CLIFE: Camera-LiDAR Fusion Framework for Edge-Deployable Roadside VRU Perception
2026-07-17 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors developed CLIFE, a system that helps cameras and LiDAR sensors work together to detect pedestrians and cyclists at intersections, even in tough conditions like bad weather or low light. Their system runs entirely on a small, local computer instead of relying on the cloud, making it faster and easier to deploy. They tested CLIFE at multiple intersections and found it improves detection range and reliability. The system also runs quickly enough to handle real-time traffic situations, which can help improve road safety.
roadside perceptionvulnerable road userscamera-LiDAR fusionedge computingonline calibrationlate fusiontrackingreal-time processingJetson AGX Thormulti-sensor fusion
Authors
Tam Bang, Hoang H. Nguyen, Lei Cheng, Lihao Guo, Siyang Cao, Hussam Abubakr, Tianya Zhang, Austin Harris, Mina Sartipi
Abstract
Reliable roadside perception of vulnerable road users (VRUs) remains challenging under occlusions, variable lighting, and diverse weather conditions, particularly under strict edge-computing and latency constraints. Existing multi-sensor fusion systems rely on cloud or server-grade infrastructure, creating a deployment gap at real-world intersections. We present CLIFE, an edge-native camera-LiDAR fusion framework that integrates targetless online calibration and lightweight late-fusion tracking entirely on a single embedded device, without cloud offloading. CLIFE adaptively refines camera-LiDAR alignment on demand and performs multi-sensor fusion and track association with O(N log N) per-frame cost. We deploy CLIFE across 12 signalized intersections in Chattanooga and conduct an in-depth evaluation at a representative intersection using synchronized camera-LiDAR data that spans diverse daytime, nighttime, and weather conditions. Our experiments demonstrate that the fusion architecture substantially enhances the perceptual range and robustness of the individual sensors under varied environmental and traffic conditions. The late-fusion core operates at 53.2 FPS on the Jetson AGX Thor, ensuring high throughput for real-time intersection-scale applications. By centering perception at the edge, CLIFE provides a deployable foundation for downstream safety applications, while reducing bandwidth and calibration overhead for agencies operating multi-intersection corridors.