Autonomous Vehicle Motion Planning Under Road Friction Constraints
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Solution Overview
Problem
Autonomous vehicles face challenges in safely navigating varied road surface conditions due to the lack of effective consideration of surface friction data in motion planning, which can lead to traction limit violations and unsafe vehicle operations.
Innovation Solution
The implementation of a computer-implemented method that utilizes surface friction data, along with operating and comfort constraints, to generate motion plans for autonomous vehicles, ensuring they operate within traction limits and maintain safety by dynamically adjusting trajectories based on current road conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If motion planning is performed without considering surface friction data, then the motion planning system operates with simpler constraints and calculations, but the vehicle may violate traction limits and perform unsafe operations
Solution Approach 1:
The system performs preliminary estimation of surface friction coefficients before motion planning by analyzing vehicle dynamics data (longitudinal and lateral accelerations) and comparing them with tire friction models. This preliminary friction estimation is then used to adjust motion planning constraints, ensuring traction limits are respected without requiring complex real-time friction sensing during planning.
Solution Approach 2:
The patent introduces surface friction coefficient estimation as an intermediary parameter that bridges vehicle dynamics measurements and motion planning constraints. The friction coefficient acts as a mediator that translates raw acceleration data into meaningful traction limits for the motion planner, simplifying the overall system architecture while improving safety.
2Adaptability or versatility
If the vehicle operates without real-time friction data, then the system requires fewer sensors and processing resources, but cannot adapt to varying road surface conditions
Solution Approach 1:
The system uses the vehicle's existing motion sensors (accelerometers) to self-determine surface friction conditions by analyzing the relationship between commanded and actual vehicle dynamics. Rather than requiring external friction sensors, the system exploits its own operational data to estimate friction coefficients, achieving adaptability with minimal additional complexity.
Solution Approach 2:
The system implements feedback loops where vehicle acceleration measurements are continuously compared against tire friction models to update friction coefficient estimates. These updated estimates then feed back into motion planning constraints, creating a closed-loop system that adapts to changing road conditions using readily available vehicle data.
Data Source
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AI summary
Systems and methods are directed to motion planning for an autonomous vehicle. In one example, a computer-implemented method for road surface dependent motion planning includes obtaining, by a computing system comprising one or more computing devices, surface friction data. The method further includes determining, by the computing system, one or more constraints for motion planning based at least in part on the surface friction data. The method further includes generating, by the computing system, a motion plan for an autonomous vehicle based at least in part on the one or more constraints.