Toward Human-Like Trajectory Prediction for Autonomous Driving: A Behavior-Centric Approach

Haicheng Liao (), Zhenning Li (), Guohui Zhang (), Keqiang Li () and Chengzhong Xu ()
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Haicheng Liao: State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau SAR, Macau 999078; and Department of Computer and Information Science, University of Macau, Macau SAR, Macau 999078
Zhenning Li: State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau SAR, Macau 999078; and Department of Computer and Information Science, University of Macau, Macau SAR, Macau 999078; and Department of Civil and Environmental Engineering, University of Macau, Macau SAR, Macau 999078
Guohui Zhang: Department of Civil and Environmental Engineering, University of Hawaii, Honolulu, Hawaii 96822
Keqiang Li: School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Chengzhong Xu: State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau SAR, Macau 999078; and Department of Computer and Information Science, University of Macau, Macau SAR, Macau 999078

Transportation Science, 2025, vol. 59, issue 4, 823-852

Abstract: Predicting the trajectories of vehicles is crucial for the development of autonomous driving systems, particularly in complex and dynamic traffic environments. In this study, we introduce human-like trajectory prediction (HiT), a novel model designed to enhance trajectory prediction by incorporating behavior-aware modules and dynamic centrality measures. Unlike traditional methods that primarily rely on static graph structures, HiT leverages a dynamic framework that accounts for both direct and indirect interactions among traffic participants. This allows the model to capture the subtle, yet significant, influences of surrounding vehicles, enabling more accurate and human-like predictions. To evaluate HiT’s performance, we conducted extensive experiments using diverse and challenging real-world data sets, including NGSIM, HighD, RounD, ApolloScape, and MoCAD++. The results demonstrate that HiT consistently outperforms state-of-the-art models across multiple metrics, particularly excelling in scenarios involving aggressive driving behaviors. This research presents a significant step forward in trajectory prediction, offering a more reliable and interpretable approach for enhancing the safety and efficiency of autonomous driving systems.

Keywords: autonomous driving; trajectory prediction; driving behavior; interaction understanding (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:59:y:2025:i:4:p:823-852

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