Camera Array Friction Estimation for Autonomous Vehicle Control
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Solution Overview
Problem
Autonomously controlled vehicles face challenges in estimating traction between tires and the road surface, leading to potential loss of control on surfaces with water, snow, or ice, resulting in undesirably low maneuvering performance.
Innovation Solution
A system utilizing a camera array to estimate friction-related data, which is used to adjust driving inputs, suspension settings, and aerodynamic characteristics, enabling the vehicle to adapt its maneuvering to the available traction, and communicate with networks for improved accuracy and road condition reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomously controlled vehicle is programmed to make maneuvers at slow acceleration, turning, and braking rates to ensure traction limitations are not exceeded, then vehicle control reliability is improved, but maneuvering performance deteriorates
Solution Approach 1:
The system dynamically changes maneuvering parameters (acceleration, turning, braking rates) based on real-time friction estimates. When high friction is detected, the vehicle can perform aggressive maneuvers; when low friction is detected, maneuvers are moderated. This resolves the contradiction by making maneuvering performance adaptive to actual road conditions rather than consistently conservative.
Solution Approach 2:
The system uses camera arrays to continuously monitor road surface characteristics and estimate friction coefficients, then feeds this information back to adjust maneuvering parameters in real-time. This closed-loop feedback enables the vehicle to maintain reliability while optimizing performance based on actual traction availability.
2Productivity
If the autonomously controlled vehicle attempts maneuvers requiring high traction, then maneuvering performance is improved, but loss of control may occur on surfaces with water, snow, or ice
Solution Approach 1:
The system performs preliminary assessment of road surface friction using camera arrays before executing maneuvers. By estimating friction coefficients in advance, the vehicle can plan appropriate maneuvering strategies that match available traction, preventing loss of control while maintaining performance capability.
Solution Approach 2:
The system adjusts maneuvering parameters dynamically based on detected road conditions. On high-friction surfaces, aggressive maneuvers are permitted; on low-friction surfaces, parameters are moderated. This resolves the contradiction by making performance adaptive to actual conditions rather than consistently conservative.
3Device complexity
If no friction estimation system is used, then device complexity is reduced, but the vehicle cannot adapt to varying road surface conditions
Solution Approach 1:
The camera array, already present for other autonomous vehicle functions, is repurposed to estimate friction by analyzing road surface characteristics. This multi-functional use of existing hardware adds adaptability without significantly increasing system complexity.
Solution Approach 2:
The system uses image processing algorithms as an intermediary to extract friction information from visual data. Rather than directly measuring friction, the algorithms analyze road surface textures, patterns, and characteristics to estimate friction coefficients, enabling adaptability through software rather than complex hardware.
Data Source
AI summary
A friction estimation system for estimating friction-related data associated with a surface on which a vehicle travels, may include a camera array including a plurality of imagers configured to capture image data associated with a surface on which a vehicle travels. The image data may include light data associated with the surface. The friction estimation system may also include an image interpreter in communication with the camera array and configured to receive the image data from the camera array and determine friction-related data associated with the surface based, at least in part, on the image data. The image interpreter may be configured to be in communication with a vehicle control system and provide the friction-related data to the vehicle control system.