Real-time parking occupancy information is valuable for guiding drivers' searching for parking spaces. Recently many parking detection systems using on-vehicle range- based sensors are invented. However, they disregard the practical difficulty of obtaining access to raw sensory data which are required for feature-based algorithms. In this paper, we focus on transforming 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 raw data transmitted through ADAS unit has been encoded to sparse points is overcome by analyzing the cumulative data by statistical techniques instead of direct feature extractions. We propose a two-step classification algorithm combining Mean-Shift clustering and Support Vector Machine, 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 $20 \%$, and Bayesian updating can recursively improve the mapping results. This paper provides a comprehensive method to leverage on ADAS sensors for the parking detection function.


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    Title :

    A Statistical Method for Parking Spaces Occupancy Detection via Automotive Radars


    Contributors:
    Luo, Qi (author) / Saigal, Romesh (author) / Hampshire, Robert (author) / Wu, Xinyi (author)


    Publication date :

    2017-06-01


    Size :

    630785 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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