Multi-sensor Data Fusion in Automotive Applications

The application of environment sensor systems in modern - often called "intelligent" - cars is regarded as a promising instrument for increasing road traffic safety. Based on a context perception enabled by well-known technologies such as radar, laser or video, these cars are able to detect threats on the road, anticipate emerging dangerous driving situations and take proactive actions for collision avoidance. Besides the combination of sensors towards an automotive multi-sensor system, complex signal processing and sensor data fusion strategies are of remarkable importance for the availability and robustness of the overall system. In this paper, we consider data fusion approaches on near-raw sensor data (low-level) and on pre-processed measuring points (high-level). We model sensor phenomena, road traffic scenarios, data fusion paradigms and signal processing algorithms and investigate the impact of combining sensor data on different levels of abstraction on the performance of the multi-sensor system by means of discrete event simulation.