Vehicle Taillight Recognition Using Multi-Frame CNN Analysis
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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 diverse 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 brake, turn, and emergency stop signals, enabling real-time recognition across various driving conditions and vehicle types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional camera systems are used for taillight detection, then the system structure remains simple, but the recognition accuracy and reliability are insufficient to distinguish between turning and braking intentions
Solution Approach 1:
The patent segments the taillight recognition task into multiple specialized neural network models: a brake light recognition model, a turn signal recognition model, and an emergency stop recognition model. Each model is trained to detect specific taillight patterns, improving overall recognition accuracy while maintaining modular system architecture that manages complexity.
Solution Approach 2:
The patent transitions from traditional single-frame image analysis to temporal sequence analysis by processing sequences of images over time. This temporal dimension enables the system to distinguish between transient turn signals and sustained brake lights, significantly improving recognition accuracy.
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 accurately recognize taillight signals
Solution Approach 1:
The system implements feedback mechanisms where recognition results from multiple neural network models are continuously evaluated and refined. The system adjusts its confidence levels based on temporal patterns and cross-validates detections across different models, improving reliability while automating the complex recognition process.
Solution Approach 2:
The patent replaces manual taillight interpretation with automated machine learning models that process visual data. This substitution eliminates human error in taillight signal interpretation while maintaining ease of operation through fully automated detection and intent recognition.
3Adaptability or versatility
If the system processes multiple vehicle types and driving conditions, then the adaptability improves, but the detection complexity and computational requirements increase
Solution Approach 1:
The patent develops universal neural network models that can recognize taillight signals across diverse vehicle types including cars, trucks, and motorcycles. The models are trained on varied datasets representing different driving conditions, lighting environments, and vehicle configurations, enabling single-system multi-functionality without proportionally increasing complexity.
Solution Approach 2:
The system performs preliminary processing by pre-training specialized recognition models on extensive datasets covering various vehicle types and conditions. This preliminary action prepares the models to handle diverse inputs efficiently during actual operation, reducing real-time computational complexity while maintaining high adaptability.
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.