Queensland University of Technology (QUT), Centre for Accident Research and Road Safety - Queensland (CARRS-Q), Brisbane, QLD, Australia.
Queensland University of Technology (QUT), Centre for Accident Research and Road Safety - Queensland (CARRS-Q), Brisbane, QLD, Australia.
Accid Anal Prev. 2021 Jul;157:106165. doi: 10.1016/j.aap.2021.106165. Epub 2021 May 24.
Drivers continually interact with other road users and use information from the road environment to make decisions to control their vehicle. A clear understanding of different parameters impacting this interaction can provide us with a new design approach for a more effective driver assistance system - a personalised trajectory prediction system. This paper highlights the influential factors on trajectory prediction system performance by (i) identifying driver behaviours impacting the trajectory prediction system; and (ii) analysing other contributing factors such as traffic density, secondary task, gender and age group. To explore the most influential contributing factors, we first train an interaction-aware trajectory prediction system using time-series data derived from the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS). Prediction error is then analysed based on driver characteristics such as driver profile which is subjectively measured through self-reported questions, and driving performance which is based on evaluation of time-series information such as speed, acceleration, jerk, time, and space headway. The results show that prediction error significantly increased in the scenarios where the driver engaged in risky behaviour. Analysis shows that trajectory prediction system performance is also affected by factors such as traffic density, engagement in secondary tasks, driver gender and age group. We show that the driver profile, which is subjectively measured using self-reported questionnaires, is not as significant as the driving performance information, which is objectively measured and extracted during each specific driving scenario.
驾驶员持续与其他道路使用者交互，并利用道路环境中的信息做出决策以控制车辆。清楚了解影响这种交互的不同参数可以为我们提供一种新的设计方法，以实现更有效的驾驶员辅助系统——个性化轨迹预测系统。本文通过（i）确定影响轨迹预测系统性能的驾驶员行为；以及（ii）分析交通密度、次要任务、性别和年龄组等其他影响因素，突出了对轨迹预测系统性能有影响的因素。为了探索最具影响力的影响因素，我们首先使用源自第二战略公路研究计划（SHRP2）自然驾驶研究（NDS）的时间序列数据来训练一个具有交互意识的轨迹预测系统。然后根据驾驶员特征（例如驾驶员档案，这是通过自我报告问题主观测量的，以及驾驶性能，这是基于对速度、加速度、急动度、时间和空间车头间隔等时间序列信息的评估）来分析预测误差。结果表明，在驾驶员从事危险行为的情况下，预测误差显著增加。分析表明，轨迹预测系统的性能还受到交通密度、从事次要任务、驾驶员性别和年龄组等因素的影响。我们表明，驾驶员档案（使用自我报告问卷进行主观测量）不如驾驶性能信息重要，后者是在每个特定驾驶场景中客观测量和提取的。
