Stable Inverse Reinforcement Learning via Leveraged Guided Motion Planner for Driving Behavior Prediction
Year: 2025
Type: article
Abstract: Driving behavior prediction has become increasingly important, owing to rapid advancements in autonomous vehicle technologies. Inverse reinforcement learning (IRL) has emerged as a leading approach domain, as it allows the inference of underlying reward functions from human-driving demonstrations, enabling the modeling of complex behaviors. Among various IRL approaches, maximum-entropy IRL (MaxEnt IRL) has gained prominence in driving-behavior modeling owing to its applicability to continuous state-space problems. In continuous state-space MaxEnt IRL, stochastic motion planners are commonly employed during training to approximate the partition function because integrating over the entire state-space is computationally infeasible. However, traditional motion planners often fail to efficiently explore the state-space surrounding human-demonstrated paths, leading to inaccurate approximations of the partition function. This study proposes a novel, stable MaxEnt IRL framework that integrates an IRL-aware motion planner incorporating a guided exploration and exploitation process to efficiently sample high-quality trajectories. The proposed approach leverages two distributions derived from human-demonstrated paths to balance broad state-space exploration and targeted exploitation of relevant regions. Experiments conducted in a driving simulator demonstrate that the proposed method outperforms existing IRL methods in stability and accuracy, enhancing the prediction of driving behaviors and showcasing the potential of IRL for achieving human-like decision-making in autonomous driving.
Source: IEEE Access
Authors: Minglu Zhao, Masamichi Shimosaka
Institution: Institute of Science Tokyo
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W4410427571