Autonomous Vehicle Speed Control Using Non-Motion Blur Detection
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Solution Overview
Problem
Autonomous and semi-autonomous vehicles face challenges in maintaining safe operation due to reduced sensor detection ranges caused by environmental obscurants and topographical features, which can impede the effectiveness of perception systems like cameras and laser scanners.
Innovation Solution
A method that adjusts the speed of the vehicle based on the analysis of image data and laser sensor data to determine the presence of obscurants, using a non-motion blur score and optical density calculations to ensure the vehicle operates within its effective detection range, combining data from multiple sensors to maintain safe navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the vehicle operates at high speed, then productivity is improved, but the sensor detection range is reduced due to environmental conditions like dust, smoke, and fog
Solution Approach 1:
The system dynamically adjusts the vehicle's maximum speed based on real-time environmental conditions detected by sensors. When obscurants like dust, smoke, or fog are detected, the system automatically reduces the speed limit to ensure the vehicle can stop within the reduced sensor detection range. This dynamic adjustment resolves the contradiction by making speed adaptive to changing environmental conditions rather than fixed.
Solution Approach 2:
The system uses sensor data feedback to continuously monitor environmental conditions and adjust speed accordingly. The perception systems detect obscurants and provide feedback to the control system, which then modifies the speed parameter to maintain safe operation. This closed-loop feedback mechanism ensures that productivity is maximized within the constraints of reliable sensor detection.
2Loss of time
If the vehicle increases speed to improve efficiency, then time consumption is reduced, but the ability to detect obstacles in time is compromised
Solution Approach 1:
The system dynamically adjusts speed based on the detected sensor range and environmental conditions. When obscurants reduce detection range, the system automatically lowers speed to ensure the vehicle can stop within the available detection range, maintaining adequate response time to obstacles. This dynamic speed adjustment resolves the contradiction by adapting response time to changing environmental conditions.
3Productivity
If the vehicle maintains high speed for productivity, then operational efficiency is improved, but safety is compromised in reduced visibility conditions
Solution Approach 1:
The system takes preliminary action by detecting obscurants and adjusting speed before a hazardous situation develops. The perception systems continuously monitor for dust, smoke, fog, or other obscurants, and the control system proactively reduces speed in anticipation of potential obstacles that may be beyond the reduced sensor range. This preliminary anti-action prevents safety issues rather than reacting to them after they occur.
Solution Approach 2:
The system provides a safety cushion by reducing speed below the maximum possible speed when environmental conditions deteriorate. This speed reduction creates a buffer or cushion of time and distance that allows the vehicle to safely respond to obstacles even when sensor detection range is reduced. The cushioning approach prioritizes safety while maintaining reasonable productivity.
Data Source
AI summary
A method of adjusting a speed of a mobile machine is provided. Image data of a location is collected where currently generated sensor data and previously generated sensor data indicate a discontinuity in sensor data. The image data is analyzed to determine if a non-motion blur score for the image data is above a threshold value. Then, a speed of the mobile machine is adjusted based on a determination that the non-motion blur score is above the threshold value.