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的能力是实现完全自动驾驶的关键挑战之一。为应对这一难题，我们首次提出一种新颖的行为感知轨迹预测模型（BAT），该模型整合了交通心理学、人类行为与决策过程的研究发现。模型包含行为感知、交互感知、优先级感知与位置感知模块，能够感知并理解底层交互关系，同时考虑预测中的不确定性与可变性，在不对驾驶行为进行刚性分类的前提下实现更高层次的学习与灵活性。重要的是，该方法无需训练过程中的人工标注，并解决了非连续性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）及澳门自动驾驶（MoCAD）数据集上评估了BAT的性能，证明其在预测精度与效率方面均优于当前最先进的基准模型。值得注意的是，即使仅使用25%的训练数据，该模型仍优于多数基线方法，展现了其在车辆轨迹预测中的鲁棒性与高效性，并有望减少自动驾驶训练所需的数据量，尤其是在边缘场景中。综上，该行为感知模型标志着自动驾驶车辆在达到与人类驾驶员同等轨迹预测水平方面取得重要进展。项目页面见：https://github.com/Petrichor625/BATraj-Behavior-aware-Model。