A Safe and Efficient Self-evolving Algorithm for Decision-making and Control of Autonomous Driving Systems
On the map: End-to-end autonomous driving → Reinforcement learning for driving
Abstract (arXiv)
Autonomous vehicles with a self-evolving ability are expected to cope with unknown scenarios in the real-world environment. Take advantage of trial and error mechanism, reinforcement learning is able to self evolve by learning the optimal policy, and it is particularly well suitable for solving decision-making problems. However, reinforcement learning suffers from safety issues and low learning efficiency, especially in the continuous action space. Therefore, the motivation of this paper is to address the above problem by proposing a hybrid Mechanism-Experience-Learning augmented approach. Specifically, to realize the efficient self-evolution, the driving tendency by analogy with human driving experience is proposed to reduce the search space of the autonomous driving problem, while the constrained optimization problem based on a mechanistic model is designed to ensure safety during the self-evolving process. Experimental results show that the proposed method is capable of generating safe and reasonable actions in various complex scenarios, improving the performance of the autonomous driving system. Compared to conventional reinforcement learning, the safety and efficiency of the proposed algorithm are greatly improved. The training process is collision-free, and the training time is equivalent to less than 10 minutes in the real world.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 9 of its 38 references that are in the map. First posted on arXiv 22 Aug 2024.
Builds on
9 of 38 references.
Closest ancestors · 2
Same direction, newest first
- 2023Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving31 Oct
- 2022Augmenting Reinforcement Learning with Transformer-based Scene Representation Learning for Decision-making of Autonomous Driving24 Aug
Other references · 7
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
- 2019Data-driven Economic NMPC using Reinforcement Learningcited by 68
- 2021Model-based Chance-Constrained Reinforcement Learning via Separated Proportional-Integral Lagrangiancited by 15
- 2021Interaction-Aware Trajectory Prediction and Planning for Autonomous Vehicles in Forced Merge Scenarioscited by 9
- 2022Interaction-Aware Motion Planning for Autonomous Vehicles with Multi-Modal Obstacle Uncertainty Predictionscited by 7
- 2022Prediction-aware and Reinforcement Learning based Altruistic Cooperative Drivingcited by 6
- 2020An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet Spacecited by 5
Built on by
Citing papers per year: 2026: 2.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2024A Safety-Oriented Self-Learning Algorithm for Autonomous Driving: Evolution Starting from a Basic Model
- 2024A Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Model
- 2020Safe Trajectory Planning Using Reinforcement Learning for Self Driving
- 2022Comparative Study of Q-Learning and NeuroEvolution of Augmenting Topologies for Self Driving Agents
- 2022Integrated Decision and Control for High-Level Automated Vehicles by Mixed Policy Gradient and Its Experiment Verification
- 2019Self Training Autonomous Driving Agent ↗
- 2024Automated Driving with Evolution Capability: A Reinforcement Learning Method with Monotonic Performance Enhancement
- 2024Knowledge Transfer from Simple to Complex: A Safe and Efficient Reinforcement Learning Framework for Autonomous Driving Decision-Making