Autonomous Driving Map Verification Using Camera and Radar
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Solution Overview
Problem
High-definition maps errors in autonomous driving systems can cause malfunctions, as they do not accurately reflect real-world road conditions, leading to incorrect distance calculations and lane detection issues.
Innovation Solution
An autonomous driving system equipped with front cameras, radars, and lidars that process image and detection data to calculate distances between the vehicle and road boundaries, determine a map quality index, and identify errors in the high-definition map by comparing actual and mapped data, transferring control to the driver or updating the map as necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a high-definition map is used for autonomous driving, then navigation accuracy and route planning are improved, but map errors cause malfunctions and reduce system reliability
Solution Approach 1:
The system continuously compares actual sensor data (camera images, radar detections) with high-definition map data to detect discrepancies. When errors are detected in the map, the system generates feedback signals to correct the map data or adjust navigation decisions, thereby maintaining reliability while using high-definition maps for improved accuracy.
Solution Approach 2:
The system performs preliminary verification of high-definition map data by comparing it with real-time sensor observations before using the map data for critical navigation decisions. This preliminary check prevents malfunction caused by map errors while preserving the navigation accuracy benefits of high-definition maps.
2Measurement precision
If the autonomous driving system continuously verifies map accuracy using multiple sensors, then map error detection capability is improved, but system complexity and computational load increase
Solution Approach 1:
The verification process is segmented into distinct functional modules: sensor data acquisition, feature extraction, map data comparison, error detection, and correction. Each module handles a specific aspect of the verification process, making the overall complex system more manageable and maintainable while achieving high detection capability.
Solution Approach 2:
The system uses multi-functional sensors (cameras and radars) that serve both primary autonomous driving functions and map verification functions. This universal use of existing sensors improves map error detection capability without adding dedicated verification hardware, thereby limiting the increase in system complexity.
3Measurement precision
If the system calculates multiple distance measurements and compares them to determine map quality, then map error identification accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system calculates multiple distance measurements (first and second distances from sensor data, third and fourth distances from map data) to comprehensively verify map accuracy. This excessive calculation approach ensures high error identification accuracy by cross-validating measurements from different sensors and data sources.
Solution Approach 2:
The system performs preliminary filtering and preprocessing of sensor data and map data before comparison, organizing features and extracting key parameters in advance. This preliminary action reduces the computational burden during real-time verification, mitigating the processing time increase caused by multiple distance calculations.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This solution enables the autonomous driving system to quickly detect and mitigate errors in high-definition maps, preventing malfunctions and ensuring safe operation by accurately determining vehicle coordinates and improving map quality indices.
Implementation Method 1
a first sensor installed in a vehicle, having a field of view facing in front of the vehicle, and configured to acquire front image data
Implementation Method 2
a second sensor selected from the group consisting of a radar and a light detection and ranging (lidar)
Implementation Method 3
a second sensor selected from the group consisting of a radar and a light detection and ranging (lidar)
Data Source
AI summary
Disclosed herein an autonomous driving system includes a first sensor installed in a vehicle, having a field of view facing in front of the vehicle, and configured to acquire front image data; a second sensor selected from the group consisting of a radar and a light detection and ranging, installed in the vehicle, having a detection field of view facing in front of the vehicle, and configured to acquire forward detection data; a communicator configured to receive a high definition map at a current location of the vehicle from an external server; and a controller including a processor configured to process the high definition map, the front image data, and the forward detection data.