Autonomous Vehicles MCQ
Perceive lanes, traffic, and obstacles—often fusing camera, LiDAR, and radar for robust autonomy.
ego vehicle
Pose
Lanes
Boundaries
Vulnerable
Ped / bike
Fusion
Multi-sensor
Vision in self-driving stacks
Autonomous systems use cameras for rich semantics (lanes, signs, color) and often fuse LiDAR/radar for range and weather robustness. Semantic segmentation labels drivable space; detectors track vehicles and pedestrians; HD maps and odometry integrate over time. Redundancy and validation matter as much as model accuracy.
Functional safety
Production stacks duplicate sensing modalities and monitor perception health—not only raw mAP.
Key ideas
Lane detection
Polynomial fits, segmentation masks, or row-wise classifiers on road.
Critical objects
Vehicles, pedestrians, cyclists—often tracked over time.
Segmentation
Freespace vs obstacles; curb and road boundary cues.
Fusion
Project LiDAR into camera; late or early fusion strategies.
Perception loop
capture → calibrate → detect/segment → track → planner