Fusion of telemetric and visual data from traffic scenes helps exploit synergies between different on-board sensors, which monitor the environment around the ego-vehicle. This paper outlines our approach to sensor data fusion, detection and tracking of objects in a dynamic environment. The approach uses a Bayesian Occupancy Filter to obtain a spatio-temporal grid representation of the traffic scene.We have implemented the approach on our experimental platform on a Lexus car. The data is obtained in traffic scenes typical of urban driving, with multiple road participants. The data fusion results in a model of the dynamic environment of the ego-vehicle. The model serves for the subsequent analysis and interpretation of the traffic scene to enable collision risk estimation for improving the safety of driving. © 2011 IEEE.
CITATION STYLE
Paromtchik, I. E., Perrollaz, M., & Laugier, C. (2011). Fusion of telemetric and visual data from road scenes with a lexus experimental platform. In IEEE Intelligent Vehicles Symposium, Proceedings (pp. 746–751). https://doi.org/10.1109/IVS.2011.5940571
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