Research on Intelligent Vehicle Driving Behaviour Analysis and Driver State Evaluation Based on Emotion Recognition
Keywords:
Intelligent Driving, Autonomous Driving, Optimization, Classification, Subset Model, Spider SwarmAbstract
Intelligent driving, also known as autonomous driving or self-driving, refers to the technology and systems that enable vehicles to operate without direct human intervention. Autonomous driving or self-driving, presents several significant challenges that need to be addressed to ensure the safe and widespread adoption of this transformative technology. This paper presents a novel approach, Optimal Subset Spider Monkey Swarm Optimization (OsSMSO), for behavior analysis in intelligent driving with emotional intelligence (EI). The primary goal of OsSMSO is to identify the optimal subset of driving behaviors that can be effectively enhanced by integrating emotional intelligence into the decision-making process of intelligent vehicles. The OsSMSO algorithm with spider monkey swarm optimization as the underlying optimization technique, with each spider monkey representing a potential subset of driving behaviors influenced by emotional intelligence. Emotional intelligence models are integrated into the evaluation process to assess the impact of emotions such as stress, fatigue, happiness, and anger on driving behaviors. Through multiple runs of OsSMSO, the most effective combinations of driving behaviors are identified, considering factors like safety, efficiency, and comfort. The proposed approach is compared with traditional models such as Support Vector Machine (SVM) and Random Forest, and the results demonstrate its superiority in achieving higher classification accuracy, precision, recall, and F1-score. The findings highlight the significance of integrating emotional intelligence features in intelligent vehicle systems, providing valuable insights for designing emotionally-aware autonomous vehicles for safer and more enjoyable driving experiences. Further validation and experimentation on diverse datasets and driving scenarios will be essential to establish the generalizability and effectiveness of the OsSMSO algorithm.
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