Longitudinal Vehicle Control Across Cruise, Parking, and Transition Modes
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Solution Overview
Problem
Existing autonomous driving technologies face challenges in ensuring reliable and stable longitudinal control of vehicles during point-to-point autonomous driving with integrated traveling and parking, which affects comfort and safety.
Innovation Solution
A method for longitudinal control of vehicles is introduced, which sets an intelligent driving control mode that includes parking, cruise, steady-state acceleration/deceleration, and transient-state acceleration/deceleration modes, based on planned driving trajectory information, to adaptively switch control modes and improve reliability and stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single control mode is used for all autonomous driving tasks, then the control system is simple, but the reliability and stability of longitudinal control cannot be ensured across different driving scenarios
Solution Approach 1:
The control system is segmented into multiple distinct control modes (parking mode, cruise control mode, steady-state acceleration/deceleration mode, transient-state acceleration/deceleration mode) that can be independently selected and activated based on the current driving scenario. This segmentation allows each mode to be optimized for specific tasks while maintaining overall system reliability through appropriate mode selection.
Solution Approach 2:
The control system dynamically switches between different control modes based on real-time driving conditions and task requirements. The mode selection mechanism enables the system to adapt its control strategy dynamically, ensuring optimal performance and stability across varying autonomous driving scenarios without requiring a completely different control system for each task.
2Reliability
If multiple control modes are implemented for different autonomous driving tasks, then the reliability and stability of longitudinal control are improved, but the control system complexity increases
Solution Approach 1:
The control system employs a universal mode selection framework that can handle multiple different autonomous driving tasks (high-speed cruising, vehicle following, automatic parking) through a single integrated system. The same control architecture and mode selection logic are used across all tasks, reducing overall system complexity despite supporting multiple specialized control modes.
Solution Approach 2:
The system manages complexity by changing control parameters (such as target speed, target acceleration, and control mode selection) based on the driving scenario rather than requiring fundamentally different control structures. This parameter-based adaptation allows the system to maintain reliability across different tasks while keeping the underlying control architecture relatively simple and unified.
3Adaptability or versatility
If control modes are switched frequently to adapt to different driving scenarios, then the adaptability is improved, but the stability during mode transitions may be affected
Solution Approach 1:
The control system performs preliminary evaluation of establishment conditions for each control mode before switching occurs. By assessing whether the current driving scenario meets the predefined conditions for a particular mode, the system can anticipate mode transitions and prepare appropriate control strategies in advance, ensuring smooth transitions that maintain stability while adapting to changing scenarios.
Solution Approach 2:
The mode selection mechanism incorporates feedback from the current driving state and task requirements to determine the appropriate control mode. This feedback-based selection ensures that mode transitions occur only when appropriate conditions are met, preventing unnecessary or destabilizing switches while maintaining high adaptability to different autonomous driving scenarios.
Data Source
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AI summary
The disclosure relates to the field of autonomous driving technologies, and specifically provides a method for longitudinal control of a vehicle, a computer device, a storage medium, and a vehicle, which are intended to improve the reliability and stability of longitudinal control of a vehicle. The method provided in the disclosure includes: sequentially evaluating establishment conditions for the parking mode, the cruise control mode, the steady-state acceleration/deceleration mode, and the transient-state acceleration/deceleration mode based on planned driving trajectory information of the vehicle, to determine an initial control mode; and performing longitudinal control of the vehicle according to the initial control mode and based on the planned driving trajectory information. The method enables adaptive and smooth switch between different modes based on the planned driving trajectory information when the vehicle is controlled for autonomous driving, covering various autonomous driving tasks such as high-speed cruising, vehicle following in urban areas, and automatic parking. Therefore, the reliability and stability of longitudinal control of the vehicle is significantly improved, thus improving the comfort and safety during driving of the vehicle.