Multi-sensor Data Fusion in Automotive Applications
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Joined: Jun 2010
26-11-2010, 11:23 AM

Presented by:
Thomas Herpel
Christoph Lauer
Reinhard German
Johannes Salzberger
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.

Increasing road traffic safety and at the same time reducing the number of fatal car accidents is one of the most challenging future tasks for both car manufacturers and research institutions worldwide. Besides intelligent roadside infrastructures, advanced traffic routing and information services considerable effort is spend on enhancing the intelligence of individual vehicles within the traffic flow. Presently, sensor technologies well-known from other application areas like military or civil aviation are employed. Radar, laser, ultrasonic or video devices perceive information about the environment and possible threats around the vehicle either actively or passively. This significantly enhances the car’s ability to anticipate dangerous driving situations and to act early and effectively in order to avoid a collision or at least mitigate the accident severity by proactive activation of adequate protection means. The quality of context perception by a set of environment sensors is of utmost importance for the so-called Advanced Driver Assistance Systems (ADAS) which rely on the sensor data. Important sensor properties that influence the quality of the environment perception include range, field-of-view (FOV), weather robustness, power consumption or placement constraints. Single sensor systems often have undesired weaknesses that suggest the use of multi-sensor systems. However, the sensor signal processing and fusion of sensor data from multiple devices is a sophisticated process including important design decisions regarding system performance and dependability. Various algorithms have been investigated for tasks of clustering measurement points, association of data with real world objects and filtering of sensor information . Depending on the system's fusion paradigm the data integration takes place at a specific level of data abstraction. In a low-level data fusion near-raw data from various devices is combined at a very early stage of signal processing and the algorithms are applied to the conglomerate of measurement points. A high-level data fusion strategy pre-processes the data of the single sensors individually - i.e. each sensor is capable of dedicated clustering, association and filtering - and fuses the edited information, often represented as lists of detected objects. Both approaches are expected to have certain advantages and disadvantages in terms of entropy of information, computational complexity and adaptivity. In this paper, we use the concept of discrete event simulation for the analysis of a model consisting of various multi-sensor systems, sensor phenomena like reflection of radar or laser beams, road traffic scenarios and sensor data fusion strategies. The simulation results allow for a comparison on what data fusion paradigm, low-level or high-level, performs best in which scenario and is preferable with respect to maximum detection performance, robustness and reliability of proactive ADAS applications.

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