Humanlike Driving: Empirical Decision-Making System for Autonomous Vehicles

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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A Reinforcement Learning based Decision-making System with Aggressive Driving Behavior Consideration for Autonomous Vehicles

2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) ◽

10.1109/secon52354.2021.9491587 ◽

2021 ◽

Author(s):

Liuwang Kang ◽

Haiying Shen

Keyword(s):

Decision Making ◽

Reinforcement Learning ◽

Autonomous Vehicles ◽

Driving Behavior ◽

Aggressive Driving ◽

Decision Making System

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Rule-Based Decision-Making System for Autonomous Vehicles at Intersections with Mixed Traffic Environment

10.1109/itsc48978.2021.9565085 ◽

2021 ◽

Author(s):

Andrei Aksjonov ◽

Ville Kyrki

Keyword(s):

Decision Making ◽

Autonomous Vehicles ◽

Mixed Traffic ◽

Rule Based ◽

Traffic Environment ◽

Decision Making System

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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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