Autonomous Driving Personalization Using Driver Style Parameters
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Solution Overview
Problem
Traditional autonomous vehicle (AV) systems lack personalization and fail to adapt to individual driver preferences and regional driving styles, leading to a disconnect between the AV's driving behavior and the driver's expectations.
Innovation Solution
A vehicle control system that utilizes a processor and memory to receive historical data, population data, and speed data, which are then input into a machine learning model, such as a neural network, to determine a driver's style and apply relevant parameters to the vehicle's automated driving system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional AV systems use standardized driving parameters, then system complexity is reduced and ease of operation is improved, but adaptability to individual driver preferences and regional styles deteriorates
Solution Approach 1:
The system dynamically adjusts driving parameters based on real-time analysis of driver behavior and regional characteristics. The machine learning model continuously learns from driver inputs and operational data, transforming static standardized parameters into dynamic adaptive parameters that evolve with user preferences and regional driving styles.
Solution Approach 2:
The system changes multiple driving parameters simultaneously including acceleration profiles, braking patterns, following distances, and lane-changing behaviors. These parameter changes are coordinated to create a cohesive driving style that matches both individual driver preferences and regional characteristics, resolving the contradiction between standardization and personalization.
2Adaptability or versatility
If AV systems collect and analyze extensive driver data, then adaptability to individual preferences improves, but device complexity and data processing requirements worsen
Solution Approach 1:
The system segments driver behavior into distinct categorical styles (e.g., aggressive, conservative, moderate) rather than attempting to model every nuanced parameter individually. This segmentation simplifies the complexity by grouping similar behaviors together while still capturing the essence of individual driving preferences through the machine learning model.
Solution Approach 2:
The machine learning model performs self-training and self-adjustment by automatically learning from driver behavior patterns without requiring manual configuration or complex external processing systems. The system serves itself by autonomously adapting parameters based on collected data, reducing the need for complex external data processing infrastructure.
Data Source
AI summary
Systems and methods are provided for personalizing autonomous driving. The system can receive historical data on a driver of the vehicle's performance and population data indicating a population driving style. Speed data can be recorded as the driver of the vehicle drives the vehicle during a trial period. The historical data, population data, and speed data can be input into a machine learning model to determine a style for the driver. The system can receive one or more parameters from the machine learning model indicating the style. These parameters can be applied to the vehicle's automated driving system.