Vehicle Occlusion Estimation Using 3D Point Cloud Range Images
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Solution Overview
Problem
Existing vehicle sensing systems struggle with occlusion regions due to objects blocking the sensor's field of view, leading to unexpected objects appearing suddenly, which existing 2D and 3D raytracing approaches are either resource-intensive or inaccurate.
Innovation Solution
A vehicular sensing system using a 2.5-dimensional approach with a depth buffer to process three-dimensional point clouds, generating range images to classify occlusions, reducing resource consumption by generating occlusion maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radar sensors are used to detect objects in vehicle sensing systems, then object detection capability is provided, but occlusion regions cannot be detected leading to incomplete environmental perception
Solution Approach 1:
The system performs preliminary classification of points as occluded or non-occluded by comparing range values with threshold range values before final object detection. This preliminary action identifies occlusion regions in advance, allowing the system to anticipate hidden objects and compensate for the radar sensor's inability to directly detect occluded areas.
2Measurement precision
If three-dimensional point cloud data is processed to generate virtual range images and classify occluded points, then occlusion region detection accuracy is improved, but computing resource consumption increases
Solution Approach 1:
The system applies partial action by classifying only certain points as occluded or non-occluded based on range value comparisons, rather than processing all three-dimensional point cloud data in full detail. This selective classification approach achieves sufficient occlusion region detection accuracy while reducing the overall computing resource consumption compared to exhaustive processing methods.
Data Source
AI summary
A vehicular sensing system includes a sensor disposed at a vehicle. The vehicular sensing system generates a three-dimensional point cloud of data representing a three-dimensional scene exterior of the vehicle within a field of sensing of the sensor. The virtual range image includes a set of points representing the three-dimensional point cloud of data. The system, for each respective point, classifies the respective point as occluded or non-occluded. The system determines one or more occlusion regions based on the classification for the respective points. Each occlusion region includes a region that is within the field of sensing of the sensor but that is not sensed by the sensor due to an object obstructing the sensor from the sensing region. The system tracks a moving detected object as it enters an occlusion region and predicts where the moving detected object will exit from the occlusion region.