Scenario-Risk Net: A Comprehensive Approach to Understanding Mesoscopic Risk Scene in Autonomous Driving

Abstract

This paper introduces Scenario-Risk Net, a novel approach to integrate risk assessment into the autonomous driving system segmentation process. The method incorporates an attention-based layer into an existing segmentation network, enabling the classification of driving scenes based on their risk levels. The experimental results show significant improvements in segmentation accuracy, risk classification, and situational awareness. Key contributions include an enhanced model architecture that improves Intersection over Union (IoU) scores, a detailed dataset with comprehensive annotations, and robust graph construction that effectively models real-world relationships. The results demonstrate the model’s precision in assessing risk levels and focusing on critical elements, while also emphasizing the system’s ability to refine segmentation results accurately. The research highlights the integration of risk assessment as a means to enhance the system’s ability to navigate complex environments and respond to varying levels of risk. This work lays the foundation for future advancements in autonomous driving technology, offering a path to safer and more efficient transportation solutions. Future research directions include further refinement of the dataset, optimization of the system, and extensive real-world testing to validate and improve the approach.

Keywords

• Autonomous driving
• graph neural network
• mesoscopic risk
• scene understanding
• segmentation