Decision-Making in Fallback Scenarios for Autonomous Vehicles: Deep Reinforcement Learning Approach
-
Published:2023-11-13
Issue:22
Volume:13
Page:12258
-
ISSN:2076-3417
-
Container-title:Applied Sciences
-
language:en
-
Short-container-title:Applied Sciences
Author:
Lee Cheonghwa1ORCID,
An Dawn1ORCID
Affiliation:
1. Advanced Mechatronics R&D Group, Daegyeong Division, Korea Institute of Industrial Technology, Daegu 42994, Republic of Korea
Abstract
This paper proposes a decision-making algorithm based on deep reinforcement learning to support fallback techniques in autonomous vehicles. The fallback technique attempts to mitigate or escape risky driving conditions by responding to appropriate avoidance maneuvers essential for achieving a Level 4+ autonomous driving system. However, developing a fallback technique is difficult because of the innumerable fallback situations to address and eligible optimal decision-making among multiple maneuvers. We employed a decision-making algorithm utilizing a scenario-based learning approach to address these issues. First, we crafted a specific fallback scenario encompassing the challenges to be addressed and matched the anticipated optimal maneuvers as determined by heuristic methods. In this scenario, the ego vehicle learns through trial and error to determine the most effective maneuver. We conducted 100 independent training sessions to evaluate the proposed algorithm and compared the results with those of heuristic-derived maneuvers. The results were promising; 38% of the training sessions resulted in the vehicle learning lane-change maneuvers, whereas 9% mastered slow following. Thus, the proposed algorithm successfully learned human-equivalent fallback capabilities from scratch within the provided scenario.
Funder
Korea Institute of Industrial Technology
Technology Innovation Program
Ministry of Trade, Industry & Energy
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference29 articles.
1. (2023, November 09). SAE-J3016; Taxonomy and Definitions for terms Related to Driving Automation Systems for On Road Motor Vehicles. Available online: https://www.sae.org/standards/content/j3016_202104/.
2. (2023, November 09). ISO/PAS 21448; Road Vehicles: Safety of Intended Functionality (SOTIF). Available online: https://www.iso.org/standard/77490.html.
3. Yu, J., and Luo, F. (2019, January 27–30). Fallback strategy for level 4+ automated driving system. Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand.
4. Shalev-Shwartz, S., Shammah, S., and Shashua, A. (2017). On a formal model of safe and scalable self-driving car. arXiv.
5. Survey on scenario-based safety assessment of automated vehicles;Riedmaier;IEEE Access,2020