Editing Autonomous Vehicles and Self-Driving AI (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
An autonomous vehicle system is a tightly coupled pipeline with four major subsystems:

'''1. Perception''': Raw sensor data → structured understanding of the scene.
Multiple sensors fuse data: cameras provide color and texture; LIDAR provides precise 3D geometry; RADAR provides velocity and works in fog/rain. Sensor fusion combines these into a unified world model. Deep learning (CNNs, ViTs, PointNets) detects vehicles, pedestrians, cyclists, traffic lights, and lane markings.

'''2. Prediction''': Other agents will move — where? Neural networks predict the probability distribution over future trajectories of all detected agents. This is hard because behavior is multi-modal (a pedestrian might step left or right), socially interactive (they react to each other), and intent-dependent (are they going to cross or waiting?).

'''3. Planning''': Given the current world state and predicted agent trajectories, find a safe, legal, and comfortable path for the ego vehicle. This involves:
* Route planning (global): which roads to take from A to B
* Behavior planning: when to merge, yield, proceed at an intersection
* Motion planning (local): the specific trajectory (position, velocity, acceleration over time)

'''4. Control''': Convert the planned trajectory into actuator commands. Model Predictive Control (MPC) is common — it continuously solves a short-horizon optimization that tracks the desired trajectory while respecting vehicle dynamics constraints.

The challenge: '''the long tail of edge cases'''. A system can handle 99.9% of driving situations correctly, but the remaining 0.1% is what kills people. AVs must handle construction zones, emergency vehicles, unusual signage, debris, and countless other rare events — while maintaining exact system safety.
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