Researchers have proposed a personalized longitudinal motion planning policy for intelligent vehicles that combines reinforcement learning with imitation learning. The approach is designed to reduce the gap between human driving behavior and automated vehicle decision-making by allowing a vehicle to adapt its longitudinal driving style to a target driver while still meeting performance requirements.
As autonomous driving technologies continue to develop, intelligent vehicles are expected not only to drive safely and efficiently, but also to behave in ways that human users can understand and accept. Longitudinal motion planning, which controls behaviors such as acceleration, deceleration, following distance, and speed adjustment, is central to this goal. A technically safe driving policy may still feel uncomfortable or unnatural if it differs too sharply from the preferences of the human driver or passengers.
The new study addresses this issue by focusing on anthropomorphism and personalization in intelligent vehicle planning. According to the article, longitudinal motion planning needs to consider multiple performance metrics as well as driver acceptance of the vehicle?s driving style. This is a difficult balance: a planning policy must respond to traffic conditions and safety constraints while also reflecting individual differences in how people drive.
The researchers build the primary framework on reinforcement learning, which allows an intelligent agent to learn a policy through interaction with an environment. In this case, reinforcement learning provides the foundation for longitudinal motion planning. The study then incorporates a classic trajectory prediction method to construct an environment with prediction and deduction model, or EPD, so that future interaction information between vehicles can be considered in the planning process.
This future-interaction component is important because longitudinal driving decisions are inherently dynamic. An intelligent vehicle does not merely react to the current position and speed of surrounding vehicles; it must also anticipate how traffic participants may evolve in the near future. By including prediction and deduction in the learning environment, the policy can account for interactions that may affect following, braking, or acceleration decisions.
The study also uses Generative Adversarial Imitation Learning, or GAIL, to assimilate human driver demonstration data into the reinforcement learning framework. Imitation learning helps the system learn from human driving examples, rather than relying only on handcrafted rewards or purely exploratory behavior. In practical terms, this gives the policy a way to capture individual driving style and make intelligent vehicle behavior more human-like.
The Deep Deterministic Policy Gradient, or DDPG, algorithm is then integrated with the EPD and GAIL models to formulate the personalized longitudinal motion planning policy. This combination allows the framework to use reinforcement learning for policy optimization, trajectory prediction for interactive environment modeling, and imitation learning for style adaptation. The policy was trained and tested on a natural driving dataset, which helps evaluate it under data drawn from real driving behavior rather than only from simplified simulation.
The findings reported in the paper indicate that the proposed policy can adapt to the driving style of each target driver. The authors state that it achieves personalized driving while simultaneously meeting stringent performance indices in longitudinal motion planning compared with human drivers. This suggests that learning-based planning can support both personalization and performance, rather than treating them as separate or competing goals.
Further validation will still be needed across broader traffic scenarios, vehicle platforms, driver populations, and safety-critical edge cases. Even so, the study offers a strong indication that combining reinforcement learning with imitation learning can help intelligent vehicles plan longitudinal motion in a more personalized and acceptable way. As automated driving systems move closer to everyday use, methods that align vehicle behavior with human expectations may become increasingly important for comfort, trust, and adoption.
Reference
Author:
Chongpu Chen a , Xinbo Chen a , Peng Hang b
Title of original paper:
Personalized longitudinal motion planning based on a combination of reinforcement learning and imitation learning
Article link:
https://www.sciencedirect.com/science/article/pii/S2773153725000714
Journal:
Green Energy and Intelligent Transportation
DOI:
10.1016/j.geits.2025.100321
Affiliations:
a School of Automotive Studies, Tongji University, Shanghai 201804, China
b Department of Traffic Engineering, Tongji University, Shanghai 201804, China
Green Energy and Intelligent Transportation
Experimental study
Not applicable
Personalized longitudinal motion planning based on a combination of reinforcement learning and imitation learning
31-Dec-2025