Cloud Vehicle Remote Control With Adaptive Driving Mode Switching
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Solution Overview
Problem
Existing intelligent driving systems struggle to handle complex driving scenarios and lack efficient methods for accurate vehicle control beyond basic steering, braking, and acceleration, leading to inefficient task completion.
Innovation Solution
A vehicle remote control method that adapts to different vehicle control modes based on network and running status information, including remote driving, navigation, and monitoring modes, utilizing a cloud cockpit to simulate vehicle functions and issue precise control instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If remote driving control is used with basic steering wheel, brake pedal, and accelerator pedal, then the vehicle can be controlled remotely, but the control precision is insufficient and task completion efficiency is low
Solution Approach 1:
The patent implements dynamic switching between multiple control modes (manual driving mode, navigation mode, monitoring mode) based on network status and running status. The cloud control unit adaptively adjusts the control strategy, transitioning from basic pedal/steering control to more precise control methods when conditions permit, thereby improving both control precision and task completion efficiency
Solution Approach 2:
The system changes control parameters by selecting different control modes according to network quality and vehicle status. When network status is good and positioning accuracy is sufficient, the system switches to navigation mode or monitoring mode which provide more precise control, thus resolving the contradiction between control precision and task completion efficiency
2Adaptability or versatility
If multiple control modes are implemented to improve control precision, then the vehicle control adaptability increases, but the system complexity increases
Solution Approach 1:
The cloud control unit automatically determines the appropriate control mode by evaluating network status information and running status information without requiring manual intervention. The system self-adjusts between control modes based on real-time conditions, providing adaptability while keeping the operation simple and avoiding the complexity that would arise from manual mode selection
Solution Approach 2:
The system continuously monitors network status and vehicle running status, using this feedback to dynamically select the most appropriate control mode. This feedback mechanism enables the system to adapt to changing conditions automatically, achieving high adaptability through a relatively simple decision-making process rather than complex manual control
3Measurement precision
If remote monitoring and path planning algorithms are added to improve control accuracy, then the positioning precision increases, but the information processing requirements increase
Solution Approach 1:
The system implements path planning and monitoring algorithms selectively based on whether they are available and needed, rather than always using them. When path planning algorithms are available and positioning accuracy is sufficient, the system uses these enhanced features to improve precision. This partial implementation approach achieves higher positioning accuracy when possible while avoiding the constant information processing load of always-running complex algorithms
Data Source
AI summary
The disclosure relates to a vehicle remote control method, applied to a cloud control unit, where the method includes: acquiring network status information and running status information of a vehicle; in a case that the network status information indicates that the vehicle meets a remote control requirement, determining a vehicle control mode adapted to the running status information, where the vehicle control mode includes a remote driving vehicle control mode, a remote navigation vehicle control mode, or a remote monitoring vehicle control mode, and a control range of the cloud control unit increases in turn among the remote driving vehicle control mode, the remote navigation vehicle control mode, and the remote monitoring vehicle control mode; and performing control operation on the vehicle based on the vehicle control mode.