the same for cars of similar weight and air drag regardless of power-train. The EVs are very efficient due to braking energy regeneration. To analyze impact of braking energy regeneration, comparison must be made for energy that flows from the battery to the wheels.
To summarize, different energy consumption requirements can be studied: 1. Energy at the wheels. (ATW) 2. Energy at the wheels reduced by regenerated braking energy (ATWR). 3. Energy battery to wheels or tank to wheels (BTW or TTW). 4. Energy well to wheels (WTW).
In this paper an analysis of the energy consumption from the BTW/TTW analysis will be presented in order to assess impact of the increasing shares of EVs in realistic traffic scenarios on the CO2 emissions.

Model description
The traffic will be modeled using the SUMO (Simulation of Urban Mobility) traffic microsimulation model in order obtain vehicle trajectories (position, speed) that will be used to estimate energy consumption for different vehicle types using a power-based backward EV model. The backward models estimate power requirement from vehicle kinematic parameters and environmental conditions as they are based on instantaneous acceleration, speed, road grade and depend on friction and rolling resistance coefficients. The output of the model is instantaneous energy consumption that is used to estimate either power consumed from the fuel or the battery. The vehicle specific power (VSP) model is defined as where C1, C2 and C3 are constants that depend on acceleration losses, rolling and internal resistance and air drag. The model for simulation of EV energy flow is described and evaluated in the paper 9. The parameters for simulation are based on the assumptions shown in the table 2.

Microsimulation results
In the first run of simulations, an urban traffic case was simulated in fluent (scenario 1) and in congested states (scenario 2) to assess the impact of increased EV share on the overall energy consumption. The simulations were repeated using the shares of electric vehicles between 0 and 80%. For each case overall energy consumption was evaluated. From the Figure 2 it is evident that the overall energy consumption is decreasing linearly with the increasing share of EVs. As it can be seen in the Figures 2-4, much greater energy savings can be obtained when traffic is congested and there is more stop-and-go events. This has also evident in the Figure  1, where distribution of travel times has a lot higher frequency of long travel times in case of congested scenario 2. According to the Slovenia's target for 2030, the 20% share of EVs in the fleet is expected to bring about 12% (congested traffic) to 17% (flowing traffic) total energy consumption reduction for city traffic.

Conclusion and discussion
Use of traffic microsimulation coupled with EV energy flow model is demonstrated for estimation of impact of increasing share of EVs on overall traffic energy consumption. The simulations were carried out only for city traffic under different traffic conditions (fluent and congested). Absolute energy savings were higher for congested traffic, but relative lowers were slightly lower, 12% compared to 17% for fluent traffic. On the other hand, it should be noted that the simulation scenarios covered only specific traffic cases and cannot yet be generalized to a wider set of conditions. Additional simulations that would include freeway traffic, different terrain configurations and weather (temperature) conditions should be analyzed in order to draw more general conclusions.


Containerized Batch System Monitoring

Running a batch system for grid jobs and for local users, we investigate a generic solution to monitor the resource usages of jobs. We extend a standard toolkit for monitoring container performance metrics also for non-containerized applications, so that we can make easy use of a popular industry solution. By concentrating on the basic kernel features also used by containers frameworks, we envisage to use the same tools on non-containerized batch systems as well as container orchestrators. For deployment, we encapsulate the tools as Singularity containers and distribute them via CVMFS.

Introduction
Since microservices in the form of containers have become en vogue, various monitoring tools have been developed. While such tools are primarily focused on Docker as the framework with the largest following, these tools can also be used for a more generic approach. As Containers are based on standard Linux kernel features employed to encapsulate processes in Linux namespaces and cgroups, container monitoring tools can be reused more generically for any kernel resources in cgroups. While containers and container orchestration frameworks are becoming established as Local-Resource-Management-Systems (LRMS) alongside traditional batch systems, batch systems as HTCondor [1,2] or SLURM [3] are prevalent in the HEP world, where workloads of the Grid computing communities have evolved along these. Since nowadays both, batch systems and container frameworks, use the same kernel features for resource management and control, it becomes feasible to refit and establish off-the-shelf monitoring tools for existing LRMSes. We chose Google's cAdvisor [4] as lightweight tool without further dependencies and a rich REST API. We distribute cAdvisor and our Logstash-based aggregation script as Singularity [5] containers via CVMFS [6] for an easy and fast deployment along the lines of established system requirements in the grid world. Aiming for portability, configuration parameters are controlled via environment variables.
While the container for aggregating job statistics is primarily aligned to HTCondor, support for other batch systems, which use cgroups for resource management, could also be added to the aggregation container or handled by a separate aggregation tool. For easier interchangeability of database engines, we stick to JSON formatted data [11] to structure job details. Locally, we use ElasticSearch [14] for information storage and information retrieval, which itself uses Apache Lucene as back-end [13].

Motivation
DESY operates HTCondor 1 clusters with ∼15 000 cores dedicated to grid workflows as well as ∼9 000 cores for batch processing by users in the National Analysis Facility (NAF) [7,8].
Where the grid aims for large scale bulk processing of data and Monte Carlo event simulations, the NAF is targeted on a more interactive work-flow with a fast turnaround of user jobs. Storage back-ends are based on several dCache instances 2 [10] for long-term storage and on a fast scratch space called DUST based on IBM's Spectrum Scale file system [9]. For both use cases, visual job profiling simplifies the analysis and debugging of performance and failures. For the bulk production in most of the Grid workflows, the local site admin can gather a quick overview of failing job tasks from the experiments by mapping the global job IDs in the experiment workload management systems to the local batch systems. E.g., figure 1 shows the job memory profiles of an active node, which were used to identify the jobs that sent their host node into swapping. Equivalently in the NAF use case, individual users could generate an overview of their jobs spread over time as well as over batch system nodes instead of working through all jobs' logfiles to gather an overview of the jobs' performances (provided that some kind of pilot process had been logging the performance alongside the actual job).

Data Aggregation
We use cAdvisor to gather basic system statistics on process trees, which are confined in cgroups, i.e., batch jobs and their child processes for which the HTCondor daemon created separate cgroup sub-slices. cAdvisor itself can export the current system statistics as JSON objects. Per node, we have cAdvisor read the job statistics and add to cAdvisor's JSON object our own additional information, which we gathered from the Condor batch system. The expanded job statistics are then sent to our central Elastic Search 3 [14] cluster and are visualized with Kibana as UI.

cAdvisor: cgroup Statistics
As cAdvisor already brings its own driver for exporting statistics to Elastic Search, in the most basic approach cAdvisor could gather and forward cgroup metrics to the database. But as we have potentially additional interesting knowledge at hand on a node, e.g., the batch system's job details, we intercept the metrics before storing them. Since cAdvisor is primarily aimed at monitoring containerized applications, it is deployed itself as Docker [16] container. Since we do not necessarily use Docker as a container engine and consider the installation and maintenance of Docker on batch nodes an unnecessary potential security risk due to the Docker daemon running under the root context, we decided to deploy cAdvisor as Singularity container 4 due to its more lightweight approach. The conversion of the cAdvisor docker container into a Singularity container is straight forward, as the actual cAdvisor application is a Go binary with hardly any dependencies on the container OS and environment 5 . cAdvisor runs as a Singularity container without problems and we applied only minor tweaks to the container's environment settings, optimizing it for our use case. Since cAdvisor has to have read access to all process information in the /proc and cgroup virtual file system mounts, the container has to be run in the root context 6 with cgroup bind-mounted in the container's namespace 7 . In addition to its JSON export, cAdvisor can also publish the the current system status and cgroup resource usages via an integrated web server as shown in Figure 2. Thus, cAdvisor can also be used for a quick overview on a node's current utilization. In production, we run the cAdvisor container as systemd service started after the network and HTCondor services as requirements. cAdvisor view on a 8-core HT-Condor batch job's current resource usage.

Batch System Parameters
Since cAdvisor is agnostic towards container engines or other cgroup handlers, the raw cAdvisor statistics do not contain any batch system job specifics. Thus, we extend cAdvisor's raw cgroup statistics by batch job details. We wrote a Python script to aggregate cAdvisor and batch system statistics. The script reads HTCondor batch job information details from the file system as well as the jobs process details created at start under /proc/{PID}/. This information is combined with the cAdvisor cgroup details. The script itself is executed by a Logstash service, reads cAdvisors's metrics for the aggregation script, and forwards the resulting enriched job infos to a Elastic Search instance. We wrap Logstash and the aggregation script in another Singularity container. Executed regularly 8 , the container will try to read the current statistics from the running cAdvisor container, process them and forward the extended JSON object. The container can be configured via environment variables, e.g., the address of the ElasticSearch cluster, so that no local configuration files are necessary and the container can be run out-of-thebox after setting the environment variables. 6 When run as a native Docker container, cAdvisor needs to be run as exposed and privileged container as well to have access to all necessary information in the virtual file systems. 7 Mount suggestions for cAdvisor as Docker container can be applied to its avatar as Singularity container. 8 We use a timectl unit to run the container regularly and define dependencies on other systemd units, but any other cron-like scheduler as cron or condor_cron should be working as well.
To take the European Data Protection Rules (GDPR) [17] into account to ensure encrypted transfer of metrics, we modified our first approach adding an intermediate step via Filebeat. Our first approach is sketched in Figure 3. Here, the aggregation container collects information from Condor and cAdvisor forwarding them directly to a Logstash node within our ELK Stack for further processing and storage in Elastic Search. To comply with the European Data Protection rules, we allow for sensitive data only to be transmitted encrypted. As we use mainly Filebeat and other Beat shippers as log and information forwarders, we use our existing PKI infrastructure as certificate basis to establish encrypted channels from our batch nodes to Logstash parsing nodes or Elastic Search database nodes. Since using the existing PKI with Logstash proved to be cumbersome, we took a shortcut. Instead of sending the extended information directly from the aggregation container to ElasticSearch, we write the extended information in a file and attach a Filebeat to the file. Figure 4 sketches both possible ways for forwarding the data, directly or encrypted via the intermediate Filebeat. Figure 3. Combining job information from a cAdvisor container instance with batch system details in an aggregation container and forwarding the extended job information directly to Elastic Search or for further processing to a separate Logstash instance.

Deployment
We deploy the cAdvisor and the aggregation containers via CVMFS. As the containers are self-contained (except requiring a local installation of Singularity), we can avoid building packages for different distributions and can use the already existing distribution infrastructure. Both containers need to be run with root privileges as they need to have access to the process and cgroup information in the virtual file systems as /proc and /sys/fs/cgroup. Since we mount the host file system only as a read-only bind mount, the security implications are assumed to be limited. The cAdvisor container expects the host namespace mounted under /rootfs 9 and can be initiated directly or as Singularity instance 10 . We run the cAdvisor container 9 as cAdvisor needs to read basic process information, virtual file systems as /proc or /sys/fs/cgroup (or other cgroup mounts) need to be readable, we bind / read-only onto "/rootfs". 10 /usr/bin/singularity run -bind /:/rootfs:ro /cvmfs/grid.desy.de/container/cadvisor as a persistent systemd unit after a node's network start-up with the paths in CVMFS as requirement.
For the aggregation container, we bind mount only the necessary directories. To take different cgroup implementations/mount points of the different kernel generations into account, the aggregation container expects the cgroup virtual file system to be mounted on a dedicated mount point. For the operating mode of dumping the job information into a file for Filebeat, a writeable mount shared with the host namespace is necessary. If no output paths are given as parameters, it is assumed to send the job data directly to a end point 11 . A timectl unit regularly runs the aggregation container as a one-time systemd unit with the cAdvisor unit as dependency. Additional information to enable environment variables for changing the container's default behaviour can be obtained via singularity help.
Since we encountered a number of nodes with stalled CVMFS repository mounts when running the containers as services, we additionally deploy an auxiliary unit to prevent the repository mount point to be unmounted 12 .

Outlook
Instead of reading HTCondor batch system details from the jobs' and PIDs' information in the (virtual) file systems, the HTCondor tool condor_who could be a more direct and less error prone way to collect the jobs' information on a node. Furthermore, implementing our own Beat [18] for encrypted transmission instead of using an intermediate Filebeat as stop-gap would be advisable. Our approach could be generalized to other batch systems (apart from HTCondor) by rewriting and sourcing our code into separate classes, which would also make the code maintenance easier. 11 /usr/bin/singularity exec -bind /var/log/condor/jobmon:/var/log -bind /sys/fs/cgroup:/sys/fs/cgroup:ro -bind /sys/fs/cgroup:/cgroup:ro /cvmfs/grid.desy.de/container/condormon.d /usr/local/bin/condor_services_logstash.py -json /var/log/jobs.stats -stderr /var/log/jobs.err 12 /cvmfs/cernvm-prod.cern.ch/extras/cvmfs_block /cvmfs/grid.desy.de