Video Distribution for Autonomous Vehicles Using Collision Probability
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Solution Overview
Problem
Conventional technologies face limitations in scaling the number of vehicles that can be surveilled by a single surveillant in autonomous driving systems, particularly in agricultural settings where not all machines require equal surveillance intensity, necessitating a method to prioritize and weight surveillance based on collision probability.
Innovation Solution
A video distribution system that calculates the probability of collision for each agricultural machine and adjusts video quality accordingly, distributing high-quality video for machines at higher collision risk and lower-quality video for those at lower risk, allowing a surveillant to effectively monitor multiple vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a surveillant monitors all agricultural machines with equal video quality, then surveillance coverage is comprehensive, but the number of machines that can be monitored is limited
Solution Approach 1:
The patent applies local quality by assigning different video quality levels to different agricultural machines based on their individual collision probabilities. High-risk machines receive high-quality video streams while low-risk machines receive lower-quality streams, allowing the surveillant to maintain comprehensive monitoring of more machines without overwhelming system resources.
Solution Approach 2:
The system dynamically changes the video quality parameter based on the calculated collision probability of each machine. The video quality is adjusted as a variable parameter rather than being fixed, enabling the system to adapt to changing risk conditions and optimize the balance between surveillance reliability and the number of monitorable machines.
2Measurement precision
If high video quality is provided for all machines, then surveillance precision is high, but system resource consumption increases
Solution Approach 1:
Different video quality levels are applied to different machines based on their collision probability. Machines with high collision risk receive high-quality video streams for precise surveillance, while machines with low collision risk receive lower-quality streams, thereby reducing overall system resource consumption while maintaining necessary surveillance precision where needed.
Solution Approach 2:
The system applies excessive action (high video quality) only partially to machines that truly need it based on their collision probability. Rather than providing high-quality video to all machines, the system selectively applies high-quality surveillance only to high-risk machines, reducing unnecessary resource consumption.
3Productivity
If video quality is reduced for low-risk machines, then system scalability improves, but surveillance reliability for those machines decreases
Solution Approach 1:
The system applies different quality levels locally to different machines based on their specific risk profiles. Low-risk machines receive lower-quality video streams which enables system scalability, while the reduced quality is acceptable because these machines pose minimal collision risk. This selective approach maintains surveillance reliability for high-risk machines while improving overall system scalability.
Data Source
AI summary
A video distribution device according to an embodiment is a video distribution device that distributes videos from a plurality of cameras installed in each of a plurality of vehicles that perform autonomous driving to a terminal, and includes a probability of collision calculating unit that calculates a probability of collision indicating a probability of the vehicles colliding with an object by a predetermined time of day, a selecting unit that selects, out of the videos of the plurality of cameras, video from a camera installed in a vehicle of which the probability of collision is highest, and a control unit for setting video quality of the video of the selected camera to be high.