Vehicle Decision Making

Vehicle decision-making in autonomous driving focuses on developing algorithms that enable vehicles to safely and efficiently navigate complex traffic scenarios. Current research heavily utilizes deep reinforcement learning, often incorporating attention mechanisms and hierarchical architectures like Proximal Policy Optimization (PPO) and Deep Q-Networks (DQN), to learn optimal driving strategies from data and simulated environments. This field is crucial for improving the safety and reliability of autonomous vehicles, impacting both the development of robust control systems and the creation of effective safety assessment frameworks for regulatory approval. Furthermore, understanding how humans perceive and explain AV behavior is increasingly important for designing trustworthy and acceptable systems.

Papers

September 3, 2024

Situation-aware Autonomous Driving Decision Making with Cooperative Perception on Demand
Wei Liu
Autonomous Driving Decision Making Long Range Observable Markov Decision Process Cooperative Perception Video Game Demand Vehicle Decision Making

August 6, 2024

Research on Autonomous Driving Decision-making Strategies based Deep Reinforcement Learning
Zixiang Wang, Hao Yan, Changsong Wei, Junyu Wang, Shi Bo, Minheng Xiao
Deep Reinforcement Learning Autonomous Driving DH Research Deep Q Network Reinforcement Learning Problem Vehicle Decision Making

July 12, 2024

Deep Attention Driven Reinforcement Learning (DAD-RL) for Autonomous Vehicle Decision-Making in Dynamic Environment
Jayabrata Chowdhury, Venkataramanan Shivaraman, Sumit Dangi, Suresh Sundaram, P. B. Sujit
Reinforcement Learning Dynamic Environment Temporal Attention Spatiotemporal Representation Spatiotemporal Attention Vehicle Decision Making

March 18, 2024

Demystifying the Physics of Deep Reinforcement Learning-Based Autonomous Vehicle Decision-Making
Hanxi Wan, Pei Li, Arpan Kusari
Reinforcement Learning Deep Reinforcement Learning Autonomous Vehicle Theoretical Physic Deep Reinforcement Vehicle Decision Making

March 11, 2024

People Attribute Purpose to Autonomous Vehicles When Explaining Their Behavior
Balint Gyevnar, Stephanie Droop, Tadeg Quillien, Shay B. Cohen, Neil R. Bramley, Christopher G. Lucas, Stefano V. Albrecht
Autonomous Vehicle Explainable AI Person Name BEHAVIOR Explanation Cognitive Science Human Explanation Vehicle Decision Making

March 5, 2024

Autonomous vehicle decision and control through reinforcement learning with traffic flow randomization
Yuan Lin, Antai Xie, Xiao Liu
Reinforcement Learning External Control Domain Randomization Vehicle Decision Making Microscopic Traffic

December 27, 2023

Risk-anticipatory autonomous driving strategies considering vehicles' weights, based on hierarchical deep reinforcement learning
Di Chen, Hao Li, Zhicheng Jin, Huizhao Tu, Meixin Zhu
Autonomous Driving Autonomous Vehicle Vehicle Plan High Quality Risk Description Balancing Weight REinforcement Learning Vehicle Decision Making Autonomous Driving Strategy

October 31, 2023

Decision-Making for Autonomous Vehicles with Interaction-Aware Behavioral Prediction and Social-Attention Neural Network
Xiao Li, Kaiwen Liu, H. Eric Tseng, Anouck Girard, Ilya Kolmanovsky
Autonomous Vehicle Traffic Datasets Interaction Prediction Vehicle Decision Making

October 2, 2023

Wellbeing in Future Mobility: Toward AV Policy Design to Increase Wellbeing through Interactions
Shashank Mehrotra, Zahra Zahedi, Teruhisa Misu, Kumar Akash
Automated Driving Interaction Strategy Mobility Management Human Wellbeing Interaction Aware Future Mobility Social Perception Vehicle Decision Making

August 9, 2023

Automated Driving Without Ethics: Meaning, Design and Real-World Implementation
Katherine Evans, Nelson de Moura, Raja Chatila, Stéphane Chauvier
Product Design Automated Driving Multiple Meaning Intercultural Ethic Moral Theory Moral Agent Vehicle Decision Making Real World Implementation

December 17, 2021

Scenario-Based Safety Assessment Framework for Automated Vehicles
J. Ploeg, E. de Gelder, M. Slavík, E. Querner, T. Webster, N. de Boer
Automated Driving Traffic Scenario Public Transit Functional Safety Critical Scenario Generation Vehicle Decision Making

Vehicle Decision Making

Papers

Situation-aware Autonomous Driving Decision Making with Cooperative Perception on Demand

Research on Autonomous Driving Decision-making Strategies based Deep Reinforcement Learning

Deep Attention Driven Reinforcement Learning (DAD-RL) for Autonomous Vehicle Decision-Making in Dynamic Environment

Demystifying the Physics of Deep Reinforcement Learning-Based Autonomous Vehicle Decision-Making

People Attribute Purpose to Autonomous Vehicles When Explaining Their Behavior

Autonomous vehicle decision and control through reinforcement learning with traffic flow randomization

Risk-anticipatory autonomous driving strategies considering vehicles' weights, based on hierarchical deep reinforcement learning

Decision-Making for Autonomous Vehicles with Interaction-Aware Behavioral Prediction and Social-Attention Neural Network

Wellbeing in Future Mobility: Toward AV Policy Design to Increase Wellbeing through Interactions

Automated Driving Without Ethics: Meaning, Design and Real-World Implementation

Scenario-Based Safety Assessment Framework for Automated Vehicles