Multiple Sensor Fusion and Classification for Moving Object Detection and Tracking

被引:264
作者
Chavez-Garcia, Ricardo Omar [1 ]
Aycard, Olivier [1 ]
机构
[1] Univ Grenoble 1, Informat Lab Grenoble, F-38000 Grenoble, France
关键词
Intelligent vehicles; sensor fusion; classification algorithms; vehicle detection; vehicle safety;
D O I
10.1109/TITS.2015.2479925
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The accurate detection and classification of moving objects is a critical aspect of advanced driver assistance systems. We believe that by including the object classification from multiple sensor detections as a key component of the object's representation and the perception process, we can improve the perceived model of the environment. First, we define a composite object representation to include class information in the core object's description. Second, we propose a complete perception fusion architecture based on the evidential framework to solve the detection and tracking of moving objects problem by integrating the composite representation and uncertainty management. Finally, we integrate our fusion approach in a real-time application inside a vehicle demonstrator from the interactIVe IP European project, which includes three main sensors: radar, lidar, and camera. We test our fusion approach using real data from different driving scenarios and focusing on four objects of interest: pedestrian, bike, car, and truck.
引用
收藏
页码:525 / 534
页数:10
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