Vehicle Control Rights Arbitration Under Controller Abnormal States
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Solution Overview
Problem
Existing autonomous driving systems fail to effectively manage vehicle control when controllers or communication systems experience abnormal states, leading to potential safety risks and loss of control.
Innovation Solution
A system that analyzes the state of multiple vehicle controllers and communication modules, learns their performance, and determines abnormal states to dynamically allocate control rights between autonomous and manual driving modes based on the severity of the abnormality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the autonomous driving system relies on multiple controllers and communication modules, then the functionality and capability of the system are improved, but the reliability deteriorates when controller or communication abnormalities occur
Solution Approach 1:
The system dynamically adjusts control rights allocation based on real-time controller states and communication conditions. When abnormalities are detected in controllers or communication modules, the system transitions control from autonomous mode to manual mode, ensuring continuous reliable operation despite component failures
Solution Approach 2:
The vehicle controller acts as an intermediary that monitors the states of multiple controllers and communication modules, learns their performance characteristics, and determines abnormal states. This intermediary coordinates between the autonomous driving functions and manual control, maintaining system reliability through intelligent arbitration
2Reliability
If the system continuously monitors and analyzes controller states to ensure safety, then the reliability is improved, but the computational complexity and processing requirements increase
Solution Approach 1:
The system performs preliminary learning of controller states during normal operation, building knowledge bases about controller performance and communication patterns before abnormalities occur. This preliminary action enables faster and more accurate abnormal state detection when issues arise, reducing the complexity of real-time decision-making
Solution Approach 2:
The system implements continuous feedback loops that monitor controller states, communication quality, and system performance. This feedback mechanism enables the vehicle controller to learn from ongoing operations and automatically adjust control strategies, maintaining safety without requiring overly complex manual intervention systems
Data Source
AI summary
Disclosed is a system for vehicle control. The system analyzes a state of each controller of multiple controllers inside a vehicle based on information collected from the controllers, learns the state of the each controller, based on a state analysis result obtained by analyzing the state of the each controller, and determines an abnormal state for at least one of the controllers, based on the state analysis result for the each controller and a learning result obtained by learning the state of the each controller. The system also includes a vehicle controller that manages vehicle control rights, based on the abnormal state for the at least one of the one or more controllers.