The paper introduces a hybrid framework combining Model Predictive Control (MPC) and Reinforcement Learning (RL) for autonomous highway driving of heavy‑duty trucks. A finite‑horizon MPC, solved via dynamic programming, uses a pre‑trained RL model to estimate terminal costs, addressing the limitations of short‑horizon MPC and the lack of constraint guarantees in pure RL. In simulation with reactive traffic, the hybrid approach achieves zero collisions while incurring the lowest cost among compared methods.
arXiv:2601. 18783v2 Announce Type: replace-cross Abstract: Balancing safety, efficiency, and operational costs in highway driving poses a challenging decision-making problem for heavy-duty vehicles.
By Deepthi Pathare, Leo Laine, Morteza Haghir Chehreghani
The paper introduces Composite‑Gradient Learning (CGL), a method that explicitly incorporates a model predictive controller (MPC) into the training of a deep reinforcement learning (DRL) agent by treating their control inputs as a joint action. CGL updates the DRL policy while accounting for the interaction with the MPC, unlike prior approaches that view MPC merely as part of the environment. Experiments on two freeway traffic networks show that CGL performs better than alternative methods when the interaction between DRL and MPC is strong, though overall gains are modest.
By Giray \"On\"ur, Azita Dabiri, Bart De Schutter
The paper introduces a preference-conditioned multi-objective reinforcement learning framework for tactical decision making in heavy-duty vehicles. It explicitly models safety, energy efficiency, and time efficiency as separate objectives and uses Generalized Policy Improvement and Proximal Policy Optimization to learn policies that approximate the Pareto front across these conflicting goals. Experiments in a scalable highway traffic simulation show smooth, interpretable Pareto fronts and meaningful trade-offs, with the policy enabling behavior transitions without retraining.
By Deepthi Pathare, Leo Laine, Morteza Haghir Chehreghani
arXiv:2610.19429v1 Announce Type: new
Abstract: This paper presents a comprehensive comparison of four representative reinforcement learning (RL) methods in autonomous vehicle (AV) decision-making, i...
By Dechen Xie, Mushuang Liu
arXiv:2608.20858v1 Announce Type: cross
Abstract: Transportation networks, in particular multi-class transportation networks (i.e., networks with mixed vehicle types), are complex systems that are ch...
By Giray Onur, Azita Dabiri, Bart De Schutter