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Xie Fei, Huang Haiyang, Zhang Jian, Wang Yu, Jiang Hongwei, Zhang Sutie
China FAW Group Co ., Ltd.
Abstract: Intelligence is an inevitable trend of automobile technology and industry development, and its development pace is also accelerating.Autonomous driving has also become one of the research hotspots in the field of intelligent vehicles.Although there are many researches on autonomous driving, most of them are based on relatively simple scenarios.However, with the gra dual expansion of the application scenario of autonomous driving, many scenarios present dynamic and complex characteristics, which makes the traditional path planning algorithm difficult to adapt.Therefore, the study of autonomous driving algorithm in dynamic and complex scenarios becomes the key to improve the adaptability of autonomous driving scenarios and promote the wide application of automatic driving.This paper proposes a dynamic obstacle tracking and predicttion algorithm based on extended Kalman filter to reduce dynamic obstacle tracking The tracking error caused by lag and its influence on trajectory planning.At the same time, a predictive descryiption method of dynamic scenario is proposed.Based on approximate grid decomposition, hierarchical cost map updating method is adopted and the prediction cost layer of dynamic obstacles is integrated to complete the predictive description of dynamic scenario.
Key words: dynamic obstacle tracking, predictive scenario description, hierarchical cost map