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.