scholarly journals An Investigation of Autonomous Vehicle Roundabout Situation

2019 ◽  
Vol 48 (3) ◽  
pp. 236-241
Author(s):  
Hang Cao ◽  
Máté Zöldy
Keyword(s):  
Fuel Consumption ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Preliminary Test ◽  
Driving Behavior ◽  
Physical Parameters ◽  
Test Scenario ◽  
Vehicle Type ◽  
Fuel Consumption And Emissions ◽  
The Impact

The aim of this paper is to evaluate the impact of connected autonomous behavior in real vehicles on vehicle fuel consumption and emission reductions. Authors provide a preliminary theoretical summary to assess the driving conditions of autonomous vehicles in roundabout, which attempts exploring the impact of driving behavior patterns on fuel consumption and emissions, and including other key factors of autonomous vehicles to reduce fuel consumption and emissions. After summarizing, driving behavior, effective in-vehicle systems, both roundabout physical parameters and vehicle type are all play an important role in energy using. ZalaZONE’s roundabout is selected for preliminary test scenario establishment, which lays a design foundation for further in-depth testing.

Investigation of the Impact on Throughput of Connected Autonomous Vehicles with Headway Based on the Leading Vehicle Type

2019 ◽  
Vol 2673 (5) ◽  
pp. 617-626 ◽  
Author(s):  
Marilo Martin-Gasulla ◽  
Peter Sukennik ◽  
Jochen Lohmiller
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Maximum Throughput ◽  
Vehicle Class ◽  
Car Following ◽  
Control Delay ◽  
Vehicle Type ◽  
The Impact ◽  
Introductory Phase

Although the future era of autonomous driving is seen as a solution for many of the current problems in traffic; the introductory phase, with low penetration rates of connected-autonomous vehicles (CAVs), might lead to lower capacities. This forecast is based on certain assumptions that the CAVs can operate more efficiently when communicating and cooperating—already proved in real tests—therefore in practice, they can keep smaller following headways. However, it is envisioned that they might keep larger headways to other conventional vehicles for safety reasons. Lower connected-autonomous vehicle (CAV) penetration rates lead to a reduction in the overall vehicle throughput, then with increasing penetration rates, throughput is recovered and eventually improved. Simulations demonstrate that the impact on vehicle throughput depends on the car following headway and penetration rate. Based on this potential reduction in the maximum throughput for low penetration rates, the aim of this paper is the mitigation of this phenomenon at urban intersections through a possible managing solution to sort CAVs and a pre-set green-time start. A microsimulation model has been calibrated using PTV Vissim to reflect this operating solution, using new possibilities as leading vehicle class dependent headway settings and formula-based routing for sorting vehicles at a two-lane intersection entry. This approach allows the formation of platoons at intersections and uses their effectiveness even at low CAV penetration rates. The tested scenario is simplified to through traffic without turnings maneuvers and the results show that the potential loss in throughput is canceled and reductions in the control delay can reach 17% for oversaturated conditions.

scholarly journals An Efficiency Enhancing Methodology for Multiple Autonomous Vehicles in an Urban Network Adopting Deep Reinforcement Learning

2021 ◽  
Vol 11 (4) ◽  
pp. 1514 ◽  
Author(s):  
Quang-Duy Tran ◽  
Sang-Hoon Bae
Keyword(s):  
Reinforcement Learning ◽  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Penetration Rate ◽  
Autonomous Vehicle ◽  
Effective Means ◽  
Urban Network ◽  
Learning Agents ◽  
Policy Optimization ◽  
The Impact

To reduce the impact of congestion, it is necessary to improve our overall understanding of the influence of the autonomous vehicle. Recently, deep reinforcement learning has become an effective means of solving complex control tasks. Accordingly, we show an advanced deep reinforcement learning that investigates how the leading autonomous vehicles affect the urban network under a mixed-traffic environment. We also suggest a set of hyperparameters for achieving better performance. Firstly, we feed a set of hyperparameters into our deep reinforcement learning agents. Secondly, we investigate the leading autonomous vehicle experiment in the urban network with different autonomous vehicle penetration rates. Thirdly, the advantage of leading autonomous vehicles is evaluated using entire manual vehicle and leading manual vehicle experiments. Finally, the proximal policy optimization with a clipped objective is compared to the proximal policy optimization with an adaptive Kullback–Leibler penalty to verify the superiority of the proposed hyperparameter. We demonstrate that full automation traffic increased the average speed 1.27 times greater compared with the entire manual vehicle experiment. Our proposed method becomes significantly more effective at a higher autonomous vehicle penetration rate. Furthermore, the leading autonomous vehicles could help to mitigate traffic congestion.

Effects of adaptive and cooperative adaptive cruise control on the fuel consumption and emissions at the signalized intersection

2018 ◽  
Vol 32 (32) ◽  
pp. 1850396 ◽  
Author(s):  
Hongjun Cui ◽  
Jiangke Xing ◽  
Xia Li ◽  
Minqing Zhu
Keyword(s):  
Fuel Consumption ◽  
Red Light ◽  
Signalized Intersection ◽  
Environmental Benefit ◽  
Cruise Control ◽  
Car Following ◽  
Vehicle Type ◽  
Cooperative Adaptive Cruise Control ◽  
Car Following Model ◽  
Fuel Consumption And Emissions

In this paper, the HDM car-following model, the IIDM car-following model and the IDM car-following model with a constant-acceleration heuristic is utilized to explore the effects of ACC/CACC on the fuel consumption and emissionsat the signalized intersection. Two simulation experiments are studied: (i) one with free road ahead and (ii) the second with a red light 300 m downstream at the second intersection. The numerical results show that CACC vehicle is the best vehicle type among the three vehicle types from the perspective of vehicle’s cumulative fuel consumptions and cumulative exhaust emissions. The results of this paper also suggest a very high environmental benefit of ACC/CACC at little or no cost in infrastructure.

scholarly journals The importance of eco-driving training for drivers in transport companies – A case study

2021 ◽  
Vol 67 (1) ◽  
pp. 47-51
Author(s):  
Tatjana Savković ◽  
Pavle Gladović ◽  
Milica Miličić ◽  
Pavle Pitka ◽  
Dejan Koleška
Keyword(s):  
Fuel Consumption ◽  
Management System ◽  
Driving Behavior ◽  
Truck Drivers ◽  
Training Period ◽  
Fleet Management ◽  
Short Term ◽  
The Impact

The paper evaluates the impact of eco-driving programs on driving behavior. The study involved 4 professional truck drivers, which examined two operational driving prameters: fuel consumption and idling. Driving behavior was analyzed through three periods: pre-training period (P1), training period (P2), first month after training (P3) and second month after training (P4). Data were collected using Scania Fleet Management System. The results show that there was an improvement in the observed parameters in short-term. Namely, a decrease in fuel consumption and idling was achieved, in the periods P2, P3 and P4 in relation to the period P1. Due to the realized reductions of the observed parameters, costs in transport companies can be significantly reduced annually.

scholarly journals Assessment of Social Acceptance for Autonomous Vehicles in Southeastern Poland

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5778
Author(s):  
Agnieszka Dudziak ◽  
Monika Stoma ◽  
Andrzej Kuranc ◽  
Jacek Caban
Keyword(s):  
Autonomous Vehicles ◽  
Social Acceptance ◽  
New Technologies ◽  
Consumer Preferences ◽  
Autonomous Vehicle ◽  
Developed Countries ◽  
Automotive Market ◽  
Advantages And Disadvantages ◽  
Autonomous Cars ◽  
The Impact

New technologies reaching out for meeting the needs of an aging population in developed countries have given rise to the development and gradual implementation of the concept of an autonomous vehicle (AV) and have even made it a necessity and an important business paradigm. However, in parallel, there is a discussion about consumer preferences and the willingness to pay for new car technologies and intelligent vehicle options. The main aim of the study was to analyze the impact of selected factors on the perception of the future of autonomous cars by respondents from the area of Southeastern Poland in terms of a comparison with traditional cars, with particular emphasis on the advantages and disadvantages of this concept. The research presented in this study was conducted in 2019 among a group of 579 respondents. Data analysis made it possible to identify potential advantages and disadvantages of the concept of introducing autonomous cars. A positive result of the survey is that 68% of respondents stated that AV will be gradually introduced to our market, which confirms the high acceptance of this technology by Poles. The obtained research results may be valuable information for governmental and local authorities, but also for car manufacturers and their future users. It is an important issue in the area of shaping the strategy of actions concerning further directions of development on the automotive market.

Capturing driving behavior Heterogeneity based on trajectory data

2020 ◽  
Vol 11 (03) ◽  
pp. 2050023
Author(s):  
Dong-Fan Xie ◽  
Tai-Lang Zhu ◽  
Qian Li
Keyword(s):  
Reaction Time ◽  
Fuel Consumption ◽  
Driving Behavior ◽  
Surprising Result ◽  
Trajectory Data ◽  
Dimensional Approach ◽  
Time Headway ◽  
Fuel Consumption And Emissions ◽  
Measure Index ◽  
Real Traffic

Driving behavior is heterogeneous for various drivers due to the different influencing factors as reaction time, gender, driving years and so on. Some existing works tried to reproduce some of the complex characteristics of real traffic flow by taking into account the heterogeneous driving behavior, and the drivers are generally divided into two classes (including aggressive drivers and careful drivers) or three classes (including aggressive drivers, normal drivers and careful drivers). Nevertheless, the classification approaches have not been verified, and the rationality of the classifications has not been confirmed as well. In this study, the trajectory data of drivers is extracted from the NGSIM datasets. By combining the K-Means method and Silhouette measure index, the drivers are classified into four clusters (named as clusters A, B, C and D, respectively) in accordance with the acceleration and time headway. The two-dimensional approach is applied to analyze the characteristics of different clusters. Here, one dimension consists of “Cautious” and “Aggressive” behaviors in terms of velocity and acceleration, and the other dimension consists of “Sensitive” and “Insensitive” behaviors in terms of reaction time. Finally, the fuel consumption and emissions for different clusters are calculated by using the VT-Micro model. A surprising result indicates that overly “cautious” and “sensitive” behaviors may result in more fuel consumption and emissions. Therefore, it is necessary to find the balance between the driving characteristics.

scholarly journals Neuro-Fuzzy System for Energy Management of Conventional Autonomous Vehicles

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1745 ◽  
Author(s):  
Duong Phan ◽  
Alireza Bab-Hadiashar ◽  
Reza Hoseinnezhad ◽  
Reza N. Jazar ◽  
Abhijit Date ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Energy Management ◽  
Autonomous Vehicles ◽  
Pid Controller ◽  
Autonomous Vehicle ◽  
Subtractive Clustering ◽  
Energy Management System ◽  
Power Demand ◽  
Neuro Fuzzy ◽  
Simulation Results

This paper investigates the energy management system (EMS) of a conventional autonomous vehicle, with a view to enhance its powertrain efficiency. The designed EMS includes two neuro-fuzzy (NF) systems to produce the optimal torque of the engine. This control system uses the dynamic road power demand of the autonomous vehicle as an input, and a PID controller to regulate the air mass flow rate into the cylinder by changing the throttle angle. Two NF systems were trained by the Grid Partition (GP) and the Subtractive Clustering (SC) methods. The simulation results show that the proposed EMS can reduce the fuel consumption of the vehicle by 6.69 and 6.35 l/100 km using the SC and the GP, respectively. In addition, the EMS based on NF trained by GP and NF trained by SC can reduce the fuel consumption of the vehicle by 11.8% and 7.08% compared with the case without the controller, respectively.

scholarly journals The Impact of Social Influence, Technophobia, and Perceived Safety on Autonomous Vehicle Technology Adoption

2019 ◽  
Vol 48 (2) ◽  
pp. 133-142
Author(s):  
Sahil Koul ◽  
Ali Eydgahi
Keyword(s):  
Technology Adoption ◽  
Social Influence ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Negative Relationship ◽  
Future Research ◽  
Intention To Use ◽  
Perceived Safety ◽  
Vehicle Technology ◽  
The Impact

The objective of this study was to determine whether there was a relationship between social influence, technophobia, perceived safety of autonomous vehicle technology, number of automobile-related accidents and the intention to use autonomous vehicles. The methodology was a descriptive, cross-sectional, correlational study. Theory of Planned Behavior provided the underlying theoretical framework. An online survey was the primary method of data collection. Pearson’s correlation and multiple linear regression were used for data analysis. This study found that both social influence and perceived safety of autonomous vehicle technology had significant, positive relationships with the intention to use autonomous vehicles. Additionally, a significant negative relationship was found among technophobia and intention to use autonomous vehicles. However, no relationship was found between the number of automobile-related accidents and intention to use autonomous vehicles. This study presents several original and significant findings as a contribution to the literature on autonomous vehicle technology adoption and proposes new dimensions of future research within this emerging field.

scholarly journals The impact of single engine taxiing on aircraft fuel consumption and pollutant emissions

2018 ◽  
Vol 122 (1258) ◽  
pp. 1967-1984 ◽  
Author(s):  
M. E. J. Stettler ◽  
G. S. Koudis ◽  
S. J. Hu ◽  
A. Majumdar ◽  
W. Y. Ochieng
Keyword(s):  
Fuel Consumption ◽  
Pollutant Emissions ◽  
Future Research ◽  
Reduce Fuel Consumption ◽  
Flight Data ◽  
Set Operations ◽  
Aircraft Fuel ◽  
Fuel Consumption And Emissions ◽  
The Impact

ABSTRACTOptimisation of aircraft ground operations to reduce airport emissions can reduce resultant local air quality impacts. Single engine taxiing (SET), where only half of the installed number of engines are used for the majority of the taxi duration, offers the opportunity to reduce fuel consumption, and emissions of NOX, CO and HC. Using 3510 flight data records, this paper develops a model for SET operations and presents a case study of London Heathrow, where we show that SET is regularly implemented during taxi-in. The model predicts fuel consumption and pollutant emissions with greater accuracy than previous studies that used simplistic assumptions. Without SET during taxi-in, fuel consumption and pollutant emissions would increase by up to 50%. Reducing the time before SET is initiated to the 25th percentile of recorded values would reduce fuel consumption and pollutant emissions by 7–14%, respectively, relative to current operations. Future research should investigate the practicalities of reducing the time before SET initialisation so that additional benefits of reduced fuel loadings, which would decrease fuel consumption across the whole flight, can be achieved.

Implementation of an Intralogistics Routing-Service Basing on Decentralized Workspace Digitization

2018 ◽  
Vol 882 ◽  
pp. 90-95 ◽  
Author(s):  
Michael Scholz ◽  
Xu Zhang ◽  
Jörg Franke
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Routing Algorithm ◽  
Automated Guided Vehicles ◽  
Calculation Time ◽  
Vehicle Type ◽  
Moving Obstacles ◽  
Computing Unit ◽  
Vehicle System ◽  
Vehicle Fleet

The paper presents an intralogistics routing-service for autonomous and versatile transport vehicles. An infrastructural sensor digitize the workspace of the vehicle and is the basis for the vehicle-specific routing plan. Nowadays, a central computing unit allocates transportation task to a known number of automated guided vehicles, which are usually of the same type. Furthermore, this device generates a routing appropriate to the dimensions and the kinematic gauge of the vehicle fleet. The pathing for each specific vehicle is calculated and the result is send to the different entities. The approach of this paper bases on the digitization of the workspace with a ceiling camera, which divides the scenery into moving obstacles and an adaptive background picture. A central computing unit receives the background picture of several cameras and stitch them together to an overview of the entire workspace, e.g. a production hall. Furthermore, the approach includes the development of automated guided vehicles to versatile autonomous vehicles, were each entity is able to calculate the pathing on a given routing plan. A fleet of versatile autonomous vehicles consists of vehicles with task-specific dimensions and kinematic gauges. Therefore, each vehicle needs its own routing-plan. The solution is that each vehicles uses a vehicle parameter-server and register itself with these parameters at the routing unit. This unit is calculating a routing-plan for each specific vehicle dimension and gauge and providing it. When getting a new task, the vehicles uses this routing-plan to do the pathing. The routing-algorithm is implemented inside the service-layer of the versatile autonomous vehicle system. This approach lowers the amount of data, which is send between the service layer and the transportation entities by reducing the information of the workspace to the possible routes of each specific vehicle. Furthermore, the calculation time for routing and pathing is lowered, because each vehicle is calculating its task-specific path, but the route-map is calculated once for each vehicle-type by the routing-service.

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