Autonomous Vehicle Control Profiles for Driver-Specific Risk Response
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Solution Overview
Problem
Autonomous vehicles lack personalized control algorithms that adapt to individual drivers' responses to driving incidents, leading to suboptimal vehicle management and insurance cost determination.
Innovation Solution
A system that uses processors to receive driver inputs on anticipated responses to driving scenarios, generate driver-specific algorithms, and control the vehicle accordingly, while also determining insurance costs based on these inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single standardized control algorithm is used for all autonomous vehicles, then device complexity is reduced and ease of manufacture is improved, but adaptability to individual driver preferences and conditions deteriorates
Solution Approach 1:
The control algorithm is segmented into a standardized base layer and customizable driver-specific layers. The system divides the algorithmic structure into modular components that can be independently configured based on driver preferences, allowing adaptability without requiring complete algorithm redesign for each driver.
Solution Approach 2:
Driver preferences and characteristics are collected and processed in advance through surveys, interviews, or initial driving sessions. This preliminary action creates pre-configured driver profiles that are stored and applied before actual driving occurs, enabling personalized control without real-time complexity.
2Adaptability or versatility
If driver-specific algorithms are generated and implemented, then adaptability to individual drivers is improved, but loss of time for data collection and processing increases
Solution Approach 1:
The system collects only the most critical driver preference parameters rather than comprehensive data, using partial action to achieve sufficient personalization without excessive time investment. Key preferences such as risk tolerance, comfort levels, and primary objectives are captured while less critical details are omitted or inferred.
3Measurement precision
If traditional insurance pricing models are used, then simplicity in pricing is maintained, but measurement precision of actual driver risk deteriorates
Solution Approach 1:
The insurance pricing system incorporates continuous feedback loops where driver performance data, preference adherence, and actual driving outcomes are monitored and fed back into the pricing model. This allows dynamic adjustment of premiums based on measured risk rather than static traditional factors, improving measurement precision through iterative refinement.
4Measurement precision
If comprehensive driver input collection is implemented, then measurement precision of driver preferences is improved, but device complexity and ease of operation worsen
Solution Approach 1:
The system enables drivers to input their own preferences through user-friendly interfaces where they can review, modify, and confirm their profiles at any time. This self-service approach reduces the burden on the system to actively collect and manage detailed preference data, improving ease of operation while maintaining measurement precision through driver-controlled input.
Data Source
AI summary
A system for use with an autonomous vehicle includes one or more processors configured to receive one or more inputs from a driver and to control the autonomous vehicle based on the one or more inputs. Each input is indicative of an anticipated driver response to a driving incident.