利用自车规划轨迹提升目标车辆轨迹预测精度
EPN: An Ego Vehicle Planning-Informed Network for Target Trajectory Prediction
- 引入自车未来规划轨迹作为输入，模拟车辆间相互影响
- 终点预测模块结合修正机制，实现多模态轨迹生成
- 在NGSIM和HighD数据集上误差降低超60%，适合自动驾驶场景
轨迹预测在提升自动驾驶安全性方面至关重要。然而，由于任务本身具有高度动态性和多模态特性，准确预测目标车辆未来轨迹仍面临重大挑战。为此，本文提出一种自车规划信息驱动的网络（EPN）用于多模态轨迹预测。真实驾驶中，车辆未来轨迹不仅受自身历史轨迹影响，还受其他车辆行为制约。因此，我们将自车的未来规划轨迹作为额外输入，以模拟车辆间的相互作用。针对基于驾驶意图的方法常存在的意图模糊和预测误差大的问题，提出目标车辆终点预测模块：先预测终点，通过修正机制优化，再生成多模态预测轨迹。实验结果表明，在NGSIM数据集上，EPN在RMSE、ADE和FDE上分别平均降低34.9%、30.7%和30.4%；在HighD数据集上，相应指标平均降低64.6%、64.5%和64.3%。代码将在论文被接受后开源。
原文摘要 · Abstract (English)
Trajectory prediction plays a crucial role in improving the safety of autonomous vehicles. However, due to the highly dynamic and multimodal nature of the task, accurately predicting the future trajectory of a target vehicle remains a significant challenge. To address this challenge, we propose an Ego vehicle Planning-informed Network (EPN) for multimodal trajectory prediction. In real-world driving, the future trajectory of a vehicle is influenced not only by its own historical trajectory, but also by the behavior of other vehicles. So, we incorporate the future planned trajectory of the ego vehicle as an additional input to simulate the mutual influence between vehicles. Furthermore, to tackle the challenges of intention ambiguity and large prediction errors often encountered in methods based on driving intentions, we propose an endpoint prediction module for the target vehicle. This module predicts the target vehicle endpoints, refines them using a correction mechanism, and generates a multimodal predicted trajectory. Experimental results demonstrate that EPN achieves an average reduction of 34.9%, 30.7%, and 30.4% in RMSE, ADE, and FDE on the NGSIM dataset, and an average reduction of 64.6%, 64.5%, and 64.3% in RMSE, ADE, and FDE on the HighD dataset. The code will be open sourced after the letter is accepted.
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