Cognitive Control Transfer for Autonomous Vehicles
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Solution Overview
Problem
Current transportation systems face challenges such as congested roads, inefficient public transportation, and driver fatigue, leading to increased stress and safety risks, particularly in autonomous and semi-autonomous vehicles where control transfer between human operators and vehicles is not effectively managed.
Innovation Solution
The implementation of in-vehicle sensors to collect cognitive state data, including facial, audio, and biosensor data, which is analyzed using processors to determine a cognitive scoring metric, allowing for the transfer of control between the vehicle and the individual based on both the vehicle's operational state and the driver's condition, utilizing machine learning to initiate control changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If control transfer between human operators and autonomous vehicles is implemented, then transportation safety is improved, but system complexity increases due to multiple sensors and processors required for cognitive state monitoring
Solution Approach 1:
The system segments the control transfer functionality into distinct modules: cognitive state monitoring module, operational state monitoring module, and control transfer execution module. Each module handles specific aspects of the control transfer process, making the overall system more manageable and maintainable while ensuring comprehensive safety monitoring
Solution Approach 2:
The processors and sensors are designed to serve multiple functions: they monitor both cognitive state and operational state simultaneously, and can trigger different control transfer decisions based on the combination of states detected. This multi-functionality reduces the need for separate dedicated systems for each monitoring task
2Measurement precision
If in-vehicle sensors collect cognitive state data in real-time, then driver condition monitoring is improved, but energy consumption increases due to continuous data collection and processing
Solution Approach 1:
Instead of continuous monitoring at maximum intensity, the system implements periodic sampling of cognitive state data at strategically determined intervals. The monitoring frequency is adjusted based on the current operational state and detected trends in driver condition, maintaining high measurement precision when needed while reducing energy consumption during stable periods
Solution Approach 2:
The cognitive state monitoring system uses the vehicle's existing processors and power infrastructure, leveraging available computational resources rather than requiring dedicated high-power monitoring hardware. The system self-regulates its data collection intensity based on detected driver state changes, consuming more energy only when driver condition deteriorates
3Measurement precision
If control transfer decisions are based on both vehicle operational state and driver cognitive state, then control accuracy is improved, but information processing requirements increase
Solution Approach 1:
The system applies different levels of analysis to different aspects of the data: operational state data receives standard processing, while cognitive state data undergoes more intensive analysis when specific triggers are detected. This localized intensity of processing ensures high control accuracy for critical decisions while avoiding unnecessary processing of routine operational parameters
Solution Approach 2:
The system performs preliminary filtering and preprocessing of both operational and cognitive state data before combining them for control transfer decisions. This preliminary action identifies and eliminates redundant information early in the processing chain, reducing the computational burden of integrating multiple data sources while maintaining the accuracy needed for safe control transfer
Data Source
AI summary
Techniques for cognitive analysis for directed control transfer with autonomous vehicles are described. In-vehicle sensors are used to collect cognitive state data for an individual within a vehicle which has an autonomous mode of operation. The cognitive state data includes infrared, facial, audio, or biosensor data. One or more processors analyze the cognitive state data collected from the individual to produce cognitive state information. The cognitive state information includes a subset or summary of cognitive state data, or an analysis of the cognitive state data. The individual is scored based on the cognitive state information to produce a cognitive scoring metric. A state of operation is determined for the vehicle. A condition of the individual is evaluated based on the cognitive scoring metric. Control is transferred between the vehicle and the individual based on the state of operation of the vehicle and the condition of the individual.