Abstract:
The mechanism behind collisions between vehicles and pedestrians must be thoroughly studied in order to prevent future traffic accidents. In particular, preventing collis...Show MoreMetadata
Abstract:
The mechanism behind collisions between vehicles and pedestrians must be thoroughly studied in order to prevent future traffic accidents. In particular, preventing collisions where pedestrian steps out onto the road from behind an obstruction such as buildings, walls or vehicles is a challenging problem. To tackle this problem, we propose situation dependent topic model (SDTM), a regression model that predicts dangerous vehicle-pedestrian encounter in response to different driving situations, which also provides a framework to analyze and understand the underlying factors that lead to dangerous situations. Complex nature of situations where collisions with pedestrians happen can be expressed well by defining how dangerous situations arise differently for each driving situation pattern retrieved using statistical topic modeling. In experiments, we compare the performance of SDTM with orthodox logistic regression models using vehicle-pedestrian encounters in near-miss incidents. We also show the result of acquired knowledge that can form the basis of many other researches concerning pedestrian safety.
Published in: 2017 IEEE Intelligent Vehicles Symposium (IV)
Date of Conference: 11-14 June 2017
Date Added to IEEE Xplore: 31 July 2017
ISBN Information: