daniel-bogdoll/phd
Anomaly Detection for Autonomous Driving
This project offers an anomaly detection solution tailored for autonomous driving systems. It takes in multimodal sensor data from a vehicle, such as camera images and LiDAR point clouds, along with vehicle actions, to predict future environment states. The output helps identify unusual or unexpected driving scenarios, which is critical for ensuring the safety and reliability of self-driving cars. This is intended for engineers and researchers developing and testing autonomous vehicles.
Use this if you are working on autonomous driving systems and need to detect anomalous situations to improve safety and decision-making.
Not ideal if your application requires real-world validated anomaly detection or if computational cost is a primary constraint without room for resource-intensive models.
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Last pushed
Jan 13, 2026
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