scholarly journals Autonomous Vehicles - Stress Test for the Urban Traffic Infrastructure?

ATZ worldwide ◽  
2021 ◽  
Vol 124 (1) ◽  
pp. 76-76
Author(s):  
Christian U. Haas
Keyword(s):  
Autonomous Vehicles ◽  
Stress Test ◽  
Urban Traffic

scholarly journals A Game Theory-Based Approach for Modeling Autonomous Vehicle Behavior in Congested, Urban Lane-Changing Scenarios

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1523
Author(s):  
Nikita Smirnov ◽  
Yuzhou Liu ◽  
Aso Validi ◽  
Walter Morales-Alvarez ◽  
Cristina Olaverri-Monreal
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Urban Traffic ◽  
Road User ◽  
Lane Change ◽  
Traffic Light ◽  
User Interactions ◽  
Lane Changing

Autonomous vehicles are expected to display human-like behavior, at least to the extent that their decisions can be intuitively understood by other road users. If this is not the case, the coexistence of manual and autonomous vehicles in a mixed environment might affect road user interactions negatively and might jeopardize road safety. To this end, it is highly important to design algorithms that are capable of analyzing human decision-making processes and of reproducing them. In this context, lane-change maneuvers have been studied extensively. However, not all potential scenarios have been considered, since most works have focused on highway rather than urban scenarios. We contribute to the field of research by investigating a particular urban traffic scenario in which an autonomous vehicle needs to determine the level of cooperation of the vehicles in the adjacent lane in order to proceed with a lane change. To this end, we present a game theory-based decision-making model for lane changing in congested urban intersections. The model takes as input driving-related parameters related to vehicles in the intersection before they come to a complete stop. We validated the model by relying on the Co-AutoSim simulator. We compared the prediction model outcomes with actual participant decisions, i.e., whether they allowed the autonomous vehicle to drive in front of them. The results are promising, with the prediction accuracy being 100% in all of the cases in which the participants allowed the lane change and 83.3% in the other cases. The false predictions were due to delays in resuming driving after the traffic light turned green.

Pedestrians, Autonomous Vehicles, and Cities

2016 ◽  
Vol 38 (1) ◽  
pp. 6-12 ◽  
Author(s):  
Adam Millard-Ball
Keyword(s):  
Travel Behavior ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Urban Neighborhoods ◽  
Urban Traffic ◽  
Risk Averse ◽  
Strategic Interactions ◽  
Road Users ◽  
Self Driving Cars ◽  
Time Autonomous

Autonomous vehicles, popularly known as self-driving cars, have the potential to transform travel behavior. However, existing analyses have ignored strategic interactions with other road users. In this article, I use game theory to analyze the interactions between pedestrians and autonomous vehicles, with a focus on yielding at crosswalks. Because autonomous vehicles will be risk-averse, the model suggests that pedestrians will be able to behave with impunity, and autonomous vehicles may facilitate a shift toward pedestrian-oriented urban neighborhoods. At the same time, autonomous vehicle adoption may be hampered by their strategic disadvantage that slows them down in urban traffic.

scholarly journals Influence of External Environmental Factors on Range Estimation of Autonomous Hybrid Vehicles

2019 ◽  
Vol 1 (1) ◽  
pp. 472-480 ◽  
Author(s):  
Máté Zöldy ◽  
Imre Zsombók
Keyword(s):  
Fuel Consumption ◽  
Autonomous Vehicles ◽  
Combustion Engine ◽  
Hybrid Vehicles ◽  
Urban Traffic ◽  
Environmentally Conscious ◽  
Electric Cars ◽  
Electric Driving ◽  
Starting Point ◽  
The Matrix

AbstractDuring our research, we focus on a less researched area in the development of autonomous vehicles. Automotive industry is turning more and more from conventional, internal combustion engine equipped vehicles to the electric cars. Today, electric driving is mostly limited to urban traffic, this is the area where range and refueling limits can be a real alternative. However, it is important to think of those who intend to use vehicle in longer distances, and hybrid technology can provide them a modern, environmentally conscious way of transport.In this article, we describe the method of creating the fuel consumption influencing factors matrix, which is the starting point of our research. We studied relevant researches and based on refueling studies we created the matrix. Based on results of real tests, we determined the factor mix that are the basis of our fuel consumption prediction model. These results will be inputs of planning routes of autonomous vehicles with optimized refueling and fuel consumption.

scholarly journals Potential of Private Autonomous Vehicles for Parcel Delivery

2020 ◽  
Vol 2674 (11) ◽  
pp. 520-531
Author(s):  
Tilmann Schlenther ◽  
Kai Martins-Turner ◽  
Joschka Felix Bischoff ◽  
Kai Nagel
Keyword(s):  
Service Provider ◽  
Autonomous Vehicles ◽  
Urban Traffic ◽  
Vehicle Miles Traveled ◽  
Traffic System ◽  
Autonomous Cars ◽  
Multi Agent ◽  
Wide Scale ◽  
Operator Behavior

Using the same vehicles for both passenger and freight transport, to increase vehicle occupancy and decrease their number, is an idea that drives transport planners and is also being addressed by manufacturers. This paper proposes a methodology to simulate the behavior of such vehicles within an urban traffic system and evaluate their performance. The aim is to investigate the impacts of resignation from fleet ownership by a transport service company (TSC) operating on a city-wide scale. In the simulation, the service provider hires private autonomous cars for tour performance. Based on assumptions concerning the operation of such vehicles and TSCs, the software Multi-Agent Transport Simulation (MATSim) is extended to model vehicle and operator behavior. The proposed framework is applied to a case study of a parcel delivery service in Berlin serving a synthetic parcel demand. Results suggest that the vehicle miles traveled for freight purposes increase because of additional access and egress trips. Moreover, the number of vehicles en route is higher throughout the day. The lowering of driver costs can reduce the costs of the operator by approximately 74.5%. If the service provider additionally considers the resignation from fleet ownership, it might lower the operation cost by another 10%, not taking into account the costs of system transfer or risks like vehicle non-availability. From an economic perspective, the reduction of the overall number of vehicles in the system seems to be beneficial.

scholarly journals TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents

2019 ◽  
Vol 33 ◽  
pp. 6120-6127 ◽  
Author(s):  
Yuexin Ma ◽  
Xinge Zhu ◽  
Sibo Zhang ◽  
Ruigang Yang ◽  
Wenping Wang ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Short Term Memory ◽  
Autonomous Vehicle ◽  
Large City ◽  
Urban Traffic ◽  
Heterogeneous Traffic ◽  
Prediction Algorithm ◽  
Trajectory Prediction ◽  
Long Short Term Memory ◽  
Traffic Agents

To safely and efficiently navigate in complex urban traffic, autonomous vehicles must make responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles, pedestrians, etc.). A challenging and critical task is to explore the movement patterns of different traffic-agents and predict their future trajectories accurately to help the autonomous vehicle make reasonable navigation decision. To solve this problem, we propose a long short-term memory-based (LSTM-based) realtime traffic prediction algorithm, TrafficPredict. Our approach uses an instance layer to learn instances’ movements and interactions and has a category layer to learn the similarities of instances belonging to the same type to refine the prediction. In order to evaluate its performance, we collected trajectory datasets in a large city consisting of varying conditions and traffic densities. The dataset includes many challenging scenarios where vehicles, bicycles, and pedestrians move among one another. We evaluate the performance of TrafficPredict on our new dataset and highlight its higher accuracy for trajectory prediction by comparing with prior prediction methods.

Impact of self-parking autonomous vehicles on urban traffic congestion

2021 ◽  
Author(s):  
Sajjad Shafiei ◽  
Ziyuan Gu ◽  
Hanna Grzybowska ◽  
Chen Cai
Keyword(s):  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Urban Traffic

scholarly journals Human-like Decision-Making System for Overtaking Stationary Vehicles Based on Traffic Scene Interpretation

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6768
Author(s):  
Jinsoo Yang ◽  
Seongjin Lee ◽  
Wontaek Lim ◽  
Myoungho Sunwoo
Keyword(s):  
Decision Making ◽  
Autonomous Vehicles ◽  
Urban Traffic ◽  
Learning Performance ◽  
Vehicle Classification ◽  
Scene Interpretation ◽  
Decision Factors ◽  
Human Driver ◽  
Decision Making System ◽  
Interpretation Process

There are multifarious stationary vehicles in urban driving environments. Autonomous vehicles need to make appropriate overtaking maneuver decisions to navigate through the stationary vehicles. In literature, overtaking maneuver decision problems have been addressed in the perspective of either discretionary lane-change or parked vehicle classification. While the former approaches are prone to generating undesired overtaking maneuvers in urban traffic scenarios, the latter approaches induce deadlock situations behind a stationary vehicle which is not distinctly classified as a parked vehicle. To overcome the limitations, we analyzed the significant decision factors in the traffic scenes and designed a Deep Neural Network (DNN) model to make human-like overtaking maneuver decisions. The significant traffic-related and intention-related decision factors were harmoniously extracted in the traffic scene interpretation process and were utilized as the inputs of the model to generate overtaking maneuver decisions in the same manner with the human driver. The overall validation results convinced that the extracted decision factors contributed to increasing the learning performance of the model, and consequently, the proposed decision-making system enabled the autonomous vehicles to generate more human-like overtaking maneuver decisions in various urban traffic scenarios.

scholarly journals Automated Conflict Management Framework Development for Autonomous Aerial and Ground Vehicles

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8344
Author(s):  
David Sziroczák ◽  
Daniel Rohács
Keyword(s):  
Conflict Management ◽  
Autonomous Vehicles ◽  
Unmanned Vehicles ◽  
Urban Traffic ◽  
Ground Vehicles ◽  
Management Framework ◽  
Incremental Development ◽  
Stop And Go ◽  
High Level ◽  
Framework Development

The number of aerial- and ground-based unmanned vehicles and operations is expected to significantly expand in the near future. While aviation traditionally has an excellent safety record in managing conflicts, the current approaches will not be able to provide safe and efficient operations in the future. This paper presents the development of a novel framework integrating autonomous aerial and ground vehicles to facilitate short- and mid-term tactical conflict management. The methodology presents the development of a modular web service framework to develop new conflict management algorithms. This new framework is aimed at managing urban and peri-urban traffic of unmanned ground vehicles and assisting the introduction of urban air mobility into the same framework. A set of high-level system requirements is defined. The incremental development of two versions of the system prototype is presented. The discussions highlight the lessons learnt while implementing and testing the conflict management system and the introduced version of the stop-and-go resolution algorithm and defines the identified future development directions. Operation of the system was successfully demonstrated using real hardware. The developed framework implements short- and mid-term conflict management methodologies in a safe, resource efficient and scalable manner and can be used for the further development and the evaluation of various methods integrating aerial- and ground-based autonomous vehicles.

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