Reachable Sets-based Trajectory Planning Combining Reinforcement Learning and iLQR
On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic
Abstract (arXiv)
The driving risk field is applicable to more complex driving scenarios, providing new approaches for safety decision-making and active vehicle control in intricate environments. However, existing research often overlooks the driving risk field and fails to consider the impact of risk distribution within drivable areas on trajectory planning, which poses challenges for enhancing safety. This paper proposes a trajectory planning method for intelligent vehicles based on the risk reachable set to further improve the safety of trajectory planning. First, we construct the reachable set incorporating the driving risk field to more accurately assess and avoid potential risks in drivable areas. Then, the initial trajectory is generated based on safe reinforcement learning and projected onto the reachable set. Finally, we introduce a trajectory planning method based on a constrained iterative quadratic regulator to optimize the initial solution, ensuring that the planned trajectory achieves optimal comfort, safety, and efficiency. We conduct simulation tests of trajectory planning in high-speed lane-changing scenarios. The results indicate that the proposed method can guarantee trajectory comfort and driving efficiency, with the generated trajectory situated outside high-risk boundaries, thereby ensuring vehicle safety during operation.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 54 references that are in the map. First posted on arXiv 19 Mar 2025.
Builds on
8 of 54 references.
References · 8
- 2020Alternating Direction Method of Multipliers for Constrained Iterative LQR in Autonomous Drivingcited by 11
- 2023Decentralized iLQR for Cooperative Trajectory Planning of Connected Autonomous Vehicles via Dual Consensus ADMMcited by 10
- 2017The Convex Feasible Set Algorithm for Real Time Optimization in Motion Planningcited by 9
- 2020Safe Planning for Self-Driving Via Adaptive Constrained ILQRcited by 8
- 2023Real-Time Capable Decision Making for Autonomous Driving Using Reachable Setscited by 5
- 2022Hybrid iLQR Model Predictive Control for Contact Implicit Stabilization on Legged Robotscited by 2
- 2023Real-Time Spatial Trajectory Planning for Urban Environments Using Dynamic Optimizationcited by 1
- 2024EDRF: Enhanced Driving Risk Field Based on Multimodal Trajectory Prediction and Its Applicationscited by 1
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026Hamilton-Jacobi Reachability-Based Safe Reinforcement Learning for Emergency Collision Avoidance
- 2025Safe and Nonconservative Contingency Planning for Autonomous Vehicles via Online Learning-Based Reachable Set Barriers
- 2025Safe Reinforcement Learning with a Predictive Safety Filter for Motion Planning and Control: A Drifting Vehicle Example
- 2021A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning
- 2025Uncertainty-Aware Safety-Critical Decision and Control for Autonomous Vehicles at Unsignalized Intersections
- 2025Safety-Critical Human-Machine Shared Driving for Vehicle Collision Avoidance based on Hamilton-Jacobi reachability
- 2020Safe Trajectory Planning Using Reinforcement Learning for Self Driving
- 2026A Risk-Sensitive and Uncertainty-Aware Decision-Making and Control Framework for Safe and Robust Autonomous Driving