Forward-Facing Sensors: Cameras, Radar, and Lidar
Each forward sensor has distinct physics and failure modes. Cameras provide high-resolution classification (lane lines, signs, pedestrians) but degrade in low light, glare, fog, and with a dirty or obstructed lens; they output 2D images requiring processing to infer depth. Radar uses radio waves, measures range and relative speed directly via Doppler, works through rain and darkness, but has lower angular resolution and can misread stationary objects. Lidar uses laser pulses for precise 3D point clouds, strong at range and object shape, but is costlier and affected by heavy precipitation or dirt.
Candidates must know mounting locations (camera typically behind the windshield near the mirror; radar behind the grille or bumper; lidar on roof or corners), the implications of that placement for calibration and repair, and the concept of sensor fusion-combining complementary sensors to overcome individual weaknesses. Understand field of view, range, and that radar's speed measurement is a key reason it anchors ACC.
Traps: assuming radar sees color or reads signs (it cannot); assuming camera alone reliably measures closing speed at range; assuming lidar is immune to weather. Another trap is the "blocked sensor" condition-mud, snow, or a sticker over the camera causes a feature to disable with a specific message.
Rule of thumb: match the task to the physics-classification to camera, range/velocity to radar, precise 3D geometry to lidar. When a question describes a limitation, ask which sensor's physics explains it.