SDV Tire Path Selection for Roadway Wear Reduction
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Solution Overview
Problem
Self-driving vehicles (SDVs) consistently driving in the same tire path on roadways leads to premature rutting due to their precise navigation, causing uneven wear and potential damage to the road surface.
Innovation Solution
Implementing a method where SDVs determine the pattern of tire path usage and adjust their route based on the type of tires they have, selecting a less rutted path to distribute the weight and reduce wear, using onboard computers and sensor data to optimize tire path selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If SDVs maintain precise and uniform positioning in the middle of the lane to avoid accidents and improve fuel efficiency, then safety and energy consumption are improved, but premature rutting and uneven wear of the roadway occur
Solution Approach 1:
The patent applies local quality by varying the lateral positioning of SDVs based on specific road conditions and locations. Instead of uniform positioning, the system adjusts tire path selection locally according to road surface condition data, allowing SDVs to maintain precision while distributing wear across different portions of the lane depending on local roadway needs.
Solution Approach 2:
The system implements dynamics by continuously adjusting tire path selection based on real-time or near-real-time road surface condition data. The lateral positioning of SDVs is not static but dynamically modified according to accumulated wear patterns, enabling the fleet to adapt its behavior to prevent premature rutting while maintaining safety.
2Reliability
If SDVs travel exactly in the middle of the lane to avoid hitting vehicles in adjacent lanes, then collision avoidance is improved, but the same part of the lane is always driven upon causing premature rutting
Solution Approach 1:
The system performs preliminary action by using road surface condition data (accumulated from previous vehicles or external sources) to pre-determine optimal tire paths before SDVs arrive. This allows the fleet to proactively distribute lateral positioning to avoid areas that are prone to rutting, thereby extending roadway service life while maintaining safe operation.
Solution Approach 2:
The patent implements feedback mechanisms where road surface condition data is continuously collected and fed back to the SDV control system. This feedback loop enables the system to learn from accumulated wear patterns and adjust future tire path selections accordingly, creating a self-correcting mechanism that balances collision avoidance with roadway preservation.
3Productivity
If SDVs operate with high precision to exceed human driver performance, then driving efficiency and safety are improved, but uniform operation patterns lead to concentrated wear on specific roadway sections
Solution Approach 1:
The system applies parameter changes by modifying the lateral positioning parameter of SDVs based on road surface conditions. Instead of maintaining a fixed lateral position for optimal efficiency, the system varies this parameter dynamically, allowing SDVs to shift their tire paths left or right within the lane to distribute wear while maintaining overall driving efficiency and safety.
Data Source
AI summary
A method steers a self-driving vehicle (SDV) onto a particular tire path on a roadway based on what type of tires are on the SDV. The method determines a pattern of tire path usage by vehicles on a roadway, and a type of tire upon which the SDV is riding. The method then directs an on-board computer on the SDV to drive the SDV on at least one specific tire path on the roadway based on the pattern of tire path usage by the vehicles on the roadway and the type of tire upon which the SDV is riding.