Gu Yanlei

GNSS/INS/On-board Camera Integration for Vehicle Self-Localization in Urban Canyon

This paper presents a precise vehicle self-localization system for autonomous driving. The developed system integrates multiple on-board passive sensors, Global Navigation Satellite System (GNSS), Inertial Navigation system (INS) and on-board monocular camera, in order to achieve lane-level localization performance in urban environment. GNSS based positioning technique suffers from the effects of multipath and Non-Line-Of-Sight (NLOS) propagation in urban canyon. The positioning error also affects the performance in the integrated GNSS/INS system. In the other side, the lane-marking on road surface provides the important visual source of information for driving. This paper proposes to detect the occupied lane of vehicle using on-board cameras. The lane detection result is integrated with GNSS/INS system in order to improve the positioning error. The experiment results demonstrate that the proposed method can provide 90% correctness with respect to the occupied lane.

A Probabilistic Model for the Estimation of Pedestrian Crossing Behavior at Signalized Intersections

Active safety systems which assess highly dynamic traffic situations including pedestrians are required with growing demands in autonomous driving and ADAS. In this paper, we focus on one of the most hazardous traffic situations: the possible collision between a pedestrian and a turning vehicle at intersections. This paper presents a probabilistic model of pedestrian behavior to signalized crosswalks. For this purpose, we take not only pedestrian physical states but also contextual information into account. We propose a model based on the Dynamic Bayesian Network (DBN) which integrates relations among the intersection context information and the pedestrian behavior in the same way as human. Afterwards, the model jointly estimates their states by the particle filter. Experimental evaluation using real traffic data shows that this model is able to recognize the pedestrian crossing decision in advance from the traffic signal and pedestrian position information.

Pedestrian Localization Service Using 3D Map and RAIM GNSS Satellite Selection Technique

The requirement of the accurate pedestrian position is increasing due to the booming pedestrian-to-vehicle (P2V) communication. Currently, global navigation satellite system (GNSS) receivers can provide accurate and reliable positioning service in open-field areas. However, its performance in city downtown is still affected by the multipath and none-line-of-sight (NLOS) receptions. This paper proposes an innovative positioning method using the 3D building maps and the receiver autonomous integrity monitoring (RAIM) satellite selection method to achieve satisfactory positioning performance in urban area for the pedestrian user. The 3D building model is used with a ray-tracing technique to simulate the line-of-sight (LOS) and NLOS signal travel distance, which is well-known as pseudorange, between the satellite and receiver. The proposed RAIM fault exclusion is able to compare the similarity between the raw pseudorange measurement and the simulated pseudorange. The measurement of the satellite will be excluded if the simulated and raw pseudorange is inconsistent. Because of the assumption of single reflection in the ray-tracing technique, the inconsistent case indicates it is a double or multiple reflected NLOS signal. According to the experiment result, the RAIM satellite selection technique can reduce about 8.4% of the large positioning solutions (points that estimated at the wrong side of the road) in the middle urban canyon environment.