autonomous driving perception deep learning
**Autonomous Driving Perception: Multi-Sensor BEV Fusion — unified bird's-eye-view representation for detection and occupancy**
Autonomous driving perception fuses multi-sensor (camera, LiDAR, radar) data to detect objects (cars, pedestrians), lanes, and drivable space. Recent architectures transform heterogeneous sensor data into unified bird's-eye-view (BEV) representation, enabling efficient joint detection and planning.
**BEV Transformation from Multi-View Images**
BEV projection transforms 6+ cameras (360° surround view) from perspective to top-down representation. Challenge: depth estimation from monocular images (inherent ambiguity). BEVFormer (Zheng et al., 2022) uses transformer temporal fusion: current frame multi-view→BEV features, accumulates history BEV→temporal attention, detects 3D objects directly in BEV. BEVFusion (Liu et al., 2022) fuses LiDAR points + camera images via early feature fusion: both encode to voxel grid, fuse early, detect objects. BEVDepth adds monocular depth learning to improve BEV accuracy.
**LiDAR-Camera Fusion Strategies**
Early fusion: project LiDAR points to camera space, concatenate features. Late fusion: separate backbone networks (LiDAR voxel encoder, camera CNN), fuse intermediate features via concatenation/gating. Intermediate fusion: project LiDAR features to BEV, fuse with camera-extracted BEV features. Calibration precision (camera-LiDAR alignment) is critical—misalignment introduces artifacts.
**3D Object Detection**
VoxelNet voxelizes point clouds, applies 3D convolutions, outputs 3D bounding box predictions. PointNet++ processes point clouds via hierarchical neighborhood aggregation. CenterPoint (Ding et al., 2021) detects object centers directly in BEV, regressing dimensions, orientation, velocity. Transformer variants (DETR3D) leverage attention for set prediction of bounding boxes. Evaluation: Average Precision (AP) at IOU=0.7, multi-category (car, pedestrian, cyclist).
**Occupancy Networks and 4D Perception**
Tesla FSD (Full Self-Driving) employs 4D occupancy networks: 3D spatial grid (x, y, z) + time axis, predicting occupancy probability per cell. Temporal prediction enables forecasting object motion (velocity estimation). Occupancy-based approach unifies detection (high-occupancy clusters), tracking (temporal coherence), and motion prediction. Interpretability: occupancy maps directly feed to motion planning (avoiding occupied regions).
**Sensor Suite Tradeoffs**
LiDAR: accurate 3D geometry, robust to lighting, expensive (~$1-20K), mechanical (reliability). Camera: rich semantics, cheap (~$100), vulnerable to occlusion, weather. Radar: robust weather, limited resolution. Production systems: balanced multi-sensor (e.g., 1x LiDAR + 6x cameras + 3x radar). Waymo: LiDAR + cameras; Tesla: camera-only (cost reduction, scalability); traditional OEMs: LiDAR + camera + radar.