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

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. History: This paper has been accepted for the Transportation Science Special Issue on Machine Learning Methods for Urban Passenger Mobility. Funding: This work was supported by the Shenzhen-Hong Kong-Macau Science and Technology Program Category C [SGDX20230821095159012], University of Macau [SRG2023-00037-IOTSC, MYRG-GRG2024-00284-IOTSC], the Science and Technology Development Fund of Macau [0021/2022/ITP, 0122/2024/RIB2 and 001/2024/SKL], the State Key Lab of Intelligent Transportation System [2024-B001], and the Jiangsu Provincial Science and Technology Program [BZ2024055]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0366 .