道路交通行车安全预警算法研究
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TN349

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国家自然科学基金 (61701296)、上海工程技术大学研究生科研创新项目(17KY0202)资助


Research on early warning algorithm of driving safety about road traffic
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    摘要:

    为了提高道路交通安全,针对目前行车安全预警算法采用确定性参数导致的预警准确度不高的问题,提出了一种基于多层感知器神经网络的行车安全预警算法。该算法以人工神经网络为基础,将前后车的相对距离、相对速度、驾驶员风格类型、前车加速度、后车加速度以及后车的速度作为系统的输入,行车安全预警级别作为系统的输出。结合样本数据进行训练,得到行车安全预警级别的预测值,并与传统的碰撞时间算法和停车距离算法的两种预警算法进行对比。实验结果表明,多层感知器神经网络预警算法在预警的有效性与准确性方面明显优于传统预警算法。

    Abstract:

    In order to improve road traffic safety, an early warning algorithm based on Multi-Layer Perceptron Neural Network is proposed to solve the problem of low prediction accuracy caused by deterministic parameters in the current traffic safety early warning algorithm. The algorithm is based on artificial neural network (ANN). The relative distance, relative speed, driver′s driving style, the acceleration of preceding vehicle, the acceleration of following vehicle and the speed of following vehicle are used as the input of the system, and the warning level of traffic safety is the output of the system. The prediction value of traffic safety early warning level is obtained by training with sample data, and compared with the two early warning models of the traditional collision time algorithm and the stop distance algorithm. The experimental results show that the multilayer perceptron neural network early warning algorithm is superior to the traditional warning algorithm in the effectiveness and accuracy of early warning.

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陈计伟,史志才,刘瑾,陈珊珊.道路交通行车安全预警算法研究[J].电子测量技术,2019,42(5):6-10

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  • 在线发布日期: 2021-07-29
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