Decision-Making Technology for Autonomous Vehicles Learning-Based Methods, Applications and Future Outlook

2021/07/02 by Qi Liu, Liu, Qi, Xueyuan Li +5 · 6 citations
Computer Science · Engineering · Psychology · #Artificial Intelligence (cs.AI) #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #Human-Automation Interaction and Safety #Robotic Path Planning Algorithms #Robotics (cs.RO) #cs.AI #cs.RO

paper · pdf · doi:10.48550/arxiv.2107.01110

8 pages, 1 figure, 5 tables, ITSC2021(accepted)

arxiv created 2021/07/02 · openalex publication_date 2021/07/02 · arxiv updated 2021/07/05 · openalex created_date 2021/07/19 · openalex updated_date 2026/07/28

Abstract

Autonomous vehicles have a great potential in the application of both civil and military fields, and have become the focus of research with the rapid development of science and economy. This article proposes a brief review on learning-based decision-making technology for autonomous vehicles since it is significant for safer and efficient performance of autonomous vehicles. Firstly, the basic outline of decision-making technology is provided. Secondly, related works about learning-based decision-making methods for autonomous vehicles are mainly reviewed with the comparison to classical decision-making methods. In addition, applications of decision-making methods in existing autonomous vehicles are summarized. Finally, promising research topics in the future study of decision-making technology for autonomous vehicles are prospected.

Citations

• End to End Learning for Self-Driving Cars
• A Survey of Autonomous Driving: Common Practices and Emerging Technologies

Cited by

• DTCCL: Disengagement-Triggered Contrastive Continual Learning for Autonomous Bus Planners
• AUPO -- Abstracted Until Proven Otherwise: A Reward Distribution Based Abstraction Algorithm
• Cooperative Autonomous Driving in Diverse Behavioral Traffic: A Heterogeneous Graph Reinforcement Learning Approach
• Security Vulnerabilities in Software Supply Chain for Autonomous Vehicles
• Control Analysis and Design for Autonomous Vehicles Subject to Imperfect AI-Based Perception
• CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning
• Socially-Aware Autonomous Driving: Inferring Yielding Intentions for Safer Interactions

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• Autonomous Vehicles: Evolution of Artificial Intelligence and Learning Algorithms
• Automated vehicle's behavior decision making using deep reinforcement learning and high-fidelity simulation environment
• Transfer Learning and Organic Computing for Autonomous Vehicles
• Decision-making for Autonomous Vehicles on Highway: Deep Reinforcement Learning with Continuous Action Horizon
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