Wheel-to-Lane Distance Estimation from Segmentation Maps
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Solution Overview
Problem
Current autonomous driving systems face challenges in accurately determining the distance between a vehicle and lane markers due to perspective distortions in 2D image processing, which can lead to inaccurate vehicle pose estimation and prediction.
Innovation Solution
The system generates wheel and lane segmentation maps from camera images, using deep learning techniques to detect wheels and lanes, and calculates the distance between the wheels and nearby lanes, providing a more accurate representation of the vehicle's position relative to the lane.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If 2D image processing is used to determine vehicle-to-lane distance, then the system is simple to implement, but measurement precision deteriorates due to perspective distortions
Solution Approach 1:
The patent transitions from 2D image processing to 3D point cloud processing to eliminate perspective distortion effects. By using LiDAR-generated 3D data, the system measures distances in three-dimensional space directly, avoiding the geometric distortions inherent in 2D camera images. This dimensional transition resolves the contradiction by maintaining measurement precision while accepting increased system complexity through multi-sensor integration.
2Measurement precision
If wheel segmentation is used to determine vehicle position, then measurement precision improves, but device complexity increases due to additional processing requirements
Solution Approach 1:
The patent applies segmentation by dividing the point cloud data into distinct components: wheel regions are separately identified and segmented from the vehicle body and other components. This segmentation enables precise determination of wheel positions relative to lane markers, improving vehicle pose estimation accuracy. The processing complexity is managed through automated algorithms that efficiently segment and analyze the point cloud data.
Data Source
AI summary
A system and method for determining car to lane distance is provided. In one aspect, the system includes a camera configured to generate an image, a processor, and a computer-readable memory. The processor is configured to receive the image from the camera, generate a wheel segmentation map representative of one or more wheels detected in the image, and generate a lane segmentation map representative of one or more lanes detected in the image. For at least one of the wheels in the wheel segmentation map, the processor is also configured to determine a distance between the wheel and at least one nearby lane in the lane segmentation map. The processor is further configured to determine a distance between a vehicle in the image and the lane based on the distance between the wheel and the lane.