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Driving rule extraction based on cognitive behavior analysis

基于认知型行为分析的驾驶规则抽取

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Abstract

In order to make full use of the driver’s long-term driving experience in the process of perception, interaction and vehicle control of road traffic information, a driving behavior rule extraction algorithm based on artificial neural network interface (ANNI) and its integration is proposed. Firstly, based on the cognitive learning theory, the cognitive driving behavior model is established, and then the cognitive driving behavior is described and analyzed. Next, based on ANNI, the model and the rule extraction algorithm (ANNI-REA) are designed to explain not only the driving behavior but also the non-sequence. Rules have high fidelity and safety during driving without discretizing continuous input variables. The experimental results on the UCI standard data set and on the self-built driving behavior data set, show that the method is about 0.4% more accurate and about 10% less complex than the common C4.5-REA, Neuro-Rule and REFNE. Further, simulation experiments verify the correctness of the extracted driving rules and the effectiveness of the extraction based on cognitive driving behavior rules. In general, the several driving rules extracted fully reflect the execution mechanism of sequential activity of driving comprehensive cognition, which is of great significance for the traffic of mixed traffic flow under the network of vehicles and future research on unmanned driving.

摘要

为了充分利用驾驶员在道路交通信息感知、交互和车辆控制过程中的长期驾驶经验, 提出了一 种基于 ANNI 及其集成的驾驶行为规则抽取算法. 首先, 基于认知学习理论, 建立认知型驾驶行为模型, 并进行描述和分析. 然后, 基于 ANNI 的模型和规则抽取算法(ANNI-REA)来解释驾驶行为及离散性. 在不离散连续输入变量的情况下, 规则具有较高的保真度和安全性. UCI 标准数据集和自建驾驶行为数据集上的实验结果表明, 该方法比常见的 C4.5-REA、神经规则和 REFNE 方法准确度提高了约 0.4%, 简化了约 10%. 进一步地, 仿真实验验证了所提取的驾驶规则的正确性和基于认知驾驶行为规则提取的有效性. 总的来说, 抽取的多条驾驶规则充分体现了驾驶综合认知序列活动的执行机制, 这对于车联网下混合交通流的交通以及未来无人驾驶的研究具有重要意义.

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Authors and Affiliations

  1. Automotive Engineering Research Institute, Jiangsu University, Zhenjiang, 212013, China

    Yu-cheng Zhao  (赵宇橙), Jun Liang  (梁军), Long Chen  (陈龙) & Ying-feng Cai  (蔡英凤)

  2. School of Automotive and Transportation Engineering, Jiangsu University, Zhenjiang, 212013, China

    Ming Yao  (姚明)

  3. Jiangsu Zhixing Future Automobile Research Institute, Nanjing, 210000, China

    Guo-dong Hua  (华国栋)

  4. Department of Mechanical Engineering, Shizuoka University of Science and Technology, Shizuoka Bagu, 437-0032, Japan

    Ning Zhu  (朱宁)

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  1. Yu-cheng Zhao  (赵宇橙)
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  2. Jun Liang  (梁军)
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Corresponding author

Correspondence to Jun Liang  (梁军).

Additional information

Foundation item: Project(2017YFB0102503) supported by the National Key Research and Development Program of China; Projects(U1664258, 51875255, 61601203) supported by the National Natural Science Foundation of China; Projects(DZXX-048, 2018-TD-GDZB-022) supported by the Jiangsu Province’s Six Talent Peak, China; Project(18KJA580002) supported by Major Natural Science Research Project of Higher Learning in Jiangsu Province, China

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Zhao, Yc., Liang, J., Chen, L. et al. Driving rule extraction based on cognitive behavior analysis. J. Cent. South Univ. 27, 164–179 (2020). https://doi.org/10.1007/s11771-020-4286-1

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