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Safety in autonomous vehicles is a challenging task because it depends on many factors such as weather conditions, sensors, the complexity of the surrounding environment and many more. These factors are unpredictable and hard to capture in real life. Automated vehicle systems depend on sensors such as LiDAR, radar and cameras to enable them to reach safely to the target destination. In this study, we show how the automated vehicle system that utilizes a radar and a camera as an input to the Pedestrian Protection System (PPS) is influenced by uncertain weather conditions and sensor failure. Under these conditions, we investigate surrogate safety measures such as Pedestrian Classification Time-to-Collision (PCT), and Post encroachment (PET). This study uses a physics-based simulation software called Prescan as well as MATLAB and Simulink in order to demonstrate practical test scenarios for surrogate safety analysis of vulnerable road users (VRU)-vehicle conflicts at urban roads. Different scenarios are built such as a pedestrian walking and running in front of the automated vehicle from the nearside. In addition to that, uncertain weather conditions and sensor failure are modelled and analysed. The results showed a high impact of weather conditions and sensor failure to the safety measures during traffic conflict. The outcomes reveal that the physics based safety model can mimic the real world scenarios and can support safety analysis in an accurate and cost-effective way.
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