Taillight Signal Recognition Using Multi-Frame CNN Detection
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Solution Overview
Problem
Conventional autonomous vehicle control systems face challenges in accurately recognizing taillight signals, particularly distinguishing between turning and braking intentions, and struggle with diversity in vehicle types, leading to compromised safety and efficiency.
Innovation Solution
A system and method utilizing front-facing cameras, convolutional neural networks, and machine learning models to detect taillight signals by creating datasets and refining models to recognize and classify taillight illumination status in real-time, including brake, turn, and emergency stop signals for various vehicle types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional camera systems are used for autonomous vehicle control, then the system structure remains simple, but the ability to recognize taillight signals accurately is compromised
Solution Approach 1:
The patent introduces an intermediary processing layer between the camera and the control system. This includes image preprocessing modules that enhance taillight detection capability, and a dedicated taillight recognition module that acts as a specialized intermediary to interpret camera data accurately, thereby improving recognition precision without requiring a complete system overhaul
Solution Approach 2:
The system is divided into distinct functional modules: image acquisition from cameras, image preprocessing for enhancement, taillight signal detection module, and control system. This segmentation allows each module to be optimized independently, improving overall taillight recognition accuracy while managing system complexity through modular architecture
2Reliability
If conventional autonomous vehicle control systems are used, then the system is easier to operate, but the safety and efficiency are compromised due to inability to distinguish taillight signals
Solution Approach 1:
The system implements self-service through automated taillight signal recognition and interpretation. The processing system automatically detects, classifies, and interprets taillight signals without requiring manual intervention, thereby improving safety and reliability while maintaining ease of operation through automation of the complex recognition tasks
Solution Approach 2:
The system incorporates feedback mechanisms where the recognition results are fed back to the control system to adjust vehicle responses. This closed-loop feedback ensures that the control actions are based on accurate taillight signal interpretation, improving reliability while the automated nature maintains operational simplicity
3Adaptability or versatility
If the system attempts to recognize all types of vehicle taillight signals, then the versatility improves, but the device complexity increases
Solution Approach 1:
The patent implements a universal taillight recognition module that can handle multiple vehicle types and taillight configurations through a single integrated system. This multi-functional approach allows the system to adapt to various vehicle types (cars, trucks, motorcycles) and different taillight patterns without requiring separate specialized systems for each vehicle type, thereby achieving versatility while controlling complexity through consolidation
Data Source
AI summary
A system and method for taillight signal recognition using a convolutional neural network is disclosed. An example embodiment includes: receiving a plurality of image frames from one or more image-generating devices of an autonomous vehicle; using a single-frame taillight illumination status annotation dataset and a single-frame taillight mask dataset to recognize a taillight illumination status of a proximate vehicle identified in an image frame of the plurality of image frames, the single-frame taillight illumination status annotation dataset including one or more taillight illumination status conditions of a right or left vehicle taillight signal, the single-frame taillight mask dataset including annotations to isolate a taillight region of a vehicle; and using a multi-frame taillight illumination status dataset to recognize a taillight illumination status of the proximate vehicle in multiple image frames of the plurality of image frames, the multiple image frames being in temporal succession.