Advances in radar anomaly detection for automotive safety systems focus on enhanced algorithms and sensor technologies that improve the identification of unexpected objects and conditions on the road. Machine learning techniques are increasingly applied to analyze radar data, enabling effective differentiation between normal and anomalous signals. Enhanced processing power facilitates real-time analysis, while sensor fusion with modalities such as cameras and LiDAR boosts detection accuracy and reduces false positives. Challenges persist, including environmental factors affecting radar performance and the complexity of integrating data from multiple sources. Overall, these advancements significantly enhance the safety and reliability of autonomous and semi-autonomous vehicles. What are Advances in Radar Anomaly Detection for Automotive Safety Systems? Advances in radar anomaly detection for automotive safety systems include improved algorithms and sensor technologies. These advancements enhance the…