Editing Autonomous Vehicles (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ AV Sensor Comparison
! Sensor !! 3D Geometry !! Velocity || Color/Texture !! Weather Robustness !! Range !! Cost
|-
| Camera || No (stereo: approximate) || No || Yes || Poor (fog, night) || Variable || Low
|-
| LIDAR || Yes (precise) || No || No || Moderate || 100–200m || High
|-
| RADAR || Approximate || Yes || No || Excellent || 200m+ || Medium
|-
| Ultrasonic || Limited || No || No || Good || <5m || Very low
|}

'''Critical failure modes:'''
* '''Long tail edge cases''' — The AV encounters a situation not represented in training data: a child running into the road from behind a parked car, a mattress falling off a truck. These "unknown unknowns" are the central safety challenge.
* '''Sensor degradation''' — Heavy rain, snow, or direct sunlight degrades camera and LIDAR inputs. RADAR is more robust but provides less detail. Sensor fusion must handle individual sensor failures gracefully.
* '''Prediction failure''' — The system correctly perceives an agent but incorrectly predicts its behavior. A jaywalker predicted to stay on the curb who steps into the road.
* '''Map dependency''' — HD map-dependent systems fail when roads have changed (new construction, temporary detours) since the map was created.
* '''Adversarial attacks''' — Stop signs with adversarial stickers can cause misclassification. Lane markings can be fooled with tape.
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