Occlusion Aware Risk Assessment

Occlusion-aware risk assessment focuses on improving safety in scenarios where objects, particularly vulnerable road users, are hidden from view by obstacles. Current research emphasizes developing algorithms and models, including those based on transformers and convolutional neural networks, to quantify this risk using techniques like trajectory prediction and reachability analysis, often incorporating data from multiple vehicle sensors (collective perception). This work is crucial for enhancing autonomous vehicle navigation and driver-assistance systems, aiming to reduce accidents and improve overall road safety by mitigating the dangers of unseen hazards.