Driving Behavior Safety Assessment in Edge Computing Using Multitask Discriminative TS Fuzzy Model

Gu, Yi ORCID: https://orcid.org/0000-0001-7962-9466, Chen, Zhiyu, Wang, Lu ORCID: https://orcid.org/0009-0001-0453-5446, Shen, Jinsong, Bashir, Ali Kashif ORCID: https://orcid.org/0000-0003-2601-9327 and Wang, Wei (2024) Driving Behavior Safety Assessment in Edge Computing Using Multitask Discriminative TS Fuzzy Model. IEEE Transactions on Consumer Electronics. ISSN 0098-3063

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Abstract

Driving behavior safety analysis in edge computing involves the real-time collection and processing of vehicle driving data. Fuzzy models have advantages in analyzing driving behavior due to their excellent ability to handle uncertain information. This study develops a multitask discriminative Takagi-Sugeno fuzzy model (MD-TS-FM) for driving behavior safety assessment. To comprehensively analyze the correlation and differences in multitask driving behaviors, the designed consequent part consists of two parts: task-shared part reflects the shared intrinsic structure information that are consistent across different driving tasks, and task-specific part reflects distinct characteristics and variations specific to each task, allowing the model to address the unique aspects of different driving behaviors. Accordingly, the task-shared consequent part is characterized by low-rank property, which mines global structural information within the fuzzy space; whereas the task-specific consequent part exhibits sparsity, which removes non-discriminative and irrelevant information. This sparsity ensures that the model focuses on the most critical features for each specific task. Furthermore, a discriminative diversity term is introduced to enhance the diversity between tasks, which explores the consistent information of task-shared consequents while reducing the overlap of task-specific consequents. Experimental results indicate that the MD-TS-FM model can be effectively applied to driving behavior safety assessment.