# Chance-Aware Lane Change with High-Level Model Predictive Control Through Curriculum Reinforcement Learning

arXiv: https://arxiv.org/abs/2303.03723 · HTML: https://mlsanity.com/paper/2303.03723/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2023.

## Built on by (most cited first, 6 of 6)

- [Reward-Driven Automated Curriculum Learning for Interaction-Aware Self-Driving at Unsignalized Intersections](https://mlsanity.com/paper/2403.13674/) (2024)
- [LearningFlow: Automated Policy Learning Workflow for Urban Driving with Large Language Models](https://mlsanity.com/paper/2501.05057/) (2025)
- [Bilevel Multi-Armed Bandit-Based Hierarchical Reinforcement Learning for Interaction-Aware Self-Driving at Unsignalized Intersections](https://mlsanity.com/paper/2502.03960/) (2025)
- [Learning the References of Online Model Predictive Control for Urban Self-Driving](https://mlsanity.com/paper/2308.15808/) (2023)
- [Synergizing Decision Making and Trajectory Planning Using Two-Stage Optimization for Autonomous Vehicles](https://mlsanity.com/paper/2411.18974/) (2024)
- [SocialTraj: Two-Stage Socially-Aware Trajectory Prediction for Autonomous Driving via Conditional Diffusion Model](https://mlsanity.com/paper/2509.17850/) (2025)

## Abstract (arXiv)

Lane change in dense traffic typically requires the recognition of an appropriate opportunity for maneuvers, which remains a challenging problem in self-driving. In this work, we propose a chance-aware lane-change strategy with high-level model predictive control (MPC) through curriculum reinforcement learning (CRL). In our proposed framework, full-state references and regulatory factors concerning the relative importance of each cost term in the embodied MPC are generated by a neural policy. Furthermore, effective curricula are designed and integrated into an episodic reinforcement learning (RL) framework with policy transfer and enhancement, to improve the convergence speed and ensure a high-quality policy. The proposed framework is deployed and evaluated in numerical simulations of dense and dynamic traffic. It is noteworthy that, given a narrow chance, the proposed approach generates high-quality lane-change maneuvers such that the vehicle merges into the traffic flow with a high success rate of 96%. Finally, our framework is validated in the high-fidelity simulator under dense traffic, demonstrating satisfactory practicality and generalizability.