Abstract
Image segmentation and classification of surfaces and obstacles in automotive radar imagery are the key technologies to provide valuable information for path planning in autonomous driving. As opposed to traditional radar processing, where clutter is considered as an unwanted return and should be effectively removed, autonomous driving requires full scene characterization. Hence, clutter carries necessary information for situational awareness of the autonomous platform and needs to be fully assessed to find the passable areas. In this paper, we proposed a method of automatic segmentation of automotive radar images based on two main steps: unsupervised image pre-segmentation using marker-based watershed transformation, followed by the supervised segmentation and classification of regions containing objects and surfaces based on the use of statistical distribution parameters. Several distributions were considered to characterize returns from specific region types of interest within the scene (denoted as classes) in calibrated radar imagery—the extracted distribution parameters were assessed for their ability to distinguish each class. These parameters were then used as features in a multivariate Gaussian distribution model classifier. Both the performances of the proposed supervised classification algorithm and the automatically segmented results were investigated using F1-score and Jaccard similarity coefficients, respectively.
Highlights
A UTOMOTIVE sensors are the backbone of advanced driver assistant systems (ADASs) and sensing systems for self-driving cars since they can provide robust assessment of the proximate environment for path planning and decision making [1], [2]
The approach proposed in this paper aims at image segmentation and supervised classification of automotive radar images with multiple classes to be identified, so that each pixel within the image is labelled according to a determined class
It can be defined as a HM consisting of initial edge-based pre-segmentation using the WT method and a subsequent classification and region merging process on the pre-segmented regions of interest (RoI’s) based on the statistical distribution parameters extracted from radar data
Summary
A UTOMOTIVE sensors are the backbone of advanced driver assistant systems (ADASs) and sensing systems for self-driving cars since they can provide robust assessment of the proximate environment (up to several hundred meters) for path planning and decision making [1], [2]. The approach proposed in this paper aims at image segmentation and supervised classification of automotive radar images with multiple classes to be identified, so that each pixel within the image is labelled according to a determined class It can be defined as a HM consisting of initial edge-based pre-segmentation using the WT method and a subsequent classification and region merging process on the pre-segmented regions of interest (RoI’s) based on the statistical distribution parameters extracted from radar data. These will be used as features in a multi-variate Gaussian distribution (MGD) based supervised classifier.
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