Neural Network Curve Detection for Occluded Lane Boundaries
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Solution Overview
Problem
Current lane detection systems in intelligent driving monitoring systems (IDMS), advanced driver assistance systems (ADAS), and autonomous driving systems face challenges in accurately detecting curves, especially in complex and variable real-world driving scenarios with poorly marked, missing, or occluded lane boundaries, leading to unreliable and inaccurate detections.
Innovation Solution
The proposed solution involves a method and apparatus that use a trained machine learning model to simultaneously detect curves and parameterize lane boundaries from camera images, allowing for robust curve determination even in challenging conditions by processing visual data to identify curve presence and parameters in a single forward pass, thereby avoiding errors associated with sequential processing techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current sequential lane detection methods are used, then the system can detect curves in clear conditions, but the detection accuracy deteriorates in complex scenarios with poorly marked or occluded lane boundaries
Solution Approach 1:
The patent combines curve detection and curve parameterization into a single simultaneous operation using a trained machine learning model. Instead of sequentially detecting curves first and then parameterizing them, the system performs both tasks in one forward pass, allowing the model to leverage contextual information from the entire image to improve both detection reliability and parameterization precision simultaneously.
Solution Approach 2:
The patent transforms the lane detection problem into a parameter estimation problem where the machine learning model directly outputs curve parameters (such as polynomial coefficients) along with detection confidence. This parameter-based approach allows the system to handle uncertain or occluded lane boundaries by providing probabilistic outputs rather than deterministic binary detections.
2Device complexity
If sequential curve detection and parameterization is performed, then the processing steps are clear and structured, but errors from the detection step propagate to the parameterization step
Solution Approach 1:
The patent merges the detection and parameterization workflows into a unified machine learning model that performs both functions simultaneously. This eliminates the sequential dependency where parameterization relies on detection outputs, preventing error propagation while maintaining a single structured processing pipeline through the neural network forward pass.
3Productivity
If traditional curve fitting methods are used, then the system works on uncrowded highways in daylight, but performance deteriorates in diverse real-world driving situations
Solution Approach 1:
The patent uses a machine learning model that can adapt to diverse driving scenarios by learning from training data covering various conditions (weather, lighting, road types, traffic density). The model parameters are adjusted during training to handle the variability of real-world scenarios, enabling the system to maintain high performance across different environments rather than being optimized for single conditions.
Solution Approach 2:
The trained machine learning model serves as a universal solution that handles multiple driving scenarios simultaneously - from clear highways to crowded urban streets, from daylight to nighttime conditions. The single model architecture provides multi-functional capability, replacing the need for scenario-specific detection algorithms.
Data Source
AI summary
A neural network is configured to process image data captured by a vehicle-mounted camera. The neural network includes common processing layers (a trunk) and separate, parallelizable processing layers (branches). An object detection branch of the neural network is trained to detect objects that may be visible from the vehicle-mounted camera, such as cars, trucks, and traffic signs. A curve determination branch is trained to detect and parameterize salient curves, such as lane boundaries and road boundaries. The curve determination branch itself is configured with a trunk and branch architecture, having both common and separate processing layers. A first branch computes a likelihood that a curve is present in a given location of the image data and a second branch further localizes the curve within the given location if such a curve is present. Training of the different branches of the neural network may be decoupled.