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Real-time parking occupancy information is valuable for guiding drivers'searching for parking spaces. Recently many parking detection systems usingrange-based on-vehicle sensors are invented, but they disregard the practicaldifficulty of obtaining access to raw sensory data which are required for anyfeature-based algorithm. In this paper, we focus on a system using short-rangeradars (SRR) embedded in Advanced Driver Assistance System (ADAS) to collectoccupancy information, and broadcast it through a connected vehicle network.The challenge that the data transmitted through ADAS unit has been encoded tosparse points is overcome by a statistical method instead of featureextractions. We propose a two-step classification algorithm combiningMean-Shift clustering and Support Vector Machine to analyze SRR-GPS data, andevaluate it through field experiments. The results show that the average Type Ierror 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 canprovide a comprehensive method to elevate automotive sensors for the parkingdetection function.

A Statistical Method for Parking Spaces Occupancy Detection via Automotive Radars