Instance-Level Lane Detection via Feature Similarity Parallelism
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Solution Overview
Problem
Conventional autonomous vehicle control systems struggle to efficiently process high loads of image data from vehicle cameras, particularly in differentiating between multiple instances of the same object type, such as lane markings, while meeting cost, weight, power, and adaptability requirements.
Innovation Solution
The system configures an autonomous vehicle computation and control system to determine the intrinsic similarity of features in image data, allowing tasks associated with shared features to execute concurrently, thereby increasing processing parallelism and reducing execution time. This approach is applied to instance-level lane detection, enabling the recognition and differentiation of multiple lane markings in an input image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing methods are used to process vehicle camera data, then the system can identify object categories, but it cannot differentiate between different instances of the same object type such as multiple lane markings
Solution Approach 1:
The patent segments the image processing task into two distinct stages: first, semantic segmentation to identify object categories; second, instance segmentation to differentiate between multiple instances of the same object type. This is achieved through separate neural network models (semantic segmentation model and instance segmentation model) that process the image data in sequence, allowing the system to handle instance-level differentiation without overwhelming complexity in a single model.
Solution Approach 2:
The patent introduces an additional processing dimension by adding instance segmentation on top of semantic segmentation. Instead of attempting to solve instance differentiation within the same processing layer, the system adds a new processing stage that operates on the outputs of the first stage, effectively moving from 2D semantic labeling to 3D instance-aware representation by incorporating instance identifiers.
2Productivity
If high-powered data processing systems are configured to handle high processing loads, then processing capability is improved, but cost and system weight increase
Solution Approach 1:
The processing system is segmented into multiple specialized components: semantic segmentation model, instance segmentation model, and processing management module. Each component handles a specific aspect of the processing task, allowing for optimized resource allocation and reduced overall system weight compared to a single monolithic high-powered processor attempting to handle all tasks simultaneously.
Solution Approach 2:
The patent designs a multi-functional processing architecture where the same hardware platform executes different processing models and algorithms for various autonomous driving tasks. The system can dynamically allocate processing resources between semantic segmentation, instance segmentation, and other autonomous driving functions, maximizing the utility of each processing unit and reducing the need for dedicated high-powered hardware for each function.
3Measurement precision
If semantic segmentation is used to label each pixel to a category, then image analysis capability is improved, but execution time increases due to serial processing requirements
Solution Approach 1:
The system performs semantic segmentation first as a preliminary action, generating category labels for all pixels before proceeding to instance segmentation. This preliminary classification provides a foundation that accelerates the subsequent instance differentiation process, as the instance segmentation model can operate on pre-identified semantic regions rather than processing the entire image from scratch.
Solution Approach 2:
The patent transitions from 2D pixel-level semantic classification to 3D instance-aware representation by incorporating instance identifiers as an additional dimension. This dimensional extension allows the system to maintain pixel-level accuracy while adding instance differentiation capability in a structured manner that optimizes processing efficiency.
Data Source
AI summary
A system and method for instance-level roadway feature detection for autonomous vehicle control are disclosed. A particular embodiment includes: receiving image data from an image data collection system associated with an autonomous vehicle; extracting roadway features from the image data, causing a plurality of trained tasks to generate instance-level roadway feature detection results based on the image data, the plurality of trained tasks having been individually trained with different features of training image data received from a training image data collection system and corresponding ground truth data, the training image data and the ground truth data comprising data collected from real-world traffic scenarios; causing the plurality of trained tasks to generate task-specific predictions of feature characteristics based on the image data and to generate corresponding instance-level roadway feature detection results; and providing the instance-level roadway feature detection results to an autonomous vehicle subsystem to control operation of the autonomous vehicle.