Autonomous Driving Control Tuned to Driver Behavior Patterns
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Solution Overview
Problem
Autonomous vehicles often operate in a style that differs from individual drivers, leading to an uncomfortable and unfamiliar experience for human passengers, which can lower confidence and interest in using autonomous vehicles.
Innovation Solution
A system and method that objectively measure a driver's actual driving behaviors in manual mode, allowing a self-driving vehicle to mimic these behaviors within limits, thereby providing a more familiar and comfortable driving experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles operate with standardized driving programming, then safety and reliability are improved, but driver comfort and familiarity deteriorate due to unnatural driving style
Solution Approach 1:
The system dynamically adjusts the autonomous vehicle's driving behavior by switching between multiple driving styles (conservative, moderate, aggressive) based on real-time analysis of the driver's actual behavior patterns. This allows the vehicle to adapt its characteristics continuously rather than operating with fixed programming, resolving the contradiction between standardized safety protocols and personalized comfort.
Solution Approach 2:
The system changes key driving parameters such as acceleration rates, braking force, steering angle, and following distance to match the driver's preferred style. By adjusting these parameters dynamically based on collected behavioral data, the vehicle maintains safety while replicating the driver's natural driving characteristics, thereby improving comfort without sacrificing reliability.
2Device complexity
If autonomous vehicles use uniform driving behavior programming, then system complexity is reduced, but adaptability to individual driver preferences deteriorates
Solution Approach 1:
The system segments driving behavior into distinct measurable parameters (acceleration, braking, steering, following distance) and processes each parameter independently. This segmentation allows the complex task of mimicking driver behavior to be broken down into manageable components, reducing overall system complexity while maintaining high adaptability to individual preferences.
Solution Approach 2:
The system automatically collects driving data, analyzes behavioral patterns, and adjusts its programming without requiring manual configuration by the driver. This self-service approach eliminates the need for complex setup procedures while maintaining high adaptability, as the system autonomously learns and adapts to each driver's preferences.
3Measurement precision
If autonomous vehicles collect and analyze extensive driver behavior data, then customization accuracy is improved, but data processing requirements and system complexity increase
Solution Approach 1:
The system extracts only the most relevant driving behavior parameters (acceleration, braking, steering angle, following distance) from the vast amount of available sensor data, discarding redundant information. This extraction approach maintains high customization accuracy by focusing on key behavioral indicators while significantly reducing data processing requirements and system complexity.
Solution Approach 2:
The system collects more data than strictly necessary during the learning phase to ensure accurate characterization of driver behavior, then uses statistical methods to identify and retain only the most significant patterns. This partial action approach ensures high measurement precision while managing data processing loads by filtering out excessive redundant information.
Data Source
AI summary
A system and method for measuring a driver's actual driving behaviors (e.g., acceleration, deceleration) in a manual driving mode to determine their preferred driving style, and then causing an autonomous or semi-autonomous vehicle to operate itself, within limits, in accordance with the drivers' driving style when operating in a self-driving mode, thereby providing a more familiar and comfortable driving experience for the driver. Data is collected on the actual driving behavior, any pre-existing data is accessed on the actual driving behavior, and the collected data and the pre-existing data are combined. A custom control is then created based upon the combined data, and the custom control is applied to manage the self-driving behavior of the autonomous or semi-autonomous vehicle in a self-driving mode. Additional data continues to be collected on the actual driving behavior, and the custom control is adjusted based upon the collected additional data.