Agent-Based Integrated Decision Making for Autonomous Vehicles in Urban Traffic

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.

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An Integrated Decision-Making Method to Identify Design Requirements Through Agent-based Simulation for Personal Air Vehicle System

AIAA's Aircraft Technology, Integration, and Operations (ATIO) 2002 Technical Forum ◽

10.2514/6.2002-5876 ◽

2002 ◽

Cited By ~ 6

Author(s):

Jung-Ho Lewe ◽

Byung Ahn ◽

Daniel DeLaurentis ◽

Dimitri Mavris ◽

Daniel Schrage

Keyword(s):

Decision Making ◽

Agent Based Simulation ◽

Agent Based ◽

Vehicle System ◽

Air Vehicle ◽

Design Requirements ◽

Integrated Decision Making

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

Sensors ◽

10.3390/s21206768 ◽

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.

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An Agent Based Framework for Adaptive Control and Decision Making of Autonomous Vehicles

IFAC Proceedings Volumes ◽

10.3182/20100826-3-tr-4015.00058 ◽

2010 ◽

Vol 43 (10) ◽

pp. 310-317 ◽

Cited By ~ 2

Author(s):

Louise Dennis ◽

Michael Fisher ◽

Nicholas K. Lincoln ◽

Sandor M. Veres ◽

Alexei Lisitsa

Keyword(s):

Decision Making ◽

Adaptive Control ◽

Autonomous Vehicles ◽

Agent Based

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Research on Integrated Decision Control Algorithm for Autonomous Vehicles Under Multi-Task Hybrid Constraints in Intelligent Transportation Scenarios

Journal of Circuits System and Computers ◽

10.1142/s0218126622501225 ◽

2022 ◽

Author(s):

Zonghuan Guo ◽

Dihua Sun ◽

Lin Zhou

Keyword(s):

Decision Making ◽

Autonomous Vehicles ◽

Control Algorithm ◽

Process Analysis ◽

Autonomous Driving ◽

Test Method ◽

Control Effect ◽

Simulation Test ◽

Intelligent Decision Making ◽

Integrated Decision Making

In order to improve the decision-making and control effect of autonomous vehicles, in this paper, combined with literature research and process analysis, the control algorithm of autopilot vehicle is analyzed, and the driving process is analyzed combined with the flow method. In order to improve the effect of autonomous driving, with the support of improved algorithms, an integrated decision-making control system for autonomous vehicles under multi-task constraints in intelligent traffic scenarios is constructed, and system performance is improved by simulating autonomous driving decisions in a variety of complex situations. Moreover, this paper designs the road driving model according to actual needs, sets the functional modules of the entire system, and build the overall framework of the system. Finally, in order to study the integrated decision-making effect of this system, this paper conducts test research by designing a simulation test method. From the simulation test results, it can be seen that the intelligent decision-making system for autonomous vehicles constructed in this paper has certain effects.

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Research prospect of autonomous driving decision technology under complex traffic scenarios

MATEC Web of Conferences ◽

10.1051/matecconf/202235503031 ◽

2022 ◽

Vol 355 ◽

pp. 03031

Author(s):

Yaoguang Cao ◽

Yuyi Chen ◽

Lu Liu

Keyword(s):

Decision Making ◽

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Autonomous Driving ◽

Urban Traffic ◽

Knowledge Map ◽

Autonomous Decision ◽

Decision Making System ◽

Research Prospect ◽

Safety And Stability

Decision-making system is the essential part of the autonomous vehicle “brain”, which determines the safety and stability of vehicles, and is also the key to reflect the intelligent level of autonomous vehicles. Compared with simple scenarios such as expressway, urban traffic scenarios have the characteristics of complex and frequent interaction between traffic participants. Carrying out in-depth research on complex traffic scenarios and optimizing autonomous decision-making algorithms are the key methods for the purpose of promoting the application of autonomous driving technologies. In the future, we can further combine the artificial intelligence methods such as cognitive or knowledge map, behaviour prediction of traffic participants, and humanoid intelligence, so as to enhance the intelligent level of autonomous driving.

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