Wide human-like neural network incorporating driving styles for human-like driving intention analysis

Abstract

Enhancing the synergy between autonomous and human-driven vehicles at the societal level requires understanding drivers’ behaviors and cognitive patterns, as well as conducting human-like driving intention analysis. To achieve this goal, this study designs a novel framework for analyzing human-like driving intention. Firstly, a spectral clustering method is employed to characterize driving styles. Secondly, a misclassification cost matrix is tailored to different driving needs. Finally, inspired by the complex neural networks found in the human brain, we develop a specialized lightweight neural network, termed the Width Human-like Neural Network (WNN), aimed at realizing personalized cognition and facilitating human-like decision-making in driving intention. Experimental studies and validation based on natural driving trajectory data from Kunming, China, demonstrate that the method accurately infers internal implicit driving intention from external explicit and observable driving behaviors, achieving a prediction accuracy of 99.8%. This framework strategically allocates limited computational resources to critical areas for autonomous vehicles and exemplifies best practices for improving neural network performance in driving intention analysis tasks.