Anomaly detection in autonomous vehicles focuses on identifying unexpected events or data patterns that deviate from normal operation. This is critical because these systems rely on sensors, software, and real-time decision-making to operate safely. By flagging anomalies, the vehicle can trigger fail-safes, alert remote operators, or adjust its behavior to mitigate risks. For example, if a sensor suddenly reports implausible values or a component behaves erratically, anomaly detection algorithms can detect these issues before they lead to unsafe outcomes. This process often involves comparing real-time data against predefined models of expected behavior or using machine learning to learn normal patterns over time.
One key application is monitoring sensor data for errors. Autonomous vehicles use cameras, LiDAR, radar, and ultrasonic sensors to perceive their environment. If a camera is obscured by dirt or a LiDAR sensor starts returning inconsistent distance measurements, anomaly detection can identify these issues. For instance, a sudden spike in LiDAR noise levels could indicate sensor degradation or environmental interference like heavy rain. Developers might implement statistical models (e.g., threshold-based checks) or unsupervised learning techniques (e.g., autoencoders) to detect such deviations. These systems might also cross-validate sensor inputs—like flagging a scenario where the camera detects a clear path but the radar senses an obstacle—to identify conflicting data that requires intervention.
Another critical area is detecting software or behavioral anomalies. Autonomous driving stacks include perception, planning, and control modules. If the planning module generates a trajectory that sharply deviates from safe driving patterns (e.g., sudden swerving without obstacles), anomaly detection can override it. For example, a recurrent neural network (RNN) trained on historical driving data might flag unusual steering angles or acceleration patterns. Similarly, monitoring the vehicle’s internal state—such as unexpected spikes in CPU usage or memory leaks—can prevent system failures. By integrating anomaly detection at multiple layers, developers create redundancies that enhance safety, ensuring the vehicle either recovers autonomously or transitions to a minimal-risk state, like pulling over safely.