Research on Performance Limitations of Visual-based Perception System for Autonomous Vehicle under Severe Weather Conditions

Visual-based perception systems are widely used in autonomous vehicles (AVs). In severe weather conditions, hazardous events of AVs may be induced by the performance limitations of perception system. We propose a staged analyzing method to quantitatively evaluate the performance limitations of visual-based perception system under severe weather conditions and explore the influence mechanism. In our method, the working process of visual-based perception systems is divided into two stages of image obtaining by camera and target recognition by recognition algorithm. Firstly, in image obtaining stage, the quality of images obtained in scenarios with different weather types and intensity is evaluated using monofactor analysis method. The relationship between different weather and metrics of image quality is analyzed. Secondly, in target recognition stage, metrics values of image quality and recognition results are fitted with (weighted) multiple linear regression model, and a regression model representing the influence relationship is acquired. Finally, the importance of indicators in image quality metrics is verified with BP neural network, and the performance of the regression model is analyzed with the results acquired in two example scenarios. With the obtained monofactor analysis results and the regression model, the influence mechanisms of high luminance and fog conditions are analyzed and compared, which shows the effectiveness of the method in performance limitation and its influence mechanism analysis.