Single Variable-Constrained NDT Matching in Traffic Data Collection Using a Laser-Based Detector

Abstract

As indispensable components of intelligent transportation systems, traffic detection and surveillance technologies deliver speed monitoring, traffic counting, and vehicle identification and classification. This paper proposes a normal distribution transform (NDT) algorithm to improve the speed accuracy and robustness of a laser-based detector. This method can deliver more accurate estimation of vehicle speed, enabling computation of the parameters of length and height. The results of simulation with different detector update rates suggest that the average estimation errors of vehicle parameters can be reduced using the NDT matching method, especially for the low detector update rate. The study also implemented a series of field experiments using the proposed detector prototype to verify the detector's measurements of vehicle parameters. The proposed method is a promising way in which to improve the laser-based traffic detector. In simulation test, initial experiments show that the accuracy of speed estimation can reach 95%, given the update rate of 1000 Hz for detector, the average length error can be reduced by approximately 60%. Even for speeding vehicles traveling at 150 km/h, the estimated speed error is limited to 10 km/h. In field test, for a vehicle at the speed of 80km/h, the estimation errors are within the threshold of the maximum errors of simulation, that is, 32 cm for length error and 5.71 km/h for speed error results.