Driving rule extraction based on cognitive behavior analysis
| Autor: | Zhao Yucheng, Ming Yao, Guo-dong Hua, Ning Zhu, Yingfeng Cai, Long Chen, Jun Liang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
050210 logistics & transportation
Correctness Artificial neural network Computer science business.industry Interface (computing) 05 social sciences Metals and Alloys General Engineering Process (computing) Cognition 02 engineering and technology Traffic flow Machine learning computer.software_genre High fidelity 0502 economics and business 0202 electrical engineering electronic engineering information engineering Learning theory 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | Journal of Central South University. 27:164-179 |
| ISSN: | 2227-5223 2095-2899 |
| DOI: | 10.1007/s11771-020-4286-1 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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