Can generative AI improve vehicle trajectory prediction in car-following scenarios?

To answer this question, researchers from the University of Wisconsin–Madison, Tongji University, and collaborators developed FollowGen, a conditional diffusion model that integrates historical motion features and inter-vehicle interactions to generate safer and more reliable trajectory predictions for autonomous driving.

They published their study on 16 October 2025, in Communications in Transportation Research.

“We introduce a scaled noise conditioning mechanism that embeds historical motion features into the forward diffusion process. This allows the model to account for motion-aware uncertainties from the beginning and generate trajectories that better align with real driving behaviors,” says Junwei You, a Ph.D. candidate studying autonomous driving and intelligent transportation.

Different performance across diverse driving scenarios
In the study, the research group evaluated FollowGen using multiple real-world datasets, covering both urban and highway environments. They examined car-following situations such as human-driven vehicles following each other, autonomous vehicles following humans, and humans following autonomous vehicles.

“This design enables the model to explicitly capture the interaction between leading and following vehicles. As a result, FollowGen delivers consistent improvements over strong baselines, particularly in terms of final displacement accuracy and reliability,” explains Haotian Shi, Associate Professor at Tongji University.

Visualization results show how initially chaotic scaled noise progressively evolves into accurate trajectory predictions, demonstrating the effectiveness of combining diffusion models with car-following dynamics.

Significant implications for autonomous driving
The findings suggest that incorporating generative AI into trajectory prediction can enhance the safety and robustness of autonomous driving systems. Beyond real-time planning, the model also offers potential applications in large-scale traffic simulation and intelligent transportation system design.

“Our research demonstrates that diffusion-based models are not only powerful for uncertainty modeling but can also be adapted to the unique requirements of vehicle interactions on the road. This represents an important step toward safer and more reliable autonomous vehicles,” says Sikai Chen, Assistant Professor at the University of Wisconsin-Madison.

The above research is published in Communications in Transportation Research (COMMTR), which is a fully open access journal co-published by Tsinghua University Press and Elsevier. COMMTR publishes peer-reviewed high-quality research representing important advances of significance to emerging transport systems. COMMTR is also among the first transportation journals to make the Replication Package mandatory to facilitate researchers, practitioners, and the general public in understanding and advancing existing knowledge. At its discretion, Tsinghua University Press will pay the open access fee for all published papers in 2025.

About Communications in Transportation Research

Communications in Transportation Research was launched in 2021, with academic support provided by Tsinghua University and China Intelligent Transportation Systems Association. The Editors-in-Chief are Professor Xiaobo Qu, a member of the Academia Europaea from Tsinghua University and Professor Shuai’an Wang from Hong Kong Polytechnic University. The journal mainly publishes high-quality, original research and review articles that are of significant importance to emerging transportation systems, aiming to serve as an international platform for showcasing and exchanging innovative achievements in transportation and related fields, fostering academic exchange and development between China and the global community.

It has been indexed in SCIE, SSCI, Ei Compendex, Scopus, CSTPCD, CSCD, OAJ, DOAJ, TRID and other databases. It was selected as Q1 Top Journal in the Engineering and Technology category of the Chinese Academy of Sciences (CAS) Journal Ranking List. In 2022, it was selected as a High-Starting-Point new journal project of the “China Science and Technology Journal Excellence Action Plan”. In 2024, it was selected as the Support the Development Project of “High-Level International Scientific and Technological Journals”. The same year, it was also chosen as an English Journal Tier Project of the “China Science and Technology Journal Excellence Action Plan Phase Ⅱ”. In 2024, it received the first impact factor (2023 IF) of 12.5, ranking Top1 (1/58, Q1) among all journals in “TRANSPORTATION” category. In 2025, its 2024 IF was announced as 14.5, maintaining the Top 1 position (1/61, Q1) in the same category. Tsinghua University Press will cover the open access fee for all published papers in 2025.