Camera-Assisted Vehicle Lamp Diagnosis via V2V Communication
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Solution Overview
Problem
Drivers often fail to notice non-functioning vehicle lamps, such as tail lights, which can lead to safety hazards and legal issues due to the lack of on-board diagnostics and visibility challenges, especially with rear-facing lamps.
Innovation Solution
A vehicle equipped with a camera and processor that classifies target vehicles, determines lamp locations, calculates illumination values, and sends messages via vehicle-to-vehicle communication to alert drivers of malfunctioning lamps, using DSRC modules and image recognition techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If drivers rely on traditional on-board diagnostics for lamp monitoring, then system complexity is reduced, but detection capability and reliability are insufficient due to lack of visibility and diagnostic features
Solution Approach 1:
The patent uses an intermediary diagnostic system that captures images of the vehicle's exterior, processes them to detect lamp malfunctions, and communicates findings back to the driver. This intermediary layer adds detection capability without requiring direct modification of the lamp components themselves, resolving the contradiction between improved reliability and acceptable complexity.
Solution Approach 2:
The patent replaces traditional mechanical/optical diagnostic methods with image capture and digital image processing techniques. By using cameras and software algorithms to detect lamp failures instead of mechanical sensors or visual inspection, the system achieves higher reliability while keeping the added complexity manageable through software-based solutions.
2Measurement precision
If drivers manually inspect lamps for functionality, then system complexity remains low, but detection precision and timeliness are insufficient
Solution Approach 1:
The system enables the vehicle to self-diagnose lamp functionality by automatically capturing images, processing them through algorithms, and identifying malfunctions without driver intervention. This self-service approach achieves high detection precision while minimizing the complexity burden on the driver, as the system operates autonomously.
Solution Approach 2:
Manual visual inspection is replaced with automated image capture and digital processing. The system uses cameras and computer vision algorithms to detect lamp failures with precision far exceeding human capability, while the added complexity is confined to the automated diagnostic subsystem rather than requiring complex modifications to the vehicle's core systems.
3Reliability
If real-time lamp monitoring is implemented, then safety and reliability are improved, but energy consumption and device complexity increase
Solution Approach 1:
The system implements periodic image capture and processing at scheduled intervals rather than continuous monitoring. This periodic operation maintains real-time monitoring reliability for safety-critical lamp functions while significantly reducing energy consumption compared to continuous operation, as the camera and processing systems are activated only at necessary intervals.
Data Source
AI summary
Method and apparatus are disclosed for camera assisted vehicle lamp diagnosis via vehicle-to-vehicle communication. An example vehicle includes a forward-facing camera and a processor. The processor classifies a target vehicle based on images captured by the camera. Based on the classification, the processor determines locations of lamps on the target vehicle and determines when one of the lamps is not functioning properly. Additionally, when one of the lamps is not functioning properly, the processor sends, via a first communication module, a message to the target vehicle indicating of one of the lamps not functioning properly.