Virtual Observation Using Location-Dependent Statistical Information of Cyclists’ Movement for Estimation of Position and Uncertainty

Crossing collisions between cyclists and automobiles around nonsignalized intersections on community roads, where visibility around the intersection is poor due to occlusions caused by house walls, is a social issue related to traffic safety in Japan. Because available observation information for collision prevention is limited on community roads, utilizing the accumulated data is useful to compensate for the lack of observation information. Given these motivations, we propose a movement estimation method of cyclists by combining information from roadside sensors with location-dependent statistical information. First, we develop a method for analyzing the location-dependent statistical information of cyclists on a certain road from accumulated GNSS data using the Kalman smoother. Then, we develop a method for stochastically predicting the movement of cyclists even outside the observation range of a roadside sensor by using the concept of “virtual observation” based on location-dependent statistical information. To evaluate the proposed method, we conduct an experiment to accumulate GNSS data from cyclists using smartphones. As a result of comparison with a conventional method, we confirm that our proposed method can reduce the uncertainty of the estimated position; further, the reduction in the uncertainty will contribute to traffic safety by future advanced driver assistance systems.