Autonomous Travel Mode Switching Under External Abnormalities
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Solution Overview
Problem
Existing autonomous travel systems do not appropriately enable user-initiated movement operations based on situational urgency, leading to potential inefficiencies in managing transitions between autonomous and user-controlled modes.
Innovation Solution
An autonomous travel control system that includes a processor to manage modes (autonomous, stop, and user operation) and detect external abnormalities, allowing mode transitions based on external abnormality detection and center permissions, enabling quick user control when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If autonomous travel control systems are designed to handle complex real-world environments with diverse obstacles and dynamic conditions, then the system's adaptability and measurement precision improve, but the device complexity and computational requirements increase significantly
Solution Approach 1:
The autonomous travel control system divides the complex control task into multiple functional modules: perception module for detecting obstacles and environment, decision module for determining travel paths and speeds, and control module for executing motor commands. This segmentation allows each module to specialize in specific functions, improving overall adaptability while managing system complexity through modular architecture.
Solution Approach 2:
The system employs universal sensors and processors that can handle multiple types of detection and control tasks. The perception module can detect various obstacle types (static and dynamic), the decision module can generate different path types, and the control module can adjust to various travel conditions, enabling one system to adapt to diverse environments without requiring separate specialized components for each scenario.
2Measurement precision
If the system uses multiple sensors and complex algorithms to improve measurement precision and detection accuracy, then the reliability improves, but the use of energy and device complexity increase
Solution Approach 1:
The system implements periodic sensing and processing cycles rather than continuous operation. Sensors are activated at specific intervals to detect obstacles and environmental conditions, and the processor performs batch computations to update travel plans. This periodic operation maintains measurement precision for safe autonomous travel while significantly reducing energy consumption compared to continuous sensing and processing.
Solution Approach 2:
The system applies measurement and processing resources selectively based on operational needs. In low-risk environments with clear paths, the system uses reduced sensing frequency and simpler algorithms. In high-risk situations or when obstacles are detected, the system increases measurement precision and computational effort. This partial action approach maintains safety while optimizing energy usage by avoiding excessive processing in all conditions.
3Reliability
If the autonomous system continuously monitors and adjusts travel parameters to maintain high reliability and safety, then the reliability improves, but the loss of time and processing overhead increase
Solution Approach 1:
The system performs preliminary path planning and risk assessment before actual travel begins. The decision module pre-calculates multiple potential paths and identifies potential obstacles in advance, allowing the control system to execute pre-planned actions with minimal real-time adjustments. This preliminary action maintains high reliability by anticipating problems before they arise while reducing time loss during actual travel by minimizing on-the-fly computations.
Solution Approach 2:
The system implements selective monitoring that skips detailed analysis in low-risk situations. When the environment is stable and no obstacles are detected, the system reduces the frequency of comprehensive scans and relies on simpler continuation logic. When changes are detected or risks increase, the system intensifies monitoring and analysis. This skipping approach maintains reliability by focusing computational resources on critical moments while reducing time loss during safe, routine travel segments.
Data Source
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AI summary
The autonomous travel control system includes a processor and controls an autonomous travel device that is capable of communicating with a center and executing autonomous traveling. The processor is configured to determine whether to permit switching of control modes including an autonomous travel mode, a stop mode, and a user operation mode. The processor is configured to detect an external abnormality that occurs outside the autonomous travel device. The determination of whether to permit the switching includes permitting the switching from the stop mode to the user operation mode in response to acquisition of a permission regarding the switching from the stop mode to the user operation mode from the center when the external abnormality is not detected. The determination of whether to permit the switching includes permitting the switching from the stop mode to the user operation mode regardless of the acquisition of the permission when the external abnormality is detected.