Camera-Based Roadway Mapping With Probabilistic Lane Feature Fusion
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Solution Overview
Problem
Existing methods for creating high-precision digital road maps for autonomous vehicles rely on expensive sensor equipment and do not effectively account for errors in detecting linear roadway objects, such as road markings and boundaries, which reduces mapping accuracy.
Innovation Solution
A method that forms a global map divided into cells, processes images from cameras using a windowed Hough transform to identify linear features, and accounts for detection errors by creating a local map with probability estimates, updating these estimates iteratively, and combining them with a priori probabilities to form a high-precision roadway map without requiring expensive sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expensive sensor equipment such as GPS with RTK and LIDAR is used, then mapping precision is improved, but system cost increases
Solution Approach 1:
The patent replaces expensive, sophisticated sensor equipment (GPS with RTK, LIDAR) with inexpensive standard cameras. The system uses multiple low-cost camera units positioned at different locations on the vehicle to capture images, which are then processed through image processing algorithms to extract roadway information. This substitution of cheap cameras for expensive sensors directly resolves the contradiction between mapping precision and system cost.
Solution Approach 2:
The patent substitutes active sensing systems (LIDAR that emits laser beams, GPS that requires satellite signals) with passive optical systems (standard cameras). The camera-based system captures reflected light from the environment and uses computational image processing to derive roadway geometry and markings, replacing mechanical and electronic sensing systems with optical capture and algorithmic analysis.
2Productivity
If traditional mapping methods are used, then mapping speed is improved, but mapping accuracy deteriorates due to error accumulation
Solution Approach 1:
The patent implements a feedback mechanism where detected linear roadway features (markings, boundaries) are used to update and refine the map continuously. The system processes images in real-time, detects features, compares them with existing map data, and adjusts the map accordingly. This continuous feedback loop prevents error accumulation by constantly verifying and correcting map information against actual observations, thereby maintaining high accuracy while preserving mapping speed.
Solution Approach 2:
The patent performs preliminary processing of images to identify and extract linear roadway features before full map construction. By pre-detecting road markings, boundaries, and other linear features from camera images and storing them as prior information, the system prepares data in advance that can be quickly integrated into the map without requiring complex real-time computation during actual mapping operations, thus maintaining both speed and accuracy.
3Measurement precision
If high-precision positioning systems are used, then position accuracy is improved, but system complexity increases
Solution Approach 1:
The patent enables the camera system to determine vehicle position and orientation self-service through image processing alone. By detecting linear roadway features such as lane markings, curbs, and road boundaries in camera images, and using geometric relationships between these features, the system calculates the vehicle's position and heading without requiring external positioning systems like GPS with RTK. This self-determination capability eliminates complex positioning hardware while maintaining position accuracy.
Data Source
AI summary
The present method relates to navigation aids for highly automated vehicles (HAVs). According to the proposed terrain mapping method for HAVs, a global terrain map is generated, which is divided into cells; said map is recorded in the memory of a mapping module of an HAV on-board computer; said mapping module receives a stream of images from a camera mounted on the HAV; the received images are processed and linear roadway objects are detected; a map of features of the detected linear roadway objects is generated; a local map (1) is generated, which is divided into cells; to account for errors in the detection of linear roadway objects (2), initial probability estimates for the presence of features of detected linear roadway objects in cells of the global map and of the local map are determined, wherein the probability estimate for the cells of the global map is an a priori estimate; the features of detected linear roadway objects are recorded in the corresponding cells of the global and local maps; a final map of the roadway (3) is obtained by binarizing the cells of the obtained map using a threshold value for the probability estimate for the presence of a feature of a detected linear roadway object.