Autonomous Vehicle Control Handover Based on Driver Alertness
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Solution Overview
Problem
Autonomous vehicles face challenges in transitioning control from autonomous to manual mode, particularly due to human operator alertness levels and situational demands, which can lead to safety risks if the human operator is not adequately prepared or alert.
Innovation Solution
The system assesses human operator alertness through sensors and historical data, tailoring notifications to the operator based on their alertness level and the urgency of the transition, ensuring that the operator is appropriately prepared to assume control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If control is transferred to a human operator who has been traveling in the vehicle, then the vehicle can navigate under circumstances where autonomous navigation is not possible, but the human operator may be inattentive to road conditions and unprepared to make decisions and exercise motor control
Solution Approach 1:
The system performs preliminary assessment of the human operator's alertness level using sensors before control transfer is initiated. This advance evaluation allows the system to determine whether the operator is ready to assume control, preventing unsafe transitions and ensuring the operator is prepared to navigate under adverse conditions.
Solution Approach 2:
The system continuously monitors the human operator's alertness level through sensors and provides feedback about the operator's readiness state. This feedback mechanism enables real-time assessment and allows the system to adjust control transfer decisions based on the operator's current condition, improving safety during transitions.
2Extent of automation
If the vehicle equips sensors and processing capabilities for autonomous navigation, then the vehicle can navigate without human intervention, but the vehicle lacks capability to navigate under adverse or extreme weather conditions, sensor loss, or disaster conditions
Solution Approach 1:
The system dynamically adjusts the level of autonomous navigation capability based on environmental conditions and operational context. When adverse conditions are detected, the system transitions from fully autonomous mode to manual control mode, allowing the vehicle to adapt to circumstances where autonomous navigation is insufficient or unavailable.
Solution Approach 2:
The vehicle system integrates both autonomous navigation capabilities and manual control interfaces into a single multi-functional platform. This allows the vehicle to operate autonomously under normal conditions while being capable of switching to manual control under adverse conditions, achieving versatility across different operational scenarios.
3Reliability
If the system presents notifications to the user based on alertness level, then the operator can be appropriately prepared to assume control, but the notification system must be tailored to specific operators based on preference and historical performance
Solution Approach 1:
The notification system performs preliminary tailoring based on stored operator preferences and historical performance data before presenting alerts. This advance customization allows the system to deliver appropriately calibrated notifications that match each operator's characteristics, improving preparedness without requiring complex real-time adjustments.
Solution Approach 2:
The system automatically adapts notification parameters based on historical performance data and operator preferences without requiring manual configuration. This self-service approach allows the notification system to become increasingly optimized for each operator over time, reducing the perceived complexity while maintaining high reliability.
Data Source
AI summary
Vehicles may have the capability to navigate according to various levels of autonomous capabilities, the vehicle having a different set of autonomous competencies at each level. In certain situations, the vehicle may shift from one level of autonomous capability to another. The shift may require more or less driving responsibility from a human operator. Sensors inside the vehicle collect human operator parameters to determine an alertness level of the human operator. An alertness level is determined based on the human operator parameters and other data including historical data or human operator-specific data. Notifications are presented to the user based on the determined alertness level that are more or less intrusive based on the alertness level of the human operator and on the urgency of an impending change to autonomous capabilities. Notifications may be tailored to specific human operators based on human operator preference and historical performance.