Editing Autonomous Vehicles (section)

== <span style="color: #FFFFFF;">Creating</span> ==
Designing an autonomous vehicle perception-planning architecture:

'''1. Sensor suite design'''
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360° camera ring (6–8 cameras): full surround vision at different focal lengths
    + 3× LIDAR: long-range front (200m), two shorter-range side units
    + 5× RADAR: front long-range, four corners for blind-spot/cut-in detection
    + GPS/IMU: high-accuracy positioning fused with HD map
    + Redundant compute: two independent compute stacks (fail-safe)
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'''2. Perception architecture (modern BEV approach)'''
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Multi-camera images + LIDAR points
    ↓
[Camera backbone: ViT or ResNet → feature maps per camera]
    ↓
[BEV lifting: cross-attention maps image features → BEV grid (BEVFormer)]
    ↓
[LIDAR backbone: PointPillars or VoxelNet → BEV feature map]
    ↓
[Sensor fusion: concatenate camera-BEV + LIDAR-BEV feature maps]
    ↓
[Detection head: 3D bounding boxes + class + velocity]
    ↓
[Segmentation head: driveable area, lane markings]
    ↓
[Occupancy prediction: future occupancy grid]
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'''3. Safety architecture (redundancy and fallback)'''
* Functional safety per ISO 26262 (Automotive Safety Integrity Level D for steering/braking)
* Minimal Risk Condition (MRC): ability to safely pull over and stop if system failure detected
* Independent safety monitor: separate system that can override if primary system is compromised
* Watchdog timers: if any module stops responding within deadline, initiate MRC
* Formal verification of critical planning constraints (speed limits, collision avoidance)

[[Category:Artificial Intelligence]]
[[Category:Computer Vision]]
[[Category:Robotics]]
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