Automated Driving with Evolution Capability: A Reinforcement Learning Method with Monotonic Performance Enhancement
Reinforcement Learning (RL) offers a promising solution to enable evolutionary automated driving. However, the conventional RL method is always concerned with risk performance. The updated policy may not obtain a performance enhancement, even leading to performance deterioration. To address this challenge, this research proposes a High Confidence Policy Improvement Reinforcement Learning-based (HCPI-RL) planner. It is intended to achieve the monotonic evolution of automated driving. A novel RL policy update paradigm is designed to enable the newly learned policy performance consistently surpass that of previous policies, which is deemed as monotonic performance enhancement. Hence, the proposed HCPI-RL planner has the following features: i) Evolutionary automated driving with monotonic performance enhancement; ii) With the capability of handling scenarios with emergency; iii) With enhanced decision-making optimality. Results demonstrate that the proposed HCPI-RL planner enhances the policy return by 44.7% in emergent cut-in scenarios, 108.2% in emergent braking scenarios, and 64.4% in daily cruising scenarios, compared to the PPO planner. Adopting the proposed planner, automated driving efficiency is enhanced by 19.2% compared to the PPO planner, and by 30.7% compared to the rule-based planner.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision Makingreinforcement-learningReinforcement LearningReinforcement Learning (RL)Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Quick Learner Automated Vehicle Adapting its Roadmanship to Varying Traffic Cultures with Meta Reinforcement Learning
It is essential for an automated vehicle in the field to perform discretionary lane changes with appropriate roadmanship - driving safely and efficiently without annoying or endangering other road users - under a wide ra…
Deep Reinforcement LearningMeta Reinforcement Learningreinforcement-learningReinforcement Learning+1Accelerating the Evolution of Personalized Automated Lane Change through Lesson Learning
Personalization is crucial for the widespread adoption of advanced driver assistance system. To match up with each user's preference, the online evolution capability is a must. However, conventional evolution methods lea…
Computational EfficiencyModel Predictive ControlTrajectory PlanningReinforced Imitative Trajectory Planning for Urban Automated Driving
Reinforcement learning (RL) faces challenges in trajectory planning for urban automated driving due to the poor convergence of RL and the difficulty in designing reward functions. The convergence problem is alleviated by…
Imitation Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1UMBRELLA: Uncertainty-Aware Model-Based Offline Reinforcement Learning Leveraging Planning
Offline reinforcement learning (RL) provides a framework for learning decision-making from offline data and therefore constitutes a promising approach for real-world applications as automated driving. Self-driving vehicl…
Decision MakingOffline RLreinforcement-learningReinforcement Learning+1Punctuated Equilibria in Artificial Intelligence: The Institutional Scaling Law and the Speciation of Sovereign AI
The dominant narrative of artificial intelligence development assumes that progress is continuous and that capability scales monotonically with model size. We challenge both assumptions. Drawing on punctuated equilibrium…