Vehicle Interior Adjustment Path Planning With ML Collision Screening
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Solution Overview
Problem
The computational effort required for collision checks in complex adjustment systems of motor vehicles, particularly in semi-autonomous or autonomous vehicles, is high due to the large number of adjustable interior elements and potential overlapping adjustment paths, increasing the risk of collisions with objects and persons.
Innovation Solution
A method using a collision estimation model based on a trained machine learning model to predict collision-free intermediate configurations, reducing the need for full collision checks on these configurations, thereby optimizing the adjustment system's performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a collision check is performed for all intermediate configurations in path planning, then collision detection reliability is improved, but computational effort increases significantly
Solution Approach 1:
The patent applies preliminary action by performing a prediction of collision risk for intermediate configurations before conducting the actual collision check. The control arrangement uses a trained machine learning model to predict whether an intermediate configuration is likely to be collision-free, and only performs the computationally intensive collision check on configurations with positive predictions. This pre-screening approach maintains collision detection reliability while significantly reducing computational effort.
2Adaptability or versatility
If the number of adjustable interior elements is increased to enhance functionality, then adaptability is improved, but the complexity of path planning and collision checks increases
Solution Approach 1:
The patent introduces an intermediary machine learning model that acts as a mediator between the complex adjustment system and the collision check process. This model predicts collision risk for intermediate configurations, filtering out safe configurations before they undergo detailed collision checks. This intermediary layer simplifies the overall path planning complexity while maintaining the ability to handle multiple adjustable interior elements.
Data Source
AI summary
A method for operating an adjustment system for an interior of a vehicle, wherein a collision-free adjustment path from an initial configuration to a final configuration is determined in a path planning routine by the control arrangement by way of intermediate configurations, wherein a collision check is performed for the intermediate configurations, in which the respective intermediate configuration is checked for the presence of a collision based on a kinematics model and a geometry model, wherein the collision-free adjustment path is generated based on the intermediate configurations and depending on the results of the collision check. A prediction of the presence of a collision is generated for the intermediate configurations by the control arrangement with the aid of a predetermined collision model based on a trained machine learning model and that the intermediate configurations are subjected to the collision check or rejected depending on the prediction of the collision check.