Autonomous Vehicle MPC Compensation Using Context and Feedback
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Solution Overview
Problem
Autonomous vehicle control faces challenges due to model uncertainty and nonlinearity, which affect the accuracy and efficiency of model predictive control (MPC) systems.
Innovation Solution
The implementation of offline and online collaborative compensation methods for MPC in autonomous driving, utilizing context information and feedback to adjust control instructions and improve vehicle operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If model predictive control is used for autonomous vehicle navigation, then the vehicle can autonomously control and navigate towards a destination, but model uncertainty and nonlinearity reduce the accuracy and efficiency of the control system
Solution Approach 1:
The patent segments the control system into multiple independent modules: a planning module that generates intended operations, a context information acquisition module that gathers environmental data, and a control instruction determination module that processes both inputs. This segmentation allows each module to handle specific tasks independently, reducing the overall complexity of managing model uncertainty and nonlinearity in the autonomous control system.
Solution Approach 2:
The patent implements preliminary action by acquiring context information about the operating environment before determining control instructions. The system proactively gathers data about road conditions, weather, and environmental factors in advance, allowing the control system to pre-adjust for known uncertainties and nonlinearities rather than reacting to them in real-time, thereby improving control accuracy.
2Measurement precision
If context information and feedback are used to adjust control instructions, then real-time decision-making accuracy is enhanced, but the computational complexity and processing requirements increase
Solution Approach 1:
The patent applies local quality by determining context compensated control instructions that are specifically tailored to the current operating conditions. Instead of using a single uniform control strategy, the system adjusts control parameters locally based on specific context information such as road grade, curvature, and weather conditions. This allows high measurement precision in decision-making while avoiding the computational complexity of globally optimizing all control parameters simultaneously.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors the actual vehicle operation and compares it with intended operations. This feedback loop allows the system to refine control instructions in real-time based on actual performance data, improving decision-making accuracy. The feedback is processed efficiently by focusing only on relevant deviations and applying targeted corrections rather than reprocessing all control parameters.
Data Source
AI summary
Devices, systems, and methods for controlling a vehicle are described. An example method for controlling a vehicle includes obtaining planning information relating to an intended operation of the vehicle, the intended operation relating to an intended value of an operation parameter of the vehicle; obtaining, based on the intended operation of the vehicle, context information relating to an environment in which the vehicle is to operate following the planning information; determining a context compensated control instruction based on the planning information and the context information; obtaining feedback relating to a deviation of a real-time value of the operation parameter of the vehicle operating according to the context compensated control instruction from the intended value of the operation parameter relating to the intended operation; determining a corrected control instruction based on the feedback; and operating the vehicle based on the corrected control instruction.