arXiv cs.AI· Hooman Hamze, Benjamin Houshmand, Deepthi Pathare, Erik B\"orve, Morteza Haghir Chehreghani, Nikolce Murgovski·· 14 小时前AI 评分26
用学习式终端代价实现自动驾驶卡车短时域模型预测控制
Learned Terminal Costs for Short-Horizon Model Predictive Control of Autonomous Trucks in Interactive Highway Traffic
AI 导读
研究提出一种 MPC 与强化学习混合框架，用于重型自动驾驶卡车在交互式高速公路交通中的行驶控制：由动态规划求解的有限时域 MPC 使用预训练 RL 模型估计终端代价。在含反应式周围交通的仿真环境中，该混合方法实现零碰撞，且代价在所有对比配置中最低，优于零终端代价的纯 MPC、基于基础策略的 rollout 终端代价估计以及纯 RL。
正文 · 原文
Abstract:Autonomous highway driving for heavy-duty trucks is a challenging control problem. The greater mass, dimensions and intricate dynamics increase the requirements for cooperation from surrounding traffic in critical maneuvers, requiring a careful balance between safety constraints and efficient long-horizon decision making. Model Predictive Control (MPC) provides rigorous constraint satisfaction but relies on terminal cost which can limit its effectiveness at short, computationally affordable horizons. Reinforcement Learning (RL) captures long-term driving behavior but offers no constraint guarantees and tends toward overly conservative policies when trained under sparse collision penalties. In this paper, we propose a hybrid MPC and RL framework for highway driving in autonomous trucks, where a finite-horizon MPC, solved via dynamic programming, uses a pre-trained RL model for terminal cost estimation. We evaluate the framework in a simulation environment with reactive surrounding traffic and compare it against pure MPC with zero terminal cost, rollout with terminal cost estimation using a base policy, and pure RL. Results show that the hybrid approach achieves zero collisions with the lowest cost of any configuration.
| Comments: | 13 pages, 2 figures, 8 tables |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Robotics (cs.RO); Systems and Control (eess.SY) |
| ACM classes: | I.2.6; I.2.8 |
| Cite as: | arXiv:2610.40257 [cs.LG] |
| (or arXiv:2610.40257v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.40257 arXiv-issued DOI via DataCite |
Submission history
From: Benjamin Houshmand [view email]
[v1]
Thu, 29 Oct 2026 15:23:51 UTC (1,155 KB)
来源：arXiv cs.AI · arxiv.org