Trajectory prediction plays a crucial role in the autonomous driving stack by enabling autonomous vehicles to anticipate the motion of surrounding agents. Goal-based prediction models have gained traction in recent years for addressing the multimodal nature of future trajectories. Goal-based prediction models simplify multimodal prediction by first predicting 2D goal locations of agents and then predicting trajectories conditioned on each goal. However, a single 2D goal location serves as a weak inductive bias for predicting the whole trajectory, often leading to poor map compliance, i.e., part of the trajectory going off-road or breaking traffic rules. In this paper, we improve upon goal-based prediction by proposing the Path-based prediction (PBP) approach. PBP predicts a discrete probability distribution over reference paths in the HD map using the path features and predicts trajectories in the path-relative Frenet frame. We applied the PBP trajectory decoder on top of the HiVT scene encoder and report results on the Argoverse dataset. Our experiments show that PBP achieves competitive performance on the standard trajectory prediction metrics, while significantly outperforming state-of-the-art baselines in terms of map compliance.

翻译：轨迹预测在自动驾驶系统中扮演关键角色，使自动驾驶车辆能够预判周围智能体的运动。基于目标的预测模型近年来因能处理未来轨迹的多模态特性而备受关注。这类模型通过先预测智能体的二维目标位置，再基于每个目标预测轨迹，从而简化多模态预测。然而，单一的二维目标位置作为整条轨迹的预测依据时存在弱归纳偏置问题，常导致轨迹与地图匹配度差，即部分轨迹偏离道路或违反交通规则。本文提出基于路径的预测方法以改进目标预测范式。该方法利用高精地图路径特征预测参考路径上的离散概率分布，并在路径相关的Frenet坐标系中生成轨迹。我们将PBP轨迹解码器应用于HiVT场景编码器，并在Argoverse数据集上进行验证。实验表明，PBP在标准轨迹预测指标上表现优异，同时在地图匹配度方面显著超越现有最优基线方法。