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

arXiv 2505.21565 ↗First posted 27 May 2025cs.RO cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 55 references that are in the map. First posted on arXiv 27 May 2025.

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References · 17

  1. 2017Attention Is All You Needcited by 60,361
  2. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,358
  3. 2018Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networkscited by 690
  4. 2018Convolutional Social Pooling for Vehicle Trajectory Predictioncited by 221
  5. 2019Multiple Futures Predictioncited by 178
  6. 2018TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agentscited by 111
  7. 2020TPNet: Trajectory Proposal Network for Motion Predictioncited by 80
  8. 2018Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMscited by 77
  9. 2025CoT-Drive: Efficient Motion Forecasting for Autonomous Driving with LLMs and Chain-of-Thought Promptingcited by 26
  10. 2022S2TNet: Spatio-Temporal Transformer Networks for Trajectory Prediction in Autonomous Drivingcited by 22
  11. 2021Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidancecited by 21
  12. 2020CMetric: A Driving Behavior Measure Using Centrality Functionscited by 19
5 more
  1. 2024Human Observation-Inspired Trajectory Prediction for Autonomous Driving in Mixed-Autonomy Traffic Environmentscited by 13
  2. 2024NEST: A Neuromodulated Small-world Hypergraph Trajectory Prediction Model for Autonomous Drivingcited by 12
  3. 2025Minds on the Move: Decoding Trajectory Prediction in Autonomous Driving with Cognitive Insightscited by 10
  4. 2021Maneuver-Aware Pooling for Vehicle Trajectory Predictioncited by 3
  5. 2024DEMO: A Dynamics-Enhanced Learning Model for Multi-Horizon Trajectory Prediction in Autonomous Vehiclescited by 3

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