An in-vehicle imaging sensor is proposed for achieving lateral control to avoid single vehicle roadway departure crashes. This countermeasure system utilizes a forward looking video camera to measure the curvature of the actual roadway and compare it to the curvature assumed by the driver of the vehicle in question. If the curvatures are sufficiently diverse, as determined by the countermeasure algorithm, an in-vehicle warning is issued to alert the driver. Since the sensor is expected to function under a variety of weather conditions, it is important to determine the overall performance envelope of the combined sensor image processing algorithm. Initial roadway imagery was acquired under favorable ambient conditions and subsequently transformed to specified levels of adverse weather by means of software originally developed for military sensor applications. The transformed imagery was utilized to determine the relationship between adverse weather, measured in visibility ranges, versus the ability of the sensor image processing algorithm to maintain lateral vehicle stability. It has been shown that the TDA/EOVAST simulation tool provides valuable information regarding the performance of the combined sensor/algorithm operating in the presence of adverse weather. The results of this effort have verified expectations that if visibility is sufficiently reduced, it will have a negative impact on sensor data processing performance. Furthermore, these experiments have quantified this effect and have shown it to be a nonlinear process.
Impact of adverse weather on sensors for vehicle collision avoidance systems
Wirkung von ungünstigem Wetter auf die Positionsmeßgenauigkeit bei der Auswertung von Fensehkamerabildern in intelligenten Fahrzeuglenkungssystemen
1995
6 Seiten, 2 Bilder, 1 Quelle
Conference paper
English
Impact of adverse weather on sensors for vehicle collision avoidance systems [2592-20]
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