A Statistical Method for Parking Spaces Occupancy Detection via Automotive Radars
This work addresses the practical challenge of obtaining raw sensory data for parking detection systems, offering a method that uses encoded data from ADAS radars to improve parking guidance for drivers, though it is incremental in applying existing techniques to a specific domain.
The paper tackles the problem of real-time parking occupancy detection using automotive radars by proposing a two-step classification algorithm combining Mean-Shift clustering and Support Vector Machine to analyze sparse sensor data, achieving average Type I error rates of 15.23% for off-street and 32.62% for on-street parking with Type II errors below 20%.
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.