Autonomous Vehicle Software Parameter Adjustment via Machine Programming
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Solution Overview
Problem
Existing methods for updating autonomous vehicle software lack a standardized approach, resulting in inconsistent updates across vehicles, as they apply the same updates regardless of varying conditions and preferences, leading to differing effectiveness in improving driving styles or fixing issues.
Innovation Solution
The use of machine programming to determine specific updates for autonomous vehicles based on inputs such as passenger preferences, driver profiles, and environmental conditions, allowing for individualized adjustments to driving parameters like speed and aggressiveness, with priority levels and real-time implementation options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the same software updates are applied to all autonomous vehicles regardless of conditions, then the update process is simple and standardized, but the effectiveness of improving driving styles and fixing issues varies across different vehicles
Solution Approach 1:
The system performs preliminary analysis of vehicle data, driving conditions, and performance metrics before applying software updates. This preliminary action enables the selection and customization of updates based on each vehicle's specific needs, ensuring that the right updates are applied to the right vehicles at the right time, thereby maximizing effectiveness while maintaining a standardized update framework
Solution Approach 2:
The system changes parameters of software updates based on vehicle-specific conditions such as driving style preferences, environmental factors, and performance data. By dynamically adjusting update parameters including which updates to apply, when to apply them, and how to configure them, the system achieves both standardization through a unified update mechanism and customization through parameter variation to suit individual vehicle needs
2Reliability
If autonomous vehicle software is updated frequently to improve performance, then driving style and safety are enhanced, but system complexity and update management become more difficult
Solution Approach 1:
The system implements continuous feedback loops that monitor vehicle performance, driving conditions, and update effectiveness. This feedback mechanism automatically identifies when updates are needed, what type of updates are most beneficial, and whether previous updates achieved desired outcomes. The feedback-driven approach streamlines update management by replacing complex manual decision-making with automated, data-driven update selection and scheduling
Solution Approach 2:
The autonomous vehicle system performs self-diagnosis and self-updating based on its own performance data and predefined criteria. The vehicle automatically determines when it needs software updates, selects appropriate updates from available options, and applies them without extensive human intervention. This self-service capability reduces update management complexity by empowering the vehicle to handle its own software maintenance based on real-time performance monitoring
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed that adjust autonomous vehicle driving software using machine programming. An example apparatus for adjusting autonomous driving software of a vehicle includes an input analyzer to determine a software adjustment based on an obtained driving input and a priority determiner to determine a priority level of the software adjustment. The apparatus further includes a program adjuster to, when the priority level is above a threshold, identify a parameter of the autonomous driving software of the vehicle associated with the software adjustment and adjust the parameter based on the software adjustment, the adjustment to the parameter to change driving characteristics of the vehicle.