Self-Driving Vehicle Mode Switching by Driver-Processor Competence
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Self-driving vehicles (SDVs) face challenges in dynamically switching between autonomous and manual modes based on the competence levels of their control processors and human drivers, particularly in handling operational anomalies and varying road conditions, which can impact safety and efficiency.
Innovation Solution
A computer-implemented method and system that assesses the competence levels of both the SDV control processor and the human driver by comparing their abilities to handle current operational anomalies, selectively assigning control to either the processor or the driver based on which is more competent, and adjusting the driving mode accordingly to optimize safety and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the SDV operates in autonomous mode with control processor, then operational precision and consistency are improved, but adaptability to novel anomalies and ease of operation may deteriorate
Solution Approach 1:
The system dynamically switches between autonomous and manual modes based on real-time assessment of operational anomalies and competence comparison. The driving mode is not fixed but adapts dynamically to current conditions, allowing the SDV to leverage automated precision when available and human adaptability when needed.
Solution Approach 2:
The competence assessment system acts as an intermediary that evaluates both the control processor's ability to handle current anomalies and the human driver's competence level, then mediates the mode selection decision. This intermediary mechanism enables intelligent arbitration between automated and manual control based on situational appropriateness.
2Adaptability or versatility
If the SDV switches to manual mode with human driver, then adaptability and handling of novel situations are improved, but operational precision and response consistency may deteriorate
Solution Approach 1:
The system employs dynamic mode switching rather than static assignment, allowing transition between manual and autonomous control based on real-time conditions. This dynamic approach ensures that manual adaptability is utilized when facing novel situations while maintaining automated precision during normal operations.
Solution Approach 2:
The competence assessment mechanism provides continuous feedback about the relative capabilities of the control processor and human driver, enabling informed decisions about mode selection. This feedback loop ensures that the system maintains optimal control precision by selecting the more competent controller for current conditions.
3Reliability
If the system continuously assesses competence levels and switches modes, then reliability and safety are improved, but device complexity and computational load increase
Solution Approach 1:
The system performs self-assessment of the control processor's competence level and automatically compares it with the human driver's competence without requiring external intervention. This self-service capability enables the system to autonomously determine the appropriate driving mode, improving reliability while managing complexity through automated decision-making.
Solution Approach 2:
The system changes operational parameters (driving mode) based on assessed competence levels. By monitoring and responding to changes in competence parameters, the system dynamically adjusts its operational state to maintain safety and reliability without requiring overly complex hardwired control logic.
4Productivity
If the SDV remains in autonomous mode during operational anomalies, then operational efficiency is maintained, but safety and effectiveness may deteriorate
Solution Approach 1:
The competence assessment system continuously monitors the control processor's ability to handle operational anomalies and provides feedback that triggers mode switching when human competence is determined to be superior. This feedback mechanism ensures safety is prioritized over efficiency when the situation warrants human intervention.
Solution Approach 2:
The driving mode transitions dynamically based on real-time competence assessment rather than remaining statically in autonomous mode. This dynamic adaptation allows the system to maintain efficiency during normal operations while switching to manual mode when safety concerns arise, balancing productivity and reliability.
Data Source
AI summary
A computer-implemented method, system, and/or computer program product controls a driving mode of a self-driving vehicle (SDV). One or more processors compare a control processor competence level of an on-board SDV control processor in controlling the SDV to a human driver competence level of a human driver in controlling the SDV while the SDV encounters a current roadway condition which is a result of current weather conditions of the roadway on which the SDV is currently traveling. One or more processors then selectively assign control of the SDV to the SDV control processor or to the human driver while the SDV encounters the current roadway condition based on which of the control processor competence level and the human driver competence level is relatively higher to one another.