Active safety and CA (Collision Avoidance) is a growing field within the automotive industry. The aim of CA systems is to prevent or mitigate collisions by active interventions i.e. warning, braking and steering. For many reasons, such as driver acceptance of the system and legal requirement that the system itself must not cause hazards, the decision making is a crucial part of the system. The decision making algorithms rely on the fact that information about surrounding objects states is available. It is the task of the tracking system to provide this information. In automotive application radars, laser radars, and vision systems are used for detecting and tracking surrounding objects. In general a systems will use more than one tracking sensor. The sensor fusion algorithm involves synchronization in time and space by means of extended Kalman filtering. A method is presented for risk estimation on which to base the decision making. It is shown how one can form criterions for decision making in terms of probability of collision. This criterion handles the noisy sensor data and process noise (driver behavior) in a natural way, using modern tracking theory. The method is illustrated by simulation results as well as test result from a prototype vehicle. Simulations and tests are examples of a system which performs autonomous braking actuation at imminent collision. A sensor with a shorter detection range but a larger field of view might be more appropriate for collision mitigation purposes.


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    Title :

    A probabilistic approach to collision risk estimation for passenger vehicles


    Additional title:

    Risikoabschätzung der Kollision von Personenkraftwagen mittels Wahrscheinlichkeitsrechnung


    Contributors:


    Publication date :

    2002


    Size :

    6 Seiten, 13 Bilder, 5 Quellen


    Type of media :

    Conference paper


    Type of material :

    Storage medium


    Language :

    English




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