Self-driving vehicle mode control via competence assessment
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Solution Overview
Problem
Self-driving vehicles (SDVs) face challenges in determining the optimal operating mode between autonomous and manual control, as existing systems lack a robust method to assess and switch between modes based on the competence levels of both the on-board control processor and the human driver, leading to potential safety and efficiency issues.
Innovation Solution
A computer-implemented method that assesses the competence levels of the on-board SDV control processor and the human driver by analyzing historical data and real-time sensor readings, selectively assigning control to either the processor or the driver based on which is more competent to navigate the current roadway conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autonomous mode is used, then productivity is improved, but reliability deteriorates under certain roadway conditions
Solution Approach 1:
The system dynamically switches between autonomous and manual control modes based on real-time assessment of roadway conditions and competence levels. The control mode is not fixed but adapts to changing environmental factors, ensuring optimal safety and efficiency for each specific situation.
Solution Approach 2:
The system continuously monitors roadway conditions through sensors and compares the competence levels of the control processor and human driver in real-time. This feedback mechanism allows the system to assess whether autonomous or manual control is more appropriate for current conditions and switch modes accordingly.
2Reliability
If manual mode is used, then reliability is improved under certain conditions, but productivity deteriorates
Solution Approach 1:
The system allows dynamic transition between manual and autonomous modes based on real-time competence assessment. When human driver competence is determined to be higher for specific roadway conditions, manual control is enabled, but the system can switch back to autonomous mode when processor competence becomes superior.
Solution Approach 2:
Continuous monitoring of roadway conditions and competence levels provides feedback that determines when manual control should be preferred over autonomous control, allowing the system to optimize for safety when human judgment is superior while maintaining efficiency when automated control is better.
3Reliability
If competence assessment system is implemented, then reliability is improved, but device complexity increases
Solution Approach 1:
The competence assessment system is segmented into distinct functional modules: sensor modules for data collection, processing modules for analyzing roadway conditions, and comparison modules for evaluating competence levels. This modular architecture manages complexity by dividing the assessment function into manageable, independent components.
Solution Approach 2:
The system introduces an intermediary competence assessment mechanism that mediates between the autonomous control processor and manual driver control. This intermediary layer objectively evaluates both control options based on roadway conditions and selects the superior option, reducing the complexity of direct human-automated control arbitration.
Data Source
AI summary
A computer-implemented method, system, and/or computer program product controls a driving mode of a self-driving vehicle (SDV). Sensor readings describe a current condition of a roadway, which is part of a planned route of a self-driving vehicle (SDV). One or more processors compare a control processor competence level of the on-board SDV control processor that autonomously controls the SDV to a human driver competence level of a human driver in controlling the SDV under the current condition of the roadway. One or more processors then selectively assign control of the SDV to the on-board SDV control processor or to the human driver based on which of the control processor competence level and the human driver competence level is relatively higher to the other.