Title :
Standard platform for sensor fusion on advanced driver assistance system using Bayesian Network
Author :
Kawasaki, Naoki ; Kiencke, Uwe
Author_Institution :
Denso Corp., Aichi, Japan
Abstract :
In this paper, a new architecture for sensor fusion for advanced driver assistant system (ADAS) is proposed. This architecture is based on Bayesian Network and plays the role of a platform for integrating various sensors such as Lidar, Radar and Vision sensors into sensor fusion systems. This architecture has the following 3 major advantages: (1) It makes structure and signal flow of the complicated fusion systems easy to understand (2) It increases the reusability of the sensor algorithm modules (3) It achieves easy integration of various sensors with different specifications. These advantages are confirmed by vehicle test.
Keywords :
belief networks; driver information systems; image sensors; optical radar; probability; sensor fusion; Bayesian network; LIDAR; advanced driver assistance system; probability; radar; sensor algorithm; sensor fusion systems; standard platform; vehicle test; vision sensors; Adaptive control; Bayesian methods; Databases; Laser radar; Millimeter wave radar; Programmable control; Sensor fusion; Sensor systems; System testing; Vehicles;
Conference_Titel :
Intelligent Vehicles Symposium, 2004 IEEE
Print_ISBN :
0-7803-8310-9
DOI :
10.1109/IVS.2004.1336390
