# Reachable Sets-based Trajectory Planning Combining Reinforcement Learning and iLQR

arXiv: https://arxiv.org/abs/2503.17398 · HTML: https://mlsanity.com/paper/2503.17398/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 8 of 8)

- [Alternating Direction Method of Multipliers for Constrained Iterative LQR in Autonomous Driving](https://mlsanity.com/paper/2011.00462/) (2020)
- [Decentralized iLQR for Cooperative Trajectory Planning of Connected Autonomous Vehicles via Dual Consensus ADMM](https://mlsanity.com/paper/2301.04386/) (2023)
- [The Convex Feasible Set Algorithm for Real Time Optimization in Motion Planning](https://mlsanity.com/paper/1709.00627/) (2017)
- [Safe Planning for Self-Driving Via Adaptive Constrained ILQR](https://mlsanity.com/paper/2003.02757/) (2020)
- [Real-Time Capable Decision Making for Autonomous Driving Using Reachable Sets](https://mlsanity.com/paper/2309.12289/) (2023)
- [Hybrid iLQR Model Predictive Control for Contact Implicit Stabilization on Legged Robots](https://mlsanity.com/paper/2207.04591/) (2022)
- [Real-Time Spatial Trajectory Planning for Urban Environments Using Dynamic Optimization](https://mlsanity.com/paper/2305.02621/) (2023)
- [EDRF: Enhanced Driving Risk Field Based on Multimodal Trajectory Prediction and Its Applications](https://mlsanity.com/paper/2410.14996/) (2024)

## 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.