Abstract:
For intelligent vehicles, multi-sensor fusion is of great importance to perceive traffic environment with high accuracy and robustness. In this paper, we propose two effe...Show MoreMetadata
Abstract:
For intelligent vehicles, multi-sensor fusion is of great importance to perceive traffic environment with high accuracy and robustness. In this paper, we propose two effective methods, i.e. spatio-temporal evidence generating and independent vision channel, to improve multi-sensor track-level fusion for vehicle environmental perception. The spatio-temporal evidence includes instantaneous evidence, tracking evidence and tracks matching evidence to improve existence fusion. Independent vision channel leverages the specific advantage of vision processing on object recognition to improve classification fusion. The proposed methods are evaluated by using the multi-sensor dataset collected from real traffic environment. Experimental results demonstrate that the proposed methods can significantly improve the multi-sensor track-level fusion in terms of both detection accuracy and classification accuracy.
Published in: 2018 IEEE Intelligent Vehicles Symposium (IV)
Date of Conference: 26-30 June 2018
Date Added to IEEE Xplore: 21 October 2018
ISBN Information:
Print on Demand(PoD) ISSN: 1931-0587