Autonomous Vehicle Friction Estimation via Embedded Wheel Actions
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Solution Overview
Problem
Current computing systems in vehicles, both autonomous and non-autonomous, face challenges in accurately determining friction levels on driving surfaces, which can affect navigation and control, particularly during events like acceleration, deceleration, or stopping, without causing noticeable disruptions to passengers.
Innovation Solution
A computing system that detects events such as acceleration, deceleration, or stopping and uses operational data to determine friction levels by initiating wheel-based actions like steering, braking, or propulsion, minimizing passenger perception while obtaining necessary operational data to calculate friction between tires and the surface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wheel-based actions (steering, braking, propulsion) are initiated to obtain operational data for friction calculation, then measurement precision of friction levels is improved, but passengers may perceive disruptions during these actions
Solution Approach 1:
The system performs friction measurements during pre-planned wheel-based actions (steering, braking, propulsion) that are already scheduled as part of normal vehicle operation. By embedding the measurement actions within the vehicle's existing motion plan, the system obtains friction data without adding extra disruptive maneuvers.
Solution Approach 2:
The vehicle's own operational movements (steering, braking, propulsion) are utilized to generate the wheel-based actions needed for friction measurement. The vehicle serves its own measurement needs by using its normal operational repertoire, eliminating the need for separate dedicated measurement maneuvers that would disrupt passengers.
2Reliability
If friction data is obtained through dedicated wheel-based actions, then reliability of friction estimation is improved, but vehicle operation time is increased
Solution Approach 1:
The system combines friction measurement objectives with the vehicle's existing operational actions. Wheel-based actions originally planned for navigation or control purposes are merged with friction estimation objectives, allowing the vehicle to achieve both its operational goal and friction data collection without additional time expenditure.
Solution Approach 2:
The wheel-based actions are designed to serve multiple functions simultaneously: they perform the vehicle's primary operational task (steering, braking, or propulsion) while also generating the operational data needed for friction estimation. This multi-functionality eliminates the need for separate dedicated measurement maneuvers.
3Measurement precision
If multiple wheel-based actions are initiated for comprehensive friction measurement, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system uses feedback from the vehicle's existing sensors (wheel speed, steering angle, brake pressure) to determine when wheel-based actions have generated sufficient operational data for accurate friction estimation. This feedback mechanism allows the system to adaptively decide when measurements are complete without requiring complex predetermined sequences.
Solution Approach 2:
The system monitors changes in operational parameters (wheel speed, steering angle, brake pressure) during wheel-based actions to assess friction conditions. By tracking parameter changes rather than requiring fixed measurement protocols, the system achieves comprehensive friction measurement with simpler control logic.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables vehicles to accurately estimate friction levels during various events, improving navigation and control by using friction data for motion planning, route optimization, and reducing the risk of accidents by tailoring vehicle movements based on surface conditions.
Implementation Method 1
determining, based at least in part on the operational data, data indicative of a friction associated with a surface upon which the autonomous vehicle is traveling during the event
Data Source
AI summary
Systems and methods are provided for generating data indicative of a friction associated with a driving surface, and for using friction data as part of controlling autonomous vehicle operations. In one example, a computing system can detect an event including at least one of an acceleration, a deceleration, or a stop associated with an autonomous vehicle and obtain, in response to detecting the event, operational data associated with the autonomous vehicle during the event. The computing system can determine, based at least in part on the operational data, data indicative of a friction associated with a surface upon which the autonomous vehicle is traveling during the event. The computing system can control the autonomous vehicle based at least in part on the data indicative of the friction associated with the surface.