Post-Processing Module for Arc-Fitted Lane Template Generation
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Solution Overview
Problem
Current lane detection systems for autonomous vehicles face challenges in precision and stability due to the limitations of differential GPS and require advanced methods to accurately detect road lanes and boundaries for safe navigation.
Innovation Solution
A method and system for lane detection using a non-transitory computer-readable storage medium that executes programs to receive and process lane markings, generate hit-map images, fit lane markings in an arc using parameters, and extend lane templates for improved detection across views, incorporating multiple sensors like GPS, IMU, and LiDAR for enhanced precision and confidence verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If differential GPS is used for lane detection, then the system can provide localization capability, but the precision and stability of localization are insufficient
Solution Approach 1:
The patent combines multiple sensors (differential GPS, IMU, and vision-based lane detection) into an integrated system. The GPS provides global position, the IMU provides orientation and motion data, and the vision system provides lane marking detection. By fusing these multiple sources, the system achieves both high precision and stable localization, overcoming the limitations of using differential GPS alone.
Solution Approach 2:
The patent introduces an intermediary processing layer that fuses GPS coordinates with visual lane marking data and IMU orientation data. This intermediary system reconciles the different measurement systems and produces a unified, stable localization result that is more reliable than any single sensor could provide alone.
2Measurement precision
If traditional lane detection methods are used, then the system can identify lane markings, but the precision and stability of lane detection are insufficient for safe navigation
Solution Approach 1:
The patent merges traditional computer vision lane detection with inertial measurement unit (IMU) data and GPS information. The vision system detects lane markings in images, the IMU provides vehicle orientation and motion compensation, and GPS provides geographic context. This combination achieves both high precision and stable lane detection, resolving the contradiction between precision and reliability.
Solution Approach 2:
The patent implements a dynamic lane detection system that continuously adapts to changing conditions. The system uses IMU data to compensate for vehicle motion and orientation changes, dynamically adjusting the detection parameters and coordinate transformations. This dynamic approach maintains both precision and stability even as the vehicle moves and environmental conditions change.
Data Source
AI summary
A method of visual odometry for a non-transitory computer readable storage medium storing one or more programs is disclosed. The one or more programs include instructions, which when executed by a computing device, cause the computing device to perform the following steps comprising: receiving a lane marking, expressed in god's view, associated with a current view; fitting, in a post-processing module, the lane marking in an arc by using a set of parameters; generating a lane template, using the set of parameters, the lane template including features of the lane marking associated with the current view and features of the arc; and feeding the lane template associated with the current view for detection of a next view.