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5 July 2024 Reinforcement learning obstacle avoidance algorithm for autonomous driving considering driving risks
Qian Du, Yong Wang, YanQiang Li, DaiFeng Zhang, Wei Zhang, XiaoBin Qi
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Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131843I (2024) https://doi.org/10.1117/12.3033065
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia
Abstract
Driving safety has always been a core concern in the field of Autonomous driving vehicles, and the safety issue of interaction between the ego-vehicle and the surrounding vehicles is particularly important in lane-changing scenarios. This paper proposes a deep deterministic policy gradient algorithm (DDPG) based on driving risk for obstacle avoidance decision-making model (RISK-DDPG), which aims to learn risk-aware driving decision-making strategies at a minimized cost. The DDPG algorithm is used to learn the minimally risky driving strategy by using Gaussian risk field to assess the magnitude of the risk of obstacles and the value of the driving risk quantum as a reward function. The performance of the proposed algorithm in this paper is evaluated against traditional obstacle avoidance algorithms by evaluating the performance in the CARLA simulation environment. The results show that the proposed model achieves safer obstacle avoidance decisions while speeding up the model convergence.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Qian Du, Yong Wang, YanQiang Li, DaiFeng Zhang, Wei Zhang, and XiaoBin Qi "Reinforcement learning obstacle avoidance algorithm for autonomous driving considering driving risks", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131843I (5 July 2024); https://doi.org/10.1117/12.3033065
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KEYWORDS
Autonomous driving
Decision making
Safety
Detection and tracking algorithms
Education and training
Risk assessment
Computer simulations
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