  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.
