Vehicle Adverse Condition Detection With Multi-Task Neural Networks
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Solution Overview
Problem
Existing adverse environment detection systems in vehicles fail to accurately determine current weather, road, and visibility conditions concurrently, often requiring excessive computational resources and lacking reliability and responsiveness, especially in autonomous driving scenarios.
Innovation Solution
A multi-task machine learning model utilizing a backbone neural network processes images to concurrently determine road, weather, and visibility conditions, employing parallel processing layers to reduce computational load and enhance accuracy, suitable for both resource-rich and resource-constrained vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing adverse environment detection systems are used to determine weather, road, and visibility conditions, then detection coverage is provided, but computational resources are excessive and reliability is insufficient
Solution Approach 1:
The patent applies a multi-task machine learning model that simultaneously performs three detection functions: weather condition detection, road condition detection, and visibility condition detection. This multi-functional approach replaces multiple separate detection systems, reducing overall computational resource consumption while improving detection reliability through integrated processing and shared neural network layers.
2Measurement precision
If separate single-task models are used for weather, road, and visibility detection, then each condition can be detected, but processing time increases and accuracy decreases
Solution Approach 1:
The patent merges three separate detection tasks into a single unified neural network model with shared backbone layers. The integrated model processes all three conditions (weather, road, visibility) simultaneously from the same input image, enabling parallel computation and reducing total processing time while improving accuracy through cross-condition feature sharing and joint optimization.
3Measurement precision
If a comprehensive multi-task model is implemented, then detection accuracy improves, but device complexity increases
Solution Approach 1:
The patent segments the neural network into distinct functional components: a shared backbone neural network that extracts common visual features, and three separate task-specific output layers for weather, road, and visibility detection. This modular segmentation allows the model to achieve high detection accuracy across all three conditions while managing complexity through reuse of the backbone layers and independent optimization of each detection task.
Data Source
AI summary
In some implementations, a device may determine a plurality of driving conditions associated with an image of a road scene based on providing a set of features associated with the image to a plurality of processing layers of a model. Each processing layer, of the plurality of processing layers, may determine, in parallel, a respective driving condition of the plurality of driving conditions and may comprise a plurality of sequential linear layers including a first sequential, linear layer comprising a first quantity of neurons corresponding to a quantity of features included in the set of features and computing resources of the device and a last sequential, linear layer comprising a second quantity of neurons that is based on a task associated with determining the respective driving condition. The device may perform one or more actions based on the plurality of driving conditions.