Real-Time Vehicle Speed Gun With LiDAR–Camera Fusion

This paper presents a dual-sensor speed measurement system that fuses LiDAR-based distance ranging with depth-camera tracking through a Kalman filter. We quantitatively evaluate the separate contributions of per-modality filtering and dual-modal fusion via outdoor vehicle experiments encompassing acceleration, deceleration, and steady-state driving conditions. Per-sensor filtering reduces distance error and suppresses high-frequency measurement jitter, while sensor fusion further lowers the root-mean-square error (RMSE) and produces speed profiles that closely match ground-truth measurements. The proposed system operates in real time on a Raspberry Pi platform. Time-stamped CSV speed logs and synchronized, annotated video from real vehicle trials are provided to enable independent validation. The results demonstrate that complementary sensing combined with probabilistic sensor fusion offers a practical and deployable solution for accurate speed measurement under real-world conditions. The fused system achieves a speed root-mean-square error (RMSE) of 0.4993 mph and a distance RMSE of 0.4339 m, representing improvements of 55% and 56%, respectively, over the raw single-sensor baselines. The primary novelty of this research is the development of a three-stage processing architecture that systematically isolates and quantifies these performance gains achieved by per-modality Kalman filtering and subsequent probabilistic sensor fusion. Furthermore, this architecture is implemented and empirically validated on a resource-constrained Raspberry Pi platform under dynamic, real-world driving conditions.