Augmented Road Line Detection via Sensor Fusion
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Solution Overview
Problem
Conventional road line detection systems rely on single-source data, such as GPS, which may not provide accurate edge detection due to low resolution, and struggle in varying environmental conditions, leading to potential safety issues for drivers.
Innovation Solution
An augmented road line system that integrates inputs from multiple sensors and databases, including cameras, lidar, radar, and external infrastructure data, using an information fusion controller to build and validate a road line model, assign weights based on environmental conditions, and display augmented road lines on a heads-up display for improved accuracy and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single-source data (GPS) is used for road line detection, then device complexity is reduced, but measurement precision deteriorates due to low resolution
Solution Approach 1:
The patent combines multiple sensor data sources (GPS, camera, lidar, radar, IMU) into a unified road line detection system. The fusion controller integrates these diverse inputs to generate accurate road line predictions, resolving the contradiction by showing that merging multiple sources improves measurement precision while the modular architecture manages device complexity.
2Device complexity
If conventional image processing with convolution is used, then device complexity is reduced, but reliability deteriorates in varying environmental conditions
Solution Approach 1:
The system dynamically adapts to varying environmental conditions by using a trained neural network model that learns optimal detection patterns across different scenarios. The model adjusts its predictions based on input data quality and environmental context, maintaining reliability without requiring complex manual processing rules for each condition.
Solution Approach 2:
The system incorporates validation mechanisms that compare predicted road lines with actual sensor data and provide feedback for model refinement. This feedback loop continuously improves detection reliability while keeping the processing architecture manageable through automated learning rather than manual rule creation.
3Measurement precision
If multiple sensors and databases are integrated, then measurement precision improves through information fusion, but device complexity increases
Solution Approach 1:
The fusion controller is designed as a universal processing unit that handles multiple sensor types (camera, lidar, radar, GPS, IMU) and database inputs through a unified neural network architecture. This multi-functional design improves measurement precision by integrating diverse data sources while managing complexity through a single versatile processing component rather than separate handlers for each sensor type.
Data Source
AI summary
An augmented road line display system that includes one or more sensors installed on a vehicle, one or more external databases, and processing circuitry. The processing circuitry is configured to receive inputs from the one or more databases, sensors of the vehicle, and a sub-system of the vehicle, build and validate a road line model to detect or predict a road line based on the inputs received, determine environmental conditions based on the inputs from one or more of the databases, and a sub-system of the vehicle, assign weights to the inputs received based on the environmental conditions to generate weighted inputs, and execute the road line model to determine the road line based on the weighted inputs.