The ability to accurately predict the trajectory of surrounding vehicles is a critical hurdle to overcome on the journey to fully autonomous vehicles. To address this challenge, we pioneer a novel behavior-aware trajectory prediction model (BAT) that incorporates insights and findings from traffic psychology, human behavior, and decision-making. Our model consists of behavior-aware, interaction-aware, priority-aware, and position-aware modules that perceive and understand the underlying interactions and account for uncertainty and variability in prediction, enabling higher-level learning and flexibility without rigid categorization of driving behavior. Importantly, this approach eliminates the need for manual labeling in the training process and addresses the challenges of non-continuous behavior labeling and the selection of appropriate time windows. We evaluate BAT's performance across the Next Generation Simulation (NGSIM), Highway Drone (HighD), Roundabout Drone (RounD), and Macao Connected Autonomous Driving (MoCAD) datasets, showcasing its superiority over prevailing state-of-the-art (SOTA) benchmarks in terms of prediction accuracy and efficiency. Remarkably, even when trained on reduced portions of the training data (25%), our model outperforms most of the baselines, demonstrating its robustness and efficiency in predicting vehicle trajectories, and the potential to reduce the amount of data required to train autonomous vehicles, especially in corner cases. In conclusion, the behavior-aware model represents a significant advancement in the development of autonomous vehicles capable of predicting trajectories with the same level of proficiency as human drivers. The project page is available at https://github.com/Petrichor625/BATraj-Behavior-aware-Model.

翻译：准确预测周围车辆轨迹是实现全自动驾驶的关键挑战之一。为解决这一问题，我们率先提出一种新颖的行为感知轨迹预测模型（BAT），该模型融合了交通心理学、人类行为学及决策科学的研究成果与发现。模型由行为感知、交互感知、优先级感知与位置感知模块构成，能够感知并理解底层交互关系，同时处理预测中的不确定性与可变性，从而在不依赖驾驶行为刚性分类的前提下实现更高级别的学习与灵活性。值得注意的是，本方法无需在训练过程中进行人工标注，并有效解决了非连续行为标注及合适时间窗口选取等难题。我们在下一代仿真（NGSIM）、高速公路无人机（HighD）、环岛无人机（RounD）及澳门互联自动驾驶（MoCAD）数据集上评估了BAT的性能，结果表明其在预测精度与效率方面均优于当前最先进基准模型。尤为突出的是，即使在仅使用25%训练数据的情况下，该模型的预测表现仍超越大多数基线方法，充分证明了其在车辆轨迹预测中的鲁棒性与高效性，并展现出减少自动驾驶训练数据需求（尤其是针对边缘场景）的潜力。总之，该行为感知模型标志着自动驾驶车辆在达到人类驾驶员同等轨迹预测能力方面取得了重要进展。项目页面详见https://github.com/Petrichor625/BATraj-Behavior-aware-Model。