Abstract
An automatic parking system is an essential part of autonomous driving, and it starts by recognizing vacant parking spaces. This paper proposes a method that can recognize various types of parking slot markings in a variety of lighting conditions including daytime, nighttime, and underground. The proposed method can readily be commercialized since it uses only those sensors already mounted on off-the-shelf vehicles: an around-view monitor (AVM) system, ultrasonic sensors, and in-vehicle motion sensors. This method first detects separating lines by extracting parallel line pairs from AVM images. Parking slot candidates are generated by pairing separating lines based on the geometric constraints of the parking slot. These candidates are confirmed by recognizing their entrance positions using line and corner features and classifying their occupancies using ultrasonic sensors. For more reliable recognition, this method uses the separating lines and parking slots not only found in the current image but also found in previous images by tracking their positions using the in-vehicle motion-sensor-based vehicle odometry. The proposed method was quantitatively evaluated using a dataset obtained during the day, night, and underground, and it outperformed previous methods by showing a 95.24% recall and a 97.64% precision.
Highlights
As the demand for autonomous driving has rapidly increased, automatic parking systems have been actively researched [1,2]
To recognize various types of parking slot markings with open and close entrances, this paper proposes an approach that generates parking slot candidates based on separating lines and confirms their entrances using both line and corner features
To achieve robustness in severe lighting conditions at night and underground, this paper proposes an approach that tracks separating lines and parking slots using in-vehicle motion sensor-based odometry, whose accuracy is independent of lighting conditions
Summary
As the demand for autonomous driving has rapidly increased, automatic parking systems have been actively researched [1,2]. Regarding the abovementioned conditions of the slot-marking-based approach, the previous methods can briefly be summarized as follows: The first method to recognize various types of parking slot markings was proposed in [3] This method could not be fully automated because a driver has to manually designate the position of the parking slot’s entrance. In order to overcome the limitations of these previous methods, this paper proposes a method that can operate in a fully automatic manner, recognize various types of parking slot markings, and achieve robustness against a variety of lighting conditions. To recognize various types of parking slot markings with open and close entrances, this paper proposes an approach that generates parking slot candidates based on separating lines and (2). To achieve robustness in severe lighting conditions at night and underground, this paper proposes an approach that tracks separating lines and parking slots using in-vehicle motion sensor-based odometry, whose accuracy is independent of lighting conditions.
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