Motion prediction is essential for safe and efficient autonomous driving. However, the inexplicability and uncertainty of complex artificial intelligence models may lead to unpredictable failures of the motion prediction module, which may mislead the system to make unsafe decisions. Therefore, it is necessary to develop methods to guarantee reliable autonomous driving, where failure detection is a potential direction. Uncertainty estimates can be used to quantify the degree of confidence a model has in its predictions and may be valuable for failure detection. We propose a framework of failure detection for motion prediction from the uncertainty perspective, considering both motion uncertainty and model uncertainty, and formulate various uncertainty scores according to different prediction stages. The proposed approach is evaluated based on different motion prediction algorithms, uncertainty estimation methods, uncertainty scores, etc., and the results show that uncertainty is promising for failure detection for motion prediction but should be used with caution.

翻译：运动预测对于安全高效的自动驾驶至关重要。然而，复杂人工智能模型的不可解释性与不确定性可能导致运动预测模块出现不可预见的故障，从而误导系统做出不安全决策。因此，亟需开发保障自动驾驶可靠性的方法，其中故障检测是一个潜在方向。不确定性估计可用于量化模型对其预测的置信度，并可能对故障检测具有重要价值。本文从不确定性视角提出了一种运动预测故障检测框架，综合考虑运动不确定性与模型不确定性，并根据不同预测阶段构建了多种不确定性评分。基于不同运动预测算法、不确定性估计方法及不确定性评分等维度的评估结果表明，不确定性在运动预测故障检测中具有应用前景，但需谨慎使用。