面向自动驾驶的行人行为预测：需求、指标与相关特征
Pedestrian Behavior Prediction for Automated Driving: Requirements, Metrics, and Relevant Features
AI总结：
该研究针对自动驾驶中行人行为预测的难点，通过系统级方法分析需求、提出新型系统视角预测指标，经大规模真实数据集验证及消融实验，证明了系统级方法的重要性。
摘要：
自动驾驶车辆需要全面理解交通场景，以实现安全且具有预判性的驾驶。在该场景下，行人行为预测极具挑战性，因为行人行为会受到多种因素的影响。本文通过系统级方法，深入分析了自动驾驶中行人行为预测的相关需求。为此，我们研究了真实场景中人类驾驶员与行人的交互情况。基于人类驾驶行为，我们推导了自动驾驶车辆的合理反应模式，并明确了行人预测的相关需求，其中包括一种专为从系统级视角衡量预测性能而设计的新型指标。我们在包含数千次真实行人-车辆交互的大规模数据集上对所提出的指标进行了评估。此外，我们还开展了消融实验，以评估不同上下文线索的重要性，并将这些结果与使用行人预测领域现有成熟性能指标得到的结果进行对比。研究结果凸显了采用系统级方法开展行人行为预测的重要性。
英文摘要：
Automated vehicles require a comprehensive understanding of traffic situations to ensure safe and anticipatory driving. In this context, the prediction of pedestrians is particularly challenging as pedestrian behavior can be influenced by multiple factors. In this paper, we thoroughly analyze the requirements on pedestrian behavior prediction for automated driving via a system-level approach. To this end we investigate real-world pedestrian-vehicle interactions with human drivers. Based on human driving behavior we then derive appropriate reaction patterns of an automated vehicle and determine requirements for the prediction of pedestrians. This includes a novel metric tailored to measure prediction performance from a system-level perspective. The proposed metric is evaluated on a large-scale dataset comprising thousands of real-world pedestrian-vehicle interactions. We furthermore conduct an ablation study to evaluate the importance of different contextual cues and compare these results to ones obtained using established performance metrics for pedestrian prediction. Our results highlight the importance of a system-level approach to pedestrian behavior prediction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
