Driver Attention Monitoring via Head-Object Motion in Autonomous Handover
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Solution Overview
Problem
Existing vehicle control systems lack effective methods to determine a user's attention level during non-driving tasks, which is crucial for transitioning from autonomous to manual driving modes, particularly in high automation levels.
Innovation Solution
A control system that utilizes sensors to track user and object movements, determining relative movement parameters to assess attention levels and output control signals for engaging the user with the driving task based on these parameters, employing machine-learning models for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If autonomous driving mode is implemented with high automation levels, then driving convenience and reduced operational burden are improved, but the ability to monitor user attention and ensure timely manual intervention deteriorates
Solution Approach 1:
The system continuously monitors user head movements and provides feedback about attention levels to the control system. This feedback loop enables the system to detect when the user is disengaged from the driving task and automatically request intervention, resolving the contradiction by maintaining both autonomous operation and attention monitoring capability
Solution Approach 2:
The system uses the user's own head movements as the monitoring mechanism, eliminating the need for separate complex monitoring equipment. The user's natural head movements during engagement with non-driving tasks serve as the indicator of attention level, allowing the system to self-monitor without adding operational burden
2Reliability
If the system requests user intervention frequently, then safety and attention monitoring are improved, but user convenience and operational smoothness deteriorate
Solution Approach 1:
The system applies partial monitoring by focusing specifically on head movement patterns rather than comprehensive user behavior analysis. This selective approach provides sufficient safety monitoring without excessive intervention requests, maintaining operational smoothness while ensuring safety through targeted attention detection
3Measurement precision
If comprehensive user behavior monitoring is implemented, then attention detection accuracy is improved, but system complexity and computational requirements worsen
Solution Approach 1:
The system extracts only the essential feature for attention monitoring - head movement patterns - from the complex set of all possible user behaviors. By isolating this specific indicator, the system achieves accurate attention detection without the computational burden of analyzing comprehensive user behavior, resolving the contradiction between precision and complexity
Data Source
AI summary
Aspects of the present disclosure relate to a control system, a system, a method, a vehicle and a non-transitory computer readable medium for receiving user movement data indicative of movement of a user's head; receiving object movement data indicative of movement of an object, the object being associated with a non-driving task; determining one or more relative movement parameters indicative of the relative movement of the user's head with respect to the object based at least in part on the received user movement data and the object movement data; and determining an attention level of the user to the non-driving task based at least in part on the determined relative movement parameter.