Pedestrian/Bicyclist Limb Motion Analysis from 110-Car TASI Video Data for Autonomous Emergency Braking Testing Surrogate Development

Abstract

Many vehicles are currently equipped with active safety systems that can detect vulnerable road users like pedestrians and bicyclists, to mitigate associated conflicts with vehicles. With the advancements in technologies and algorithms, detailed motions of these targets, especially the limb motions, are being considered for improving the efficiency and reliability of object detection. Thus, it becomes important to understand these limb motions to support the design and evaluation of many vehicular safety systems. However in current literature, there is no agreement being reached on whether or not and how often these limbs move, especially at the most critical moments for potential crashes. In this study, a total of 832 pedestrian walking or cyclist biking cases were randomly selected from one large-scale naturalistic driving database containing 480,000 video segments with a total size of 94TB, and then the 832 video clips were analyzed focusing on their limb motions. We modeled the pedestrian/bicyclist limb motions in four layers: (1) the percentages of pedestrians and bicyclists who have limb motions when crossing the road; (2) the averaged action frequency and the corresponding distributions on when there are limb motions; (3) comparisons of the limb motion behavior between crossing and non-crossing cases; and (4) the effects of seasons on the limb motions when the pedestrians/bicyclists are crossing the road. The results of this study can provide empirical foundations supporting surrogate development, benefit analysis, and standardized testing of vehicular pedestrian/ bicyclist detection and crash mitigation systems.

Skeleton model based behavior recognition for pedestrians and cyclists from vehicle sce ne camera

“…We tested 346 videos from JAAD database [23] and some pedestrian videos from TASI naturalistic database [25] [26] [27]. We analyze data manually by separating videos into different categories.…”

Development of test scenarios and bicyclist surrogate for the evaluation of bicyclist automatic emergency braking systems

“…Euro NCAP has adopted the bicyclist surrogate with fixed leg posture (both legs bent) based on their study that suggests that a majority of bicyclists (over 80 per cent) stop pedaling when crossing an intersection in Europe. By examining 484 randomly selected bicyclist video clips in the TASI 110-car NDD (Sherony et al, 2016), it is observed that 83.2 per cent of bicyclists have pedaling motion when crossing the road, and 100 per cent of bicyclists have pedaling motion when moving along the road. The average pedaling frequency is 0.85 Hz for the cases of crossing the road (CP:VDO and CP:VS-BRO) and 1.01 Hz for the cases of moving along the road (PP-VOT and PP-VHO).…”

Diseño y simulación de un sistema de frenado y evasión autónomo (OPREVU-AES) para la evitación del atropello de peatones en diferentes condiciones de tráfico

“…La caracterización del comportamiento del peatón ha sido estudiada desde el punto de vista de la predicción de la trayectoria y del tipo de reacción de este [3], utilizando filtros de tipo Kalman para el análisis de imagen, mientras que otros autores [4] se centran en el desarrollo de modelos de decisión de tipo Markov (Markov decision process, MDP) para el desarrollo y mejora de los sistemas de frenado de emergencia y vehículos autónomos. Otros estudios [5], centran su investigación en la comprensión y el análisis del movimiento de las extremidades del peatón en función de la decisión de cruzar.…”