Dynamic Internal Image Quality Adjustment for Remote Vehicle Monitoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing remote monitoring systems for autonomous vehicles fail to provide real-time, accurate situational awareness inside the vehicle, especially in situations where external factors influence the safety of passengers, and often prioritize external images over internal ones, leading to inadequate information for remote monitoring and control.
Innovation Solution
A remote monitoring system that includes an image reception module, an accident risk prediction module, a quality determination module, and a quality adjustment module to dynamically adjust the quality of internal vehicle images based on predicted accident risks, ensuring high-quality image transmission when risks are detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If external images are prioritized for transmission, then network bandwidth is saved, but real-time situational awareness inside the vehicle is lost
Solution Approach 1:
The system dynamically adjusts the transmission priority and quality of internal images based on real-time accident risk predictions. When risk is low, internal images are transmitted at low priority to conserve bandwidth. When risk increases, the system automatically elevates internal image transmission priority to ensure situational awareness, resolving the contradiction between bandwidth conservation and information completeness.
Solution Approach 2:
The system changes the transmission parameters (priority level, quality setting) of internal images based on predicted accident risk. By modifying these parameters dynamically, the system can adapt the balance between bandwidth usage and information transmission quality, ensuring critical internal situation data is transmitted when needed while conserving resources when safe.
2Reliability
If internal image quality is always high, then passenger safety monitoring is improved, but network bandwidth consumption increases
Solution Approach 1:
The system dynamically adjusts internal image quality based on real-time accident risk assessment. During normal operation, images are transmitted at reduced quality to conserve bandwidth. When the system detects potential accidents through risk prediction, it automatically increases image quality to ensure reliable passenger safety monitoring, thus resolving the contradiction between monitoring reliability and bandwidth consumption.
Solution Approach 2:
The system changes image quality parameters (resolution, frame rate) based on predicted accident risk levels. This parameter adaptation allows the system to maintain high monitoring reliability when critical, while reducing bandwidth consumption during normal operation, effectively balancing the two competing requirements.
3Reliability
If real-time internal monitoring is implemented, then accident response is improved, but device complexity increases
Solution Approach 1:
The system uses a multi-functional approach where the same camera infrastructure serves both normal monitoring and accident risk prediction purposes. The accident risk prediction module leverages existing sensor data and image processing capabilities to provide real-time safety assessment, eliminating the need for separate dedicated systems and reducing overall device complexity while maintaining reliable accident response.
Solution Approach 2:
The system implements feedback loops where accident risk predictions directly influence monitoring parameters and resource allocation. This feedback mechanism allows the system to respond automatically to changing conditions without requiring complex manual intervention or multiple independent systems, simplifying the overall architecture while improving accident response capability.
Data Source
AI summary
An image reception unit receives an internal image from a mobile object. An accident risk prediction unit predicts a risk of occurrence of an accident inside the mobile object based on the internal image and situation information indicating a situation of the mobile object. A quality determination unit determines internal image quality information indicating quality of the internal image based on a result of the predicted risk of the inside accident. A quality adjustment unit adjusts the quality of the internal image based on the internal image quality information.