Perception Technology for Autonomous Heavy Trucks: Methods, Challenges, and Production Considerations

Automatic driving relies on perception as the vehicle's "eyes" and "ears"; for heavy‑truck scenarios, perception must handle high speeds, long detection ranges, and stringent safety requirements.

Lane‑line detection starts with traditional edge detection and Hough Transform, but these methods struggle with varying lighting, lane damage, and occlusions. Deep‑learning approaches using encoder‑decoder segmentation networks, embedding branches for instance separation, and Spatial CNNs improve accuracy and robustness.

Obstacle detection requires object detection rather than simple segmentation. Established methods such as Faster RCNN, YOLO (v2/v3), and SSD are employed, with anchor‑based and anchor‑free strategies (e.g., CenterNet) to handle large and small objects, sample imbalance, and irregular shapes.

LiDAR detection complements camera perception. Techniques like PointNet, PointNet++, and VoxelNet convert point clouds into voxels for efficient 3D object detection and ground‑plane estimation, which also aids lane‑line height and depth calculations.

Challenges specific to heavy‑truck autonomous driving include the need for higher detection speed, longer sensing distance, and uncompromising safety, as well as domain‑transfer issues when models trained on Chinese highways are applied to different road environments.

Production‑scale challenges cover cost constraints (low‑cost sensors and compute), performance optimization (hardware acceleration, parallelism, model compression, entropy‑based quantization), and reliability (sensor redundancy, multi‑sensor fusion, robust software stacks). Multi‑frame data aggregation and careful loss design (e.g., focal loss) are used to improve stability under adverse weather and lighting conditions.

The presentation concludes with a call for continued research and collaboration to address these technical hurdles and advance the commercialization of autonomous heavy‑truck technology.