Yaw Moment Distribution for Autonomous Vehicle Path Tracking
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Solution Overview
Problem
Current autonomous driving systems face challenges in effectively controlling the lateral movement of vehicles within traffic lanes, particularly in achieving desired yaw moments for path tracking, due to limitations in torque vectoring techniques and configurations.
Innovation Solution
The method involves determining a desired yaw moment and identifying achievable yaw moment changes using different torque vectoring techniques, selecting the appropriate technique, and applying it to create lateral movement of the ego vehicle through processing devices and machine-readable instructions, which can include processing information like estimated paths and nonlinear kinematics to implement direct yaw moment control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple torque vectoring techniques are used to achieve desired yaw moment, then path tracking precision is improved, but system complexity increases
Solution Approach 1:
The patent segments the yaw moment control into multiple independent torque vectoring techniques (steering torque vectoring, braking torque vectoring, drive torque vectoring). Each technique can be independently activated based on vehicle operating conditions, allowing precise path tracking while managing system complexity through modular control architecture
Solution Approach 2:
The system dynamically selects and switches between different torque vectoring techniques based on real-time vehicle state (speed, steering angle, brake application). This dynamic adaptation allows the system to use only the necessary techniques for current conditions, optimizing the balance between precision and complexity
2Speed
If torque vectoring techniques are applied to achieve lateral movement, then response time is improved, but energy consumption increases
Solution Approach 1:
The system changes operational parameters by selecting different torque vectoring techniques based on vehicle speed and driving conditions. At low speeds, steering torque vectoring is used (lower energy). At high speeds or during emergency maneuvers, braking or drive torque vectoring is activated (higher energy but faster response). This parameter-based selection optimizes the energy-response time tradeoff
Solution Approach 2:
The system applies partial torque vectoring action by selectively activating only the necessary techniques for current path tracking requirements. Instead of continuously applying all torque vectoring methods, the system uses minimal necessary intervention, reducing energy consumption while maintaining adequate response time for normal driving conditions
Data Source
AI summary
A method includes determining a desired yaw moment to be applied to an ego vehicle during travel. The method also includes identifying yaw moment changes that are achievable using different torque vectoring techniques supported by the ego vehicle. The method further includes selecting at least one of the torque vectoring techniques based on the identified yaw moment changes. In addition, the method includes using the at least one selected torque vectoring technique to obtain the desired yaw moment and create lateral movement of the ego vehicle during the travel. In some cases, a desired response time associated with the lateral movement of the ego vehicle may be used, where steering control provides a faster response time and torque vectoring control provides a slower response time. The at least one torque vectoring technique may be selected based on different energy efficiencies associated with different ones of the torque vectoring techniques.