Abstract
Trajectory prediction is a cornerstone in autonomous driving (AD), playing a critical role in enabling vehicles to navigate safely and efficiently in dynamic environments. To address this task, this paper presents a novel trajectory prediction model tailored for accuracy in the face of heterogeneous and uncertain traffic scenarios. At the heart of this model lies the Characterized Diffusion Module, an innovative module designed to simulate traffic scenarios with inherent uncertainty. This module enriches the predictive process by infusing it with detailed semantic information, thereby enhancing trajectory prediction accuracy. Complementing this, our Spatio-Temporal (ST) Interaction Module captures the nuanced effects of traffic scenarios on vehicle dynamics across both spatial and temporal dimensions with remarkable effectiveness. Demonstrated through exhaustive evaluations, our model sets a new standard in trajectory prediction, achieving state-of-the-art (SOTA) results on the Next Generation Simulation (NGSIM), Highway Drone (HighD), and Macao Connected Autonomous Driving (MoCAD) datasets across both short and extended temporal spans. This performance underscores the model's unparalleled adaptability and efficacy in navigating complex traffic scenarios, including highways, urban streets, and intersections.
Abstract (translated)
轨迹预测是自动驾驶（AD）中的关键技术，在使车辆在动态环境中安全高效地导航方面发挥了重要作用。为解决这个问题，本文提出了一种专门针对异质和不确定交通场景的轨迹预测模型。这个模型的核心是基于特征扩散模块，这是一种创新的模块，旨在通过模拟固有不确定性的交通场景来提高预测准确性。通过向这个模型注入详细语义信息，从而增强了轨迹预测的准确性。此外，我们的空间-时间（ST）交互模块有效地捕捉了交通场景对车辆动力学的影响，在时间和空间维度上实现了对车辆动态的微小影响。通过详尽评估，我们的模型在轨迹预测方面达到了新的标准，在Next Generation Simulation（NGSIM）、高速公路无人机（HighD）和澳门 Connected Autonomous Driving（MoCAD）数据集上取得了最先进的（SOTA）结果。这种性能凸显了模型的无与伦比的适应性和有效性，使其能够应对复杂的交通场景，包括高速公路、城市街道和交叉口。
URL
https://arxiv.org/abs/2405.02145
