Risk assessment is crucial for quantifying driving environment risks and reducing the Safety of the Intended Functionality (SOTIF) uncertainty in Autonomous Vehicles (AVs). Traditional methods, however, often concentrate on single-scenario metrics and are insufficient in capturing the complexities of multi-participant and multi-directional interactions. This letter introduces a risk assessment model capable of evaluating risks across diverse traffic participants, multiple directions, and complex multi-risk scenarios. Our model leverages state information from both the ego vehicle and surrounding traffic to compute a comprehensive risk probability distribution and environmental impact assessment. It achieves more precise risk quantification through an iterative process that calculates cumulative environmental costs and derives scalar risk values via sampling and summation within a map grid. Rigorously validated on extensive real-world datasets (inD, highD, rounD), our model demonstrates superior accuracy and scalability across diverse driving scenarios compared to traditional metrics like TTC, THW, RSS, and other field models, offering advantages in computational efficiency and parameter adjustability. The results showed that our model not only accurately quantifies environmental risks but also significantly enhances the safety and reliability of AVs, especially in high-risk, long-tail cases, making it a robust tool for the development and deployment of safer autonomous driving systems.