A critical aspect of safe and efficient motion planning for autonomous vehicles (AVs) is to handle the complex and uncertain behavior of surrounding human-driven vehicles (HDVs). Despite intensive research on driver behavior prediction, existing approaches typically overlook the interactions between AVs and HDVs assuming that HDV trajectories are not affected by AV actions. To address this gap, we present a transformer-transfer learning-based interaction-aware trajectory predictor for safe motion planning of autonomous driving, focusing on a vehicle-to-vehicle (V2V) interaction scenario consisting of an AV and an HDV. Specifically, we construct a transformer-based interaction-aware trajectory predictor using widely available datasets of HDV trajectory data and further transfer the learned predictor using a small set of AV-HDV interaction data. Then, to better incorporate the proposed trajectory predictor into the motion planning module of AVs, we introduce an uncertainty quantification method to characterize the errors of the predictor, which are integrated into the path-planning process. Our experimental results demonstrate the value of explicitly considering interactions and handling uncertainties.

翻译：自动驾驶车辆（AVs）实现安全高效运动规划的一个关键方面在于处理周围人工驾驶车辆（HDVs）复杂且不确定的行为。尽管针对驾驶员行为预测已开展大量研究，现有方法通常忽略自动驾驶车辆与人工驾驶车辆之间的交互作用，其假设人工驾驶车辆的轨迹不受自动驾驶车辆行为的影响。为弥补这一不足，本文提出一种基于Transformer迁移学习的交互感知轨迹预测器，用于自动驾驶的安全运动规划，重点关注由一辆自动驾驶车辆和一辆人工驾驶车辆构成的车辆对车辆（V2V）交互场景。具体而言，我们利用广泛可用的人工驾驶车辆轨迹数据集构建基于Transformer的交互感知轨迹预测器，并进一步通过少量自动驾驶车辆-人工驾驶车辆交互数据对已学习的预测器进行迁移训练。随后，为更好地将所提出的轨迹预测器整合至自动驾驶车辆的运动规划模块，我们引入一种不确定性量化方法以表征预测器的误差，并将这些误差纳入路径规划过程。实验结果验证了显式考虑交互作用并处理不确定性的重要价值。