发表机构
Technische Hochschule Augsburg; TTZ Landsberg am Lech（奥格斯堡应用技术大学; 莱希河畔兰茨贝格技术转移中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对自动驾驶轨迹预测的不确定性问题，提出X-TRACK的两种不确定性感知扩展版本，通过建模并传播运动空间不确定性提升预测精度，其不确定性可校准至期望覆盖率。
AI 中文摘要
准确的轨迹预测与定义良好的预测不确定性对自动驾驶等安全关键应用至关重要。多数轨迹预测方法仅提供点估计，而不确定性感知方法通常仅在轨迹空间中量化不确定性。在物理感知方法中，预测运动变量的不确定性应被显式建模并通过车辆动力学传播，否则生成的轨迹空间不确定性可能无法完全反映底层运动预测引入的变异性。因此，本研究提出了物理感知轨迹预测框架X-TRACK的两种不确定性感知扩展版本：X-TRACK-DE和X-TRACK-MCD。该框架预测未来车辆运动变量，通过将运动空间不确定性传播至轨迹空间来建模偶然不确定性与认知不确定性；此外，对轨迹空间预测协方差应用共形预测，以构建针对期望边际覆盖率的不确定性区域。在highD数据集上的评估显示，X-TRACK-DE相比确定性基线提升了轨迹预测精度，而两种不确定性感知变体均提供了可共形校准至期望边际覆盖率的预测不确定性。
英文摘要
Accurate trajectory forecasting and well-defined predictive uncertainty are crucial for reliable, safety-critical applications such as autonomous driving. Most trajectory prediction approaches provide point estimates only, while uncertainty-aware approaches typically quantify uncertainty only in the trajectory space. In physics-aware approaches, uncertainty in the predicted motion variables should be explicitly modeled and propagated through the vehicle dynamics. Otherwise, the resulting trajectory-space uncertainty may not fully reflect the variability introduced by the underlying motion prediction. Therefore, in this work, uncertainty-aware extensions of X-TRACK (X-TRACK-DE and X-TRACK-MCD), a physics-aware trajectory prediction framework, are proposed. The proposed framework predicts future vehicle motion variables and models both aleatoric and epistemic uncertainties by propagating motion space uncertainty to trajectory space. Additionally, conformal prediction is applied to the trajectory space predictive covariance to construct uncertainty regions targeting a desired marginal coverage level. Evaluation on the highD dataset shows that X-TRACK-DE improves trajectory prediction accuracy over the deterministic baseline, while both uncertainty-aware variants provide predictive uncertainty that can be conformally calibrated to the desired marginal coverage level.
