Parking Assistance System for Leaving Perpendicular Parking Lots: Experiments in Daytime\/Nighttime Conditions

Backing-out and heading-out maneuvers in perpendicular or angle parking lots are one of the most dangerous maneuvers, especially in cases where side parked cars block the driver view of the potential traffic flow. In this paper, a new vision-based Advanced Driver Assistance System (ADAS) is proposed...

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Bibliographic Details
Published in:IEEE intelligent transportation systems magazine 2014, Vol.6 (2), p.57-68
Main Authors: Fernández-Llorca, David, García-Daza, Iván, Martínez-Hellín, Agustín, Álvarez-Pardo, Sergio, Sotelo, Miguel Ángel
Format: Magazinearticle
Language:English
Subjects:
Applied sciences
Artificial intelligence
Automobiles
Automotive engineering
Cameras
Computer science
control theory
systems
Driver assistance systems
Exact sciences and technology
Ground, air and sea transportation, marine construction
Image edge detection
Image processing
Maneuvers
Modules
Night
Object detection
Parking
Pattern recognition. Digital image processing. Computational geometry
Performance evaluation
Road traffic
Road transportation and traffic
Traffic control
Traffic flow
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Summary:Backing-out and heading-out maneuvers in perpendicular or angle parking lots are one of the most dangerous maneuvers, especially in cases where side parked cars block the driver view of the potential traffic flow. In this paper, a new vision-based Advanced Driver Assistance System (ADAS) is proposed to automatically warn the driver in such scenarios. A monocular grayscale camera was installed at the back-right side of a vehicle. A Finite State Machine (FSM) defined according to three CAN Bus variables and a manual signal provided by the user is used to handle the activation/deactivation of the detection module. The proposed oncoming traffic detection module computes spatio-temporal images from a set of predefined scan-lines which are related to the position of the road. A novel spatio-temporal motion descriptor is proposed (STHOL) accounting for the number of lines, their orientation and length of the spatio-temporal images. Some parameters of the proposed descriptor are adapted for nighttime conditions. A Bayesian framework is then used to trigger the warning signal using multivariate normal density functions. Experiments are conducted on image data captured from a vehicle parked at different location of an urban environment, including both daytime and nighttime lighting conditions. We demonstrate that the proposed approach provides robust results maintaining processing rates close to real time.
ISSN:1939-1390
1941-1197
DOI:10.1109/MITS.2014.2305448