Optimizing Bayesian networks for recognition of driving maneuvers to meet the automotive requirements

G. Weidl, Anders L. Madsen, Dietmar Kasper, G. Breuel
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Abstract

An Object Oriented Bayesian Network for recognition of maneuver in highway traffic has demonstrated an acceptably high recognition performance on a prototype car with a Linux PC having an i7 processor. This paper is focusing on keeping the high recognition performance of the original OOBN, while evaluating alternative modelling techniques and their impact on the memory and time requirements of an ECU-processor for automotive applications. New challenges are faced, when the prediction horizon is to be further extended.
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优化贝叶斯网络识别驾驶动作以满足汽车需求
一个面向对象的贝叶斯网络在高速公路交通机动识别上已经证明了具有i7处理器的Linux PC的原型车具有可接受的高识别性能。本文的重点是保持原始OOBN的高识别性能,同时评估替代建模技术及其对汽车应用中ecu处理器的内存和时间要求的影响。在预测范围进一步扩大的同时,也面临着新的挑战。
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