Onboard Camera Traffic Light Prediction System
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Solution Overview
Problem
The Vehicle to Everything (V2X) system requires additional terminals in vehicles, increasing material costs and is unreliable without infrastructure support for traffic light communication, making it difficult to provide accurate information.
Innovation Solution
An in-vehicle device equipped with a camera to capture and process traffic light information, cumulatively store data, and provide prediction information based on accumulated data, using a processor to determine when reliable predictions can be made and calculating confidence intervals for signal change times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If V2X system is used to exchange traffic light information, then information exchange capability is improved, but material cost increases due to additional terminals
Solution Approach 1:
The in-vehicle device performs multiple functions: it captures images of traffic lights, detects traffic light information, accumulates data, and provides predictions. This multi-functional approach replaces the need for separate V2X terminals and dedicated traffic light detection equipment, reducing material costs while maintaining information exchange capability
Solution Approach 2:
Instead of relying on infrastructure-based V2X communication, the system uses onboard cameras to capture and copy visual information from traffic lights. This copying approach allows the vehicle to obtain traffic light information independently without requiring communication infrastructure or additional terminals
2Adaptability or versatility
If V2X system is used for traffic light information, then communication capability is improved, but system reliability deteriorates when infrastructure is unavailable
Solution Approach 1:
The system uses the vehicle's own camera and processing capabilities to independently detect and analyze traffic light information. This self-service approach eliminates dependency on external infrastructure, ensuring the system can reliably obtain accurate traffic light information regardless of whether V2X infrastructure is available
Solution Approach 2:
The system accumulates traffic light data in advance and performs preliminary analysis to build prediction models. By preparing data and models beforehand, the system ensures reliable and accurate information provision even when infrastructure support is unavailable, as the core functionality relies on pre-collected data rather than real-time communication
3Measurement precision
If traffic light prediction is provided with high accuracy, then information quality is improved, but data processing complexity increases
Solution Approach 1:
The system accumulates traffic light data in advance and performs preliminary processing to identify patterns and build prediction models. By preparing data beforehand and establishing confidence levels, the system achieves high prediction accuracy without requiring complex real-time processing, thus managing data processing complexity effectively
Solution Approach 2:
The system calculates confidence levels and confidence intervals for predictions, providing feedback on the reliability of predicted information. This feedback mechanism allows the system to maintain high accuracy by continuously evaluating and adjusting predictions based on accumulated data, while managing complexity through structured confidence assessment rather than overly complex algorithms
Data Source
AI summary
An in-vehicle device and a method for providing traffic light information by the in-vehicle device are provided. The in-vehicle includes a camera that photographs a front side of a vehicle and a processor that detects traffic light information by recognizing a traffic light in an image captured by the camera. The detected traffic light information is stored cumulatively and the controller provides traffic light prediction information for the traffic light, based on the accumulated traffic light information.