Parking space detection with hierarchical dynamic occupancy grids

Abstract

an automatic parking system relies on precise estimation of parking space geometry. This paper proposes the use of a hierarchical threedimensional occupancy grid for the detection of parking spaces. The occupancy grid covers the environment representation of the static world. A hierarchical design allows dynamic selection of the level of detail. Applying a three-dimensional grid provides the additional benefit of supporting a variety of other functions including height estimation using a single environment representation type [7].The presented approach derives the distance to obstacles and walls and thus is able to represent the free space that forms parking spaces. In a second step, the dimensions of the parking space are calculated. For evaluation, real parking spaces are detected and estimated using short range radar sensors. The calculated dimensions are compared to the ground truth.

Bayesian environment representation, prediction, and criticality assessment for driver assistance systems

“…2 In recent times, grid-based representations have increasingly been used in the ADAS domain, e.g. for realizing CAS with an incorporation of available free space [205], lateral vehicle guidance [155], grid mapbased road boundary and road course estimations [65,107,131,243,269], an overtaking assistant [105], precise localization by matching a digital road map to the grid [132], and parking space detection [212].…”

Where to park? minimizing the expected time to find a parking space

“…al. [17] utilize on-board short-range radars. The measurements from three radars are stored into a 3D occupancy grid, that represents the local surroundings of the car.…”

Detection of parking spots using 2D range data

“…While technologies such as loop detectors can be used to sense vehicles, they are not always available as a part of the infrastructure in every potential parking space. Many driving assistance systems use ultrasonic sensors or radar to detect targets [6], [7], but they are not applicable in the autonomous driving domain due to low accuracy. Several approaches have been attempted addressing detection of vehicles and parking spots, including detection with optical sensors (i.e.…”