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.
翻译:自动车辆需要全面了解交通情况,以确保安全和预先驾驶。在这方面,行人预测尤其具有挑战性,因为行人行为可能受到多种因素的影响。在本文中,我们透彻分析通过系统一级方法自动驾驶行人行为预测的要求。为此,我们调查现实世界行人-车辆与人驾驶者的互动。根据人的驾驶行为,我们随后得出自动车辆的适当反应模式,并确定行人预测的要求。这包括一个新颖的计量标准,专门从系统一级的角度衡量预测业绩。对由千个实际世界行人-车辆相互作用组成的大型数据集进行了评价。我们还进行一项模拟研究,以评估不同背景线索的重要性,并将这些结果与使用既定行人预测性指标获得的结果进行比较。我们的结果突出了从系统一级对行人行为预测采取的方法的重要性。