Brake Light Detection With Day-Night Mode Switching
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Solution Overview
Problem
Autonomous and assisted driving vehicles face challenges in accurately detecting brake lights, especially under varying lighting conditions, which can lead to unsafe driving maneuvers and intersection decisions.
Innovation Solution
A system utilizing deep learning and computer vision, combined with LIDAR data, to automatically detect brake lights by selecting day or night modes based on pixel brightness, processing images in HSV or LAB color spaces, and using trained neural networks to classify brake light states, providing notifications for safe driving decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning and computer vision are used to detect brake lights, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The system segments the detection task into multiple specialized components: a mode determination module that selects between day and night modes, a vehicle region detection module that identifies vehicle locations, and a brake light classification module that specifically classifies brake light states. This segmentation allows each component to be optimized for its specific function, improving overall detection accuracy while managing system complexity through modular design.
Solution Approach 2:
The system dynamically adapts its detection approach by determining whether to operate in day mode or night mode based on current lighting conditions. This dynamic switching allows the system to optimize its detection algorithms and parameters according to the environmental context, maintaining high detection accuracy across varying conditions without requiring a completely different system for each scenario.
2Adaptability or versatility
If day and night modes are implemented for brake light detection, then adaptability to different lighting conditions is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary mode determination by analyzing overall image brightness and selecting either day mode or night mode before proceeding with brake light detection. This preliminary action allows the system to pre-configure the appropriate detection algorithms and parameters for the current lighting conditions, ensuring optimal performance without requiring complex real-time adjustments during the detection process.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the ability of autonomous vehicles to accurately detect brake lights in both day and night conditions, improving safety by enabling timely braking and intersection management, and providing alerts for human drivers when necessary.
Implementation Method 1
A system utilizing deep learning and computer vision, combined with LIDAR data
Implementation Method 2
based on camera image data when in the day mode
Data Source
AI summary
Systems, methods, and devices for detecting brake lights are disclosed herein. A system includes a mode component, a vehicle region component, and a classification component. The mode component is configured to select a night mode or day mode based on a pixel brightness in an image frame. The vehicle region component is configured to detect a region corresponding to a vehicle based on data from a range sensor when in a night mode or based on camera image data when in the day mode. The classification component is configured to classify a brake light of the vehicle as on or off based on image data in the region corresponding to the vehicle.