Automatic Driver Modeling for Autonomous Vehicle Network Integration
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Solution Overview
Problem
The integration of human-controlled vehicles into autonomous vehicle networks is challenging due to the lack of standard autonomous interfaces, limiting real-time communication and interaction between autonomous vehicles and human-controlled vehicles.
Innovation Solution
An automatic driver modeling system that uses sensors to identify and generate a model of a human driver's behavior patterns, which is then transmitted to nearby vehicles with autonomous interfaces and remote servers, enabling efficient communication and interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If human-controlled vehicles are integrated into autonomous vehicle networks without standard interfaces, then vehicle diversity and adaptability are maintained, but communication efficiency and interaction reliability deteriorate
Solution Approach 1:
The patent introduces an intermediary translation layer that converts human driver behavior patterns into standardized autonomous vehicle interface protocols. Sensors detect driver actions (steering, braking, acceleration patterns) and translate them into structured data formats that autonomous vehicles can process, enabling reliable communication without requiring standard interfaces in human-controlled vehicles
Solution Approach 2:
The system transforms qualitative driver behavior characteristics into quantitative parameters that can be transmitted and processed. Driver behavior is measured across multiple dimensions (reaction time, steering angle, speed variations) and converted into standardized parameter sets that maintain communication reliability while preserving vehicle diversity
2Measurement precision
If real-time behavior pattern measurement is implemented for all drivers, then driver identification accuracy and model precision improve, but system complexity and measurement resource requirements increase
Solution Approach 1:
The patent employs universal sensor modules that can be installed in any human-controlled vehicle to measure driver behavior patterns. These multi-functional sensors capture multiple behavior parameters (steering, braking, acceleration) simultaneously using the same hardware platform, reducing overall system complexity while maintaining high measurement precision
Solution Approach 2:
Instead of directly monitoring all driver actions with complex systems, the patent creates simplified behavioral models that copy essential driver characteristics. These models replicate key behavior patterns using reduced sensor sets and simplified algorithms, achieving accurate driver identification without proportionally increasing system complexity
3Productivity
If driver behavior models are continuously updated and transmitted to multiple vehicles, then network coordination and traffic flow efficiency improve, but data transmission volume and processing time increase
Solution Approach 1:
The system extracts only the essential and changing behavior parameters from complete driver models before transmission. Instead of transmitting full behavioral datasets, only critical updates (changes in driving patterns, significant behavior modifications) are extracted and communicated to the network, reducing transmission time while maintaining traffic flow efficiency
Solution Approach 2:
The patent implements partial model transmission where only necessary portions of driver behavior models are updated and shared across the network. Rather than continuously transmitting complete models, the system performs selective partial updates based on behavior significance and network needs, reducing data transmission overhead while preserving coordination benefits
Data Source
AI summary
Automatic driver modeling is used to integrate human-controlled vehicles into an autonomous vehicle network. A driver of a human-controlled vehicle is identified based on behavior patterns of the driver measured by one or more sensors of an autonomous vehicle. A model of the driver is generated based on the behavior patterns of the driver measured by the one or more sensors of the autonomous vehicle. Previously stored behavior patterns of the driver are then retrieved from a database to augment the model of the driver. The model of the driver is then transmitted from the autonomous vehicle to nearby vehicles with autonomous interfaces.