Anomaly detection

[Kishore-Yogaraj]

• Objective:
  To develop a system that can detect unexpected behavior or irregularities in sensor data, system performance, or driving environments to ensure safe and reliable autonomous driving.

Key Details:

Data Sources: Use sensor data from LIDAR, radar, cameras, and GPS. Anomalies can occur due to sensor malfunctions, unexpected obstacles, or environmental conditions (e.g., fog, rain).
Goal: Identify deviations from normal patterns, such as unexpected object detection, abnormal vehicle trajectories, or faulty sensor readings.

Potential Approach:
Use machine learning algorithms to learn normal patterns of behavior.
Implement real-time monitoring of sensor inputs and vehicle behavior.
Trigger alerts or safety protocols when anomalies are detected.
Expected Output: A system that flags anomalies and either adjusts the vehicle's behavior (slow down, stop, reroute) or informs the human operator.