Instance-Level Lane Detection Using Shared Segmentation Tasks
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Solution Overview
Problem
Conventional autonomous vehicle control systems face challenges in efficiently processing high loads of image data for instance-level lane detection, which is crucial for accurate navigation and control, while also requiring cost-effectiveness, low power consumption, and adaptability.
Innovation Solution
The system configures tasks related to similar features in image data to execute concurrently, utilizing shared layers for pixel-level image segmentation, enabling instance-level lane detection and improving processing parallelism and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional semantic segmentation is used for image processing, then every pixel can be assigned a categorical label, but different instances of the same object type cannot be differentiated
Solution Approach 1:
The patent segments the image processing task into two distinct stages: first, semantic segmentation assigns categorical labels to pixels; second, instance segmentation differentiates between multiple instances of the same object type. This two-stage segmentation approach resolves the contradiction by maintaining pixel-level classification while adding instance differentiation capability through separate processing modules.
2Productivity
If high-powered data processing systems are configured, then processing loads can be handled, but cost and system weight increase
Solution Approach 1:
The patent implements dynamic task allocation where processing tasks are distributed to available computing resources based on current system state and workload. The system can dynamically switch between different processing configurations and leverage shared computational layers across multiple tasks, optimizing resource utilization without requiring permanently over-provisioned hardware.
Solution Approach 2:
The patent designs processing subsystems with multi-functionality, where the same hardware components and processing pipelines can handle different types of image processing tasks. By creating universal processing modules that can perform semantic segmentation, instance segmentation, and other vision tasks, the system avoids duplicating hardware for each function, thereby reducing overall system weight while maintaining high processing capability.
3Device complexity
If semantic segmentation assigns the same label to every instance, then processing is simplified, but instance-level detection capability is lost
Solution Approach 1:
The patent divides the segmentation process into distinct stages: semantic segmentation for categorical classification and instance segmentation for differentiation. Each stage has specialized processing optimized for its specific goal, maintaining relative simplicity within each module while achieving high instance-level detection accuracy through their combination.
Data Source
AI summary
A system and method for instance-level lane detection for autonomous vehicle control includes: receiving training image data from a training image data collection system; performing a training phase to train a plurality of tasks associated with features of the training image data, the training phase including extracting roadway lane marking features from the training image data, causing the plurality of tasks to generate task-specific predictions based on the training image data, determining a bias between the task-specific prediction for each task and corresponding task-specific ground truth data, and adjusting parameters of each of the plurality of tasks to cause the bias to meet a pre-defined confidence level; receiving image data from an image data collection system associated with an autonomous vehicle; and performing an operational phase including extracting roadway lane marking features from the image data, causing the plurality of trained tasks to generate instance-level lane detection results.