Title :
Understanding surrounding vehicles in urban traffic scenarios based on a low-cost lane graph
Author :
Chunzhao Guo ; Kidono, Kiyosumi ; Kojima, Yoshiko
Author_Institution :
Toyota Central R&D Labs., Inc., Nagakute, Japan
fDate :
June 28 2015-July 1 2015
Abstract :
Understanding the surrounding vehicles with respect to the road lane context and the host vehicle is crucial for the success of autonomous driving and advanced driver assistance systems (ADAS) used in daily urban traffic. This paper proposed a vision-based approach to locate the surrounding vehicles into the corresponding driving lanes as well as classify them with respect to the host vehicle´s driving. In particular, the driving corridor of the host vehicle is generated by fusing multiple sources of data and the leader vehicle is determined, if available, to provide the real-time and validated information, such as trajectory and velocity, for the control purposes of the host vehicle. The potential applications of such information range from extending the functionality of the existing ADAS systems, e.g., vision-based platooning, stop-and-go traffic jam assist, etc., to autonomous driving in terms of high-level decision making and path planning. Furthermore, instead of the expensive high-precision detailed map, only a simple lane graph, constructed automatically by using conventional low-cost sensors, is used in this work to provide the driving lane information. Experimental results in various typical but challenging urban traffic scenes have substantiated the effectiveness of the proposed system.
Keywords :
computer vision; driver information systems; edge detection; graph theory; road traffic; ADAS systems; advanced driver assistance systems; autonomous driving; daily urban traffic; driving corridor; driving lane information; high-level decision making; host vehicle; leader vehicle; low-cost road lane graph; low-cost sensors; multiple data source fusion; path planning; stop-and-go traffic jam assist; surrounding vehicle location; trajectory information; urban traffic scenarios; urban traffic scenes; velocity information; vision-based approach; vision-based platooning; Global Positioning System; Image segmentation; Roads; Sensors; Trajectory; Vehicles;
Conference_Titel :
Intelligent Vehicles Symposium (IV), 2015 IEEE
Conference_Location :
Seoul
DOI :
10.1109/IVS.2015.7225736