An Augmentation Function for Active Pedestrian Safety System Based on Crash Risk Evaluation
Lishengsa Yue, Mohamed Abdel‐Aty, Yina Wu, Jinghui Yuan
Abstract
This study proposed an augmentation function to current active pedestrian safety systems (APSSs), which is expected to be effective for pedestrian crashes caused by pedestrians’ unexpected behavior. The augmentation function estimates the crash risk with a pedestrian given its time-space-distance relationship with the pedestrian; the crash risk represents the probability of hitting that pedestrian given all the pedestrian's possible random trajectories in the near future. Once the crash risk exceeds a toleration threshold, the augmentation function activates evasive actions, even if there is no current conflict with the pedestrian. A Monte Carlo process was used to estimate the crash risk under different sets of vehicle and pedestrian kinematic features. The possible pedestrian trajectories were sampled from a fine-tuned Markovian integrated random walk model; in particular, kinematic variations between pedestrian types were considered. Then, the effectiveness of evasive actions was evaluated. It was found that children and young/middle-aged pedestrians require higher-intensity evasive actions in zones of moderate and severe crash risk; while old pedestrians need higher-intensity evasive actions in zones of mild crash risk. In addition, it is necessary to select proper speed reduction rate and combination of lane change and speed, to reduce the crash risk. Finally, the study demonstrated that a field of view of 50° and a detection range of 40 m would be the minimum requirement to support the augmentation function, which requires an upgrade for many automobile manufacturers’ current APSSs.