Liu Siyuan, Li Quan, Sun Huamu, Zhou Qing, Nie Bingbing
School of Vehicle and Mobility, State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing, China.
Traffic Inj Prev. 2025;26(3):281-290. doi: 10.1080/15389588.2024.2408402. Epub 2024 Dec 13.
Understanding pedestrians' pre-crash avoidance kinematics is crucial for improving the identification of potential collision areas in interactions with highly automated vehicles (HAVs). Age significantly influences pedestrian avoidance velocity and the subsequent crash risks. However, current active safety systems in HAVs often overlook the influence of pedestrians' avoidance velocity and age on imminent accidents. This study analyzes how age affects pedestrian avoidance velocity and explores the incorporation of these factors in pre-crash scenarios to identify potential collision areas between pedestrians and vehicles.
Due to the infeasibility of measuring pedestrian avoidance behaviors in real-world pre-crash scenarios, we designed an indoor experimental platform replicating emergency crossroad scenarios to prompt subjects to mimic avoidance behaviors. 7 young and 7 middle-aged subjects participated in the experiment, resulting in a collection of 306 forward-avoidance, 297 backward-avoidance, and 42 normal-walking posture sequences. We developed a scaling approach integrating pedestrian kinematics and muscle physiology to establish a velocity-mapping relationship between young and middle-aged groups. Finally, we proposed an identification method for potential collision areas that considers pedestrians' age and avoidance velocity.
Middle-aged subjects required more time for natural avoidance actions averaging 0.15 s for forward and 0.25 s for backward avoidance, compared to their younger counterparts. While the forward avoidance velocity of the middle-aged subjects exhibited an average decrease of 0.3 m/s compared to young subjects, their backward avoidance velocity remained nearly identical. Overall, middle-aged subjects have a larger potential collision area than young participants. Pedestrians who actively avoid vehicles have a smaller potential collision area compared to those who remain normal walking.
We developed an indoor simulated pre-crash scenario experiment and a scaling approach to reveal the correlation between pedestrian avoidance velocity and age. This method can be further applied to obtain the avoidance velocity of elderly pedestrians. Additionally, we validate the effect of these factors in assessing potential collision areas. The decrease in avoidance velocity highlights a larger potential collision area for middle-aged pedestrians when interacting with vehicles. Such facts and data shall be appropriately considered in developing intelligent protection systems for pedestrians.
了解行人碰撞前的避险运动学对于改善与高度自动驾驶车辆（HAV）交互中潜在碰撞区域的识别至关重要。年龄会显著影响行人的避险速度以及随后的碰撞风险。然而，当前HAV中的主动安全系统往往忽视了行人避险速度和年龄对即将发生事故的影响。本研究分析年龄如何影响行人避险速度，并探讨在碰撞前场景中纳入这些因素以识别行人和车辆之间的潜在碰撞区域。
由于在现实世界碰撞前场景中测量行人避险行为不可行，我们设计了一个室内实验平台，复制紧急十字路口场景以促使受试者模拟避险行为。7名年轻受试者和7名中年受试者参与了实验，共收集到306个向前避险、297个向后避险和42个正常行走姿势序列。我们开发了一种整合行人运动学和肌肉生理学的缩放方法，以建立年轻组和中年组之间的速度映射关系。最后，我们提出了一种考虑行人年龄和避险速度的潜在碰撞区域识别方法。
与年轻受试者相比，中年受试者进行自然避险动作所需时间更长，向前避险平均需要0.15秒，向后避险平均需要0.25秒。中年受试者的向前避险速度与年轻受试者相比平均降低了0.3米/秒，而他们的向后避险速度几乎相同。总体而言，中年受试者的潜在碰撞区域比年轻参与者更大。与保持正常行走的行人相比，主动避让车辆的行人潜在碰撞区域更小。
我们开发了一个室内模拟碰撞前场景实验和一种缩放方法，以揭示行人避险速度与年龄之间的相关性。该方法可进一步应用于获取老年行人的避险速度。此外，我们验证了这些因素在评估潜在碰撞区域方面的作用。避险速度的降低凸显了中年行人与车辆交互时更大的潜在碰撞区域。在开发行人智能保护系统时应适当考虑这些事实和数据。
