Image-Based Vehicle Localization Using Road Surface Feature Detection
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Solution Overview
Problem
Current vehicle localization methods for enhancing safety and automation are often unreliable and require significant infrastructure changes, failing to provide robust and error-resistant solutions for preventing or reducing accident severity.
Innovation Solution
An image-based vehicle localization method that uses a combination of cameras, computing devices, and sensors to measure vehicle position and heading, process road image data, and generate local maps, enabling accurate localization and feedback for improved safety and automation systems without requiring extensive infrastructure modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If image-based vehicle localization methods are implemented, then reliability of vehicle localization is improved, but device complexity increases due to multiple cameras and sensors
Solution Approach 1:
The patent combines multiple cameras (front-facing, rear-facing, side-facing) and various sensors (GPS, IMU, LIDAR) into an integrated image-based localization system. This merging of multiple detection components creates a redundant and cross-validated localization system that improves reliability while managing complexity through unified processing architecture.
Solution Approach 2:
The imaging system serves multiple functions: capturing road surface images for localization, generating local maps for navigation, providing feedback for vehicle control, and enabling both current position determination and historical trajectory reconstruction. This multi-functionality reduces the need for separate dedicated systems.
2Measurement precision
If multiple cameras and sensors are used for image-based localization, then measurement precision of vehicle position and heading is improved, but device complexity increases
Solution Approach 1:
The system segments the localization task into multiple specialized detection components: front cameras for forward position, rear cameras for backward position, side cameras for lateral position, and separate sensors for different measurement types. Each segment contributes to overall precision while maintaining modular complexity management.
Solution Approach 2:
The patent introduces image processing algorithms and data fusion techniques as intermediaries between the raw sensor data and final localization output. These intermediary processing layers integrate information from multiple cameras and sensors, improving measurement precision through cross-validation and error correction while abstracting the complexity from the control system.
3Productivity
If road image data processing is performed to generate local maps, then productivity of vehicle navigation is improved, but use of energy increases due to continuous image processing
Solution Approach 1:
The system performs preliminary image processing to generate local maps in advance during normal operation, storing these maps for future navigation tasks. This preliminary action allows rapid navigation decisions to be made using pre-processed map data rather than performing intensive real-time processing during critical navigation moments, reducing peak energy consumption.
Solution Approach 2:
The patent implements periodic image processing and map generation at strategically selected moments rather than continuous processing. The system processes images periodically to update local maps and reconcile position estimates, balancing navigation productivity with energy conservation by processing only when necessary to maintain accuracy.
Data Source
AI summary
A method for image-based vehicle localization includes measuring, at a vehicle, a position and a heading of the vehicle; capturing, at an image capture system of the vehicle, road surface image data of a road surface; processing the road surface image data to correct for distortion in the road surface image data due to pitch and roll of the vehicle; performing feature detection on the processed road surface image data to detect lane markers on the road surface; generating a local map based on the detected lane markers; wherein generating the local map comprises identifying a lane demarcated by the detected lane markers; and controlling the vehicle according to the local map.