X-TRACK 引入不确定性建模，改进高速公路轨迹预测
Uncertainty-Aware Optimization for Physics-Aware Highway Trajectory Prediction
一篇自动驾驶方向的论文，把 X-TRACK 的不确定性从运动空间一路传播到轨迹空间，还用 conformal prediction 做校准，highD 数据集上精度也涨了。
论文提出 X-TRACK-DE 和 X-TRACK-MCD 两个不确定性感知扩展版本，基于物理感知轨迹预测框架 X-TRACK。方法在运动变量空间建模偶然不确定性与认知不确定性，并将其传播到轨迹空间，再用 conformal prediction 校准到目标边际覆盖率。在 highD 数据集上，X-TRACK-DE 的轨迹预测精度超过确定性基线。该工作面向自动驾驶等安全关键场景的可靠性需求。
Uncertainty-Aware Optimization for Physics-Aware Highway Trajectory Prediction
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.
