The development of approaches for trajectory prediction requires metrics to validate and compare their performance. Currently established metrics are based on Euclidean distance, which means that errors are weighted equally in all directions. Euclidean metrics are insufficient for structured environments like roads, since they do not properly capture the agent's intent relative to the underlying lane. In order to provide a reasonable assessment of trajectory prediction approaches with regard to the downstream planning task, we propose a new metric that is lane distance-based: Lane Miss Rate (LMR). For the calculation of LMR, the ground-truth and predicted endpoints are assigned to lane segments, more precisely their centerlines. Measured by the distance along the lane segments, predictions that are within a certain threshold distance to the ground-truth count as hits, otherwise they count as misses. LMR is then defined as the ratio of sequences that yield a miss. Our results on three state-of-the-art trajectory prediction models show that LMR preserves the order of Euclidean distance-based metrics. In contrast to the Euclidean Miss Rate, qualitative results show that LMR yields misses for sequences where predictions are located on wrong lanes. Hits on the other hand result for sequences where predictions are located on the correct lane. This means that LMR implicitly weights Euclidean error relative to the lane and goes into the direction of capturing intents of traffic agents. The source code of LMR for Argoverse 1 is publicly available.

翻译：轨迹预测方法的发展需要评估指标来验证和比较其性能。当前已建立的指标基于欧几里得距离，这意味着所有方向上的误差被同等加权。对于道路等结构化环境，欧几里得指标并不充分，因为它们未能正确捕捉智能体相对于底层车道的意图。为了更合理地评估轨迹预测方法在下游规划任务中的表现，我们提出一种基于车道距离的新指标：车道未命中率（LMR）。计算LMR时，将真实终点与预测终点分配至车道段，更精确地说是其中心线。通过沿车道段的距离衡量，在特定阈值距离内的预测计为命中，否则计为未命中。LMR定义为产生未命中的序列比例。我们在三个最先进的轨迹预测模型上的实验结果表明，LMR保留了基于欧几里得距离的指标排序。与欧几里得未命中率相比，定性结果显示，当预测位于错误车道时，LMR会产生未命中；而当预测位于正确车道时则产生命中。这意味着LMR隐式地根据车道对欧几里得误差进行加权，并朝着捕捉交通智能体意图的方向发展。LMR在Argoverse 1数据集上的源代码已公开。