AI Perception mainly lives in the Neural Network / Deep Learning rings but is orchestrated at the AI/ML level.
Object Detection (vehicles, pedestrians, cyclists, obstacles)
- Examples:
- Mercedes Active Brake Assist with pedestrian detection in millions of vehicles ADAS & Autonomous Vehicle International+1
- City-safety AEB in Volvo, etc., using camera/radar perception. ABI Research+1
Lane & Road-Geometry Recognition
- Example: Nissan ProPILOT Assist with Navi-Link – keeps the car centred in lane while following traffic, using camera-based lane detection + map data. Nissan+2Nissan+2
Sensor Fusion (camera + radar + lidar + maps)
- Example: Multi-sensor stacks in autonomous-driving systems such as Waymo, and sensor-fusion-based ADAS for object detection and distance estimation in production vehicles. Loughborough University Repository+3arXiv+3MDPI+3
Distance, Speed & Risk Assessment
- Use: Time-to-collision, risk scores used by ACC, AEB, collision-avoidance.
- Example: Radar+vision-based AEB and collision-warning systems in modern Mercedes, Toyota, Nissan, etc. ABI Research+2Toyota+2
ADAS Level 1–4 Perception Stack
- Example: Highway-assist systems (Tesla Autopilot, Nissan ProPILOT, GM Super Cruise, etc.) – all rely on a full perception stack of the above elements to provide ACC, LKA, lane-change assist, and limited self-driving. arXiv+3Comet+3concordvillenissan.com+3