Autonomous Driving Takeover Timing for Secondary Task Completion
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Solution Overview
Problem
When vehicles switch from autonomous driving to manual or driving assistance modes, drivers often face discomfort due to unfinished secondary tasks, as they need to abruptly shift attention from these tasks to driving.
Innovation Solution
A driving control device and vehicle behavior suggestion system that generates a second task priority plan, ensuring secondary tasks are conveniently finished before mode switching, allowing smooth attention transition and comfortable driving.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the vehicle switches from autonomous driving to manual driving mode, then the driver takes control of driving tasks, but the driver experiences discomfort due to unfinished secondary tasks and abrupt attention shift
Solution Approach 1:
The system performs preliminary actions by predicting the completion time of secondary tasks before mode switching occurs. It uses this prediction to determine the optimal timing for requesting driver takeover, ensuring that secondary tasks are completed or near-completion at the moment of mode transition. This prevents abrupt interruption of tasks and reduces driver discomfort during attention shift.
2Ease of operation
If the vehicle delays mode switching to allow secondary task completion, then driver comfort improves, but the responsiveness of the driving control system decreases
Solution Approach 1:
The system dynamically changes the parameter of mode switching timing based on the predicted completion time of secondary tasks. Instead of using a fixed or immediate switching trigger, the system adjusts the takeover request timing to coincide with task completion predictions. This parameter adaptation resolves the contradiction by making the switching speed context-dependent rather than constant.
3Measurement precision
If the system monitors and predicts secondary task completion, then task completion timing accuracy improves, but the system complexity increases
Solution Approach 1:
The system implements feedback by continuously monitoring the progress of secondary tasks and using this information to predict completion times. The prediction accuracy improves through feedback from actual task progression patterns. This feedback mechanism allows the system to adapt to different task types and driver behaviors, achieving high prediction accuracy without requiring overly complex hardware modifications.
Data Source
AI summary
A vehicle switches between an autonomous driving state, in which the vehicle is responsible for implementing driving tasks including vehicle steering, vehicle driving, vehicle braking, and periphery monitoring, and a manual driving state or a driving assistance state, in which a driver of the vehicle is responsible for implementing at least one of the driving tasks. A processor implements the driving tasks based on a vehicle behavior plan, which indicates a vehicle behavior scheduled in the autonomous driving state, and generates a second task priority plan, which is the vehicle behavior plan until the driving mode is switched from the autonomous driving state to the manual driving state, such that a convenient finish time, at which a second task implemented by the driver in the autonomous driving state is conveniently finished, comes before the driving mode switching.