Driver Take-Over Time Estimation for ODD Exit Handover
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Solution Overview
Problem
Current autonomous driving systems lack effective solutions for safely transitioning control from autonomous to semi-autonomous or manual driving, as they are not fully capable in all scenarios, and there is a need for accurate estimation of take-over time to ensure driver readiness and vehicle safety.
Innovation Solution
A system comprising a trained Action Time Network (ATN) and Recovery Time Network (RTN) that estimates the time needed for a driver to take over vehicle control, using driver status parameters and environmental data to generate hand-over request signals and update networks based on actual recovery times for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If autonomous driving systems are deployed without accurate take-over time estimation, then automation extent is improved, but reliability deteriorates due to unsafe transitions
Solution Approach 1:
The system performs preliminary estimation of take-over time before the actual handover occurs. The ATN and RTN continuously monitor driver status and predict the time required for safe transition, allowing the system to prepare appropriate handover strategies in advance and initiate driver alerts with sufficient lead time.
Solution Approach 2:
The system implements continuous feedback loops where the ATN estimates action time based on scenario data, the RTN estimates recovery time based on real-time driver status parameters, and these estimates are continuously refined by comparing predicted versus actual take-over times. This feedback mechanism ensures reliable and safe transitions by adapting to actual driver responses.
2Reliability
If complex monitoring systems are implemented to ensure driver readiness, then reliability is improved, but device complexity increases
Solution Approach 1:
The monitoring system is segmented into two independent but complementary networks: the ATN responsible for estimating action time based on driving scenarios, and the RTN responsible for estimating recovery time based on driver status. This segmentation allows each network to specialize in specific aspects of take-over estimation, improving reliability while keeping individual components manageable in complexity.
Solution Approach 2:
The dual-network architecture serves multiple functions: the ATN handles scenario assessment and action time prediction, the RTN handles driver status monitoring and recovery time prediction, and together they provide comprehensive take-over time estimation, handover strategy selection, and system optimization capabilities through a unified framework.
3Measurement precision
If real-time driver status monitoring is implemented, then take-over time estimation accuracy is improved, but use of energy increases
Solution Approach 1:
The system implements partial monitoring by focusing computational resources on the most critical driver status parameters that have the greatest impact on take-over time estimation, such as attentiveness level, response time, and task completion status. The DMS selectively monitors these key parameters rather than continuously tracking all possible driver states, reducing energy consumption while maintaining sufficient estimation accuracy.
Solution Approach 2:
The system dynamically adjusts the level of monitoring intensity and computational processing based on the current driving scenario and driver status. When the driver is already highly attentive or the scenario is low-risk, the system reduces monitoring frequency and processing depth. When the driver shows signs of distraction or the scenario becomes critical, the system increases monitoring intensity, optimizing the balance between measurement precision and energy consumption.
Data Source
AI summary
The present disclosure relates to systems and methods capable of adaptively estimating time-to-take over during ODD exit events, by estimating the recovery time for driver and the action time required to safely handle the situation. In more detail, the proposed system allows for field monitoring for online verification (i.e., in-vehicle verification) of adaptive hand over time, and for facilitated updating of the systems predicting the hand-over time (i.e., Action Time Network and Reaction Time Network) in a decoupled manner by efficient use of data from field monitoring.