Vehicle Control With Kinematics Decoder for Stable ML Trajectories
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Solution Overview
Problem
Autonomous driving systems using machine learning models fail to ensure kinematic stability in vehicle control predictions, lacking adaptability and adherence to physical constraints.
Innovation Solution
Integrate a kinematics decoder, such as a unicycle model, within a machine learning-based prediction framework, combining it with heuristic-based predictions to generate kinematically stable trajectories that adhere to acceleration constraints and enhance risk avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a machine learning model is used to predict vehicle controls, then adaptability and data-driven predictions are improved, but kinematic stability and adherence to physical constraints deteriorate
Solution Approach 1:
The patent introduces a kinematics decoder as an intermediary component between the machine learning model and the vehicle control system. This decoder acts as a mediator that translates the ML model's predicted controls into kinematically stable commands by enforcing physical constraints and feasibility checks, thereby resolving the contradiction between adaptability and stability
Solution Approach 2:
The patent merges two different approaches: data-driven machine learning predictions and physics-based kinematic constraints. By combining these previously separate methodologies into a unified control framework, the system achieves both adaptability from ML and stability from physical constraints simultaneously
2Productivity
If machine learning predictions are used for vehicle control, then productivity and response time are improved, but reliability and safety deteriorate due to lack of kinematic feasibility
Solution Approach 1:
The kinematics decoder performs preliminary feasibility checks and constraint enforcement on predicted controls before they are executed. By pre-validating the kinematic feasibility of control commands, the system ensures safety and reliability without compromising the rapid response capabilities of the machine learning model
3Stability of the object's composition
If a kinematics decoder is added to enforce physical constraints, then kinematic stability and safety are improved, but device complexity increases
Solution Approach 1:
The kinematics decoder enforces constraints by adjusting control parameters to satisfy physical feasibility conditions. By modifying parameters such as acceleration limits, curvature bounds, and velocity constraints, the system achieves kinematic stability through parameter optimization rather than complex structural changes
Data Source
AI summary
A method for controlling a vehicle based on a kinematically stable trajectory is described. The method includes receiving sensor data, from one or more sensor of an ego-vehicle configured to perceive an environment around the ego-vehicle; predicting, with a machine learning model configured to ingest the sensor data, an acceleration and a curvature defining a trajectory for the ego-vehicle; evaluating the acceleration and the curvature with a kinematics decoder configured to determine that the acceleration and the curvature are kinematically feasible for the ego-vehicle; generating, with the kinematics decoder, one or more outputs based on evaluation of the acceleration and the curvature, wherein the one or more outputs define the kinematically stable trajectory for controlling the ego-vehicle; and controlling the ego-vehicle based on the one or more outputs.