Towards Human-Like Trajectory Prediction for Autonomous Driving: A Behavior-Centric Approach
This addresses the problem of accurate trajectory prediction for autonomous driving systems, representing a strong specific gain rather than a paradigm shift.
The researchers tackled vehicle trajectory prediction for autonomous driving by developing HiT, a behavior-aware model with dynamic centrality measures that outperformed other top models across multiple real-world datasets, particularly excelling in aggressive driving scenarios.
Predicting the trajectories of vehicles is crucial for the development of autonomous driving (AD) systems, particularly in complex and dynamic traffic environments. In this study, we introduce HiT (Human-like Trajectory Prediction), 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 datasets, including NGSIM, HighD, RounD, ApolloScape, and MoCAD++. The results demonstrate that HiT consistently outperforms other top 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 fully autonomous driving systems.