Road Friction Estimation via Dynamic Vehicle Probing Selection
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Solution Overview
Problem
Autonomous and semi-autonomous vehicles face challenges in efficiently estimating road friction, leading to increased wear and tear and reduced driving experience due to frequent road friction probing maneuvers, which are not optimally distributed across road segments.
Innovation Solution
A computing system dynamically selects vehicles to perform road friction probing maneuvers based on elapsed time since the last probing event, weather conditions, and predicted vehicle traffic, optimizing the selection of road segments and vehicles to reduce the frequency of probing and enhance data coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vehicles frequently perform road friction probing maneuvers to estimate road friction, then the accuracy of road friction data is improved, but vehicle wear and tear increases and driving experience deteriorates
Solution Approach 1:
The system dynamically adjusts the frequency and timing of road friction probing maneuvers based on real-time conditions such as vehicle speed, road type, weather, and traffic. Instead of fixed-frequency probing, the system optimizes when probing occurs to minimize wear while maintaining data accuracy, allowing flexible adaptation to changing operational conditions.
Solution Approach 2:
The system changes multiple parameters simultaneously to optimize probing frequency: vehicle speed adjustments, acceleration rates, steering angles, and timing intervals are all dynamically modified based on road conditions, vehicle state, and environmental factors. This multi-parameter optimization reduces unnecessary probing while ensuring accurate friction estimation when needed.
2Measurement precision
If vehicles frequently perform road friction probing maneuvers to estimate road friction, then the accuracy of road friction data is improved, but the driving experience deteriorates due to increased vibrations
Solution Approach 1:
The system dynamically determines optimal probing moments based on current driving conditions, selecting times when probing maneuvers will have minimal impact on passenger comfort. The system adapts probing intensity and timing to match traffic conditions, road geometry, and vehicle dynamics, reducing vibrations during normal driving while maintaining measurement accuracy when conditions are favorable.
Solution Approach 2:
The system continuously monitors vehicle responses during and after probing maneuvers, using feedback from sensors to assess both friction estimation quality and driver comfort impact. This feedback loop allows the system to learn from past maneuvers and optimize future probing strategies to balance data accuracy with driving experience, reducing unnecessary vibrations.
3Quantity of substance
If road friction probing maneuvers are performed without optimized vehicle selection, then individual vehicle data collection is sufficient, but overall road friction map coverage is limited and redundant probing occurs
Solution Approach 1:
The system merges data collection efforts across multiple vehicles by coordinating probing maneuvers spatially and temporally. Vehicles in the same fleet or geographic area are grouped to collectively cover road segments, with the system assigning probing tasks to specific vehicles based on their routes and timing. This collaborative approach consolidates redundant probing into single coordinated events, maximizing coverage while minimizing total probing frequency across the fleet.
Solution Approach 2:
The system performs preliminary routing analysis and vehicle assignment before probing maneuvers occur, using predicted vehicle trajectories and scheduled routes to pre-determine optimal probing opportunities. By planning ahead and assigning probing tasks based on forecasted vehicle positions and road segment priorities, the system ensures comprehensive coverage without redundant maneuvers, as each vehicle is strategically selected for specific probing assignments.
Data Source
AI summary
Techniques are described for dynamically selecting vehicles to perform road friction probing maneuvers and estimating road friction based on sensor data collected while a vehicle performs the road friction probing maneuvers. In one example, a computing system is configured to select, from a plurality of vehicles, based on an amount of elapsed time since each respective vehicle of the plurality of vehicles has performed a road friction probing maneuver, a vehicle to perform the road friction probing maneuver within a road segment of a roadway, and responsive to selecting the vehicle, output, to the vehicle, a command causing the vehicle to perform the road friction probing maneuver within the road segment.