Real-time parking occupancy information is valuable for guiding drivers' searching for parking spaces. Recently many parking detection systems using range-based on-vehicle sensors are invented, but they disregard the practical difficulty of obtaining access to raw sensory data which are required for any feature-based algorithm. In this paper, we focus on a system using short-range radars (SRR) embedded in Advanced Driver Assistance System (ADAS) to collect occupancy information, and broadcast it through a connected vehicle network. The challenge that the data transmitted through ADAS unit has been encoded to sparse points is overcome by a statistical method instead of feature extractions. We propose a two-step classification algorithm combining Mean-Shift clustering and Support Vector Machine to analyze SRR-GPS data, and evaluate it through field experiments. The results show that the average Type I error rate for off-street parking is 15.23% and for on-street parking is 32.62%. In both cased the Type II error rates are less than 20%. Bayesian updating can recursively improve the mapping results. This paper can provide a comprehensive method to elevate automotive sensors for the parking detection function.
@article{arxiv.1607.06708,
title = {A Statistical Method for Parking Spaces Occupancy Detection via Automotive Radars},
author = {Qi Luo and Romesh Saigal and Robert Hampshire and Xinyi Wu},
journal= {arXiv preprint arXiv:1607.06708},
year = {2016}
}