✓ Accepted Answer
Self-driving cars use a combination of sensors, AI, and real-time decision-making. Cameras, LiDAR (laser-based distance sensing), radar, and ultrasonic sensors continuously map the car's environment in 3D. This sensor fusion creates a detailed model of everything around the vehicle.
The AI processes this sensor data to identify objects — other vehicles, pedestrians, cyclists, road signs, lane markings — classify them, and predict their movements. Computer vision models trained on millions of hours of driving footage do the recognition.
Decision-making is the hardest part. The system must simultaneously plan a route, manage speed, navigate traffic rules, predict the behaviour of other road users, and handle unexpected situations. This happens in milliseconds.
There are 6 levels of automation from 0 (no automation) to 5 (full automation). Most current production vehicles are at level 2 (adaptive cruise control + lane keeping). Tesla's Autopilot is arguably level 2-3. Waymo's robotaxi service in parts of the US is the only commercial level 4 deployment.
Full level 5 (drives anywhere in any conditions without human intervention) remains unsolved. Edge cases — unusual situations not well represented in training data — remain the fundamental challenge.
by leilaalhussain
· 73 upvotes