Machine-learning-based hybrid recognition approach for longitudinal driving behavior in noisy environment. (September 2022)

• Record Type: Journal Article
• Authors: Sun, Haochen; Fu, Zhumu; Tao, Fazhan; Dong, Yongsheng; Ji, Baofeng
• Is Part Of: Engineering applications of artificial intelligence. Volume 114(2022)
• Publication Date: 2022-09
• Subjects: Driving behavior -- Clustering method -- Online recognition -- Support vector machine -- Artificial neural network; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.104990
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Driving behavior recognition has attracted wide attention as it can act as an important reference input of many vehicle intelligent control systems. In this paper, a real-time recognition of driver's longitudinal driving behavior is investigated by proposing a hybrid adaptive pattern recognition method. Firstly, a framework of integrated behavior recognition model is established consisting of two sub models to cluster and label the sample data, respectively. Secondly, a new fast high-stability clustering method is proposed to solve the problem of clustering samples with non-negligible noise. And the test results show the clustering process can be completed within a short time lower than 0.1 s with different number of cluster centers. Then, support vector machine and artificial neural network are employed and trained to construct a heuristic self-labeling approach to label the clustered sample automatically in real time with high accuracy (92.9%) under an experimental driving cycle generated from our real-vehicle test bench. Subsequently, the two established modules are integrated and offline trained by 96 thousand historical data, and employed to a 400-second-long online application under a smoothly-varying driving condition, and three further applications under extreme driving cycles with about 1000 s, respectively. Simulation results show that the proposed integrated model is capable of eliminating the interference of noise, and has a relatively stable performance of recognition for different driving cycles with different driving behaviors (92.7% of overall performance for commonly cycles, and greater than 91.2% for extreme cycles), enhancing the capacity for online application.