AI Prediction Model Training for Behavior-Planner Alignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing AI-based prediction models for self-driving vehicles face a distribution mismatch due to deviations in driving styles between training scenes and the intended behavior planner, leading to invalid planning outcomes.
Innovation Solution
A method to train AI-based prediction models by applying weightings to training data elements based on the compatibility with a downstream behavior planner, ensuring the model learns the intended driving style, using planning and prediction deviations to adjust data distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If training data from diverse driving styles is used to train the prediction model, then the model's generalization capability is improved, but distribution mismatch occurs between the prediction model's assumed behavior and the behavior planner's intended behavior
Solution Approach 1:
The patent applies local quality by differentiating the treatment of training data based on its origin. Training data elements are categorized into different groups (e.g., data from the same behavior planner vs. data from other sources), and weighting factors are assigned specifically to each group. This allows the prediction model to learn general patterns from diverse data while maintaining consistency with the specific behavior planner's driving style through targeted weighting of relevant data subsets.
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting the weighting factors assigned to different training data elements during the training process. These weighting factors act as parameters that control the influence of each training data element on the prediction model's learning. By modifying these parameters based on the compatibility between training data and the behavior planner, the system resolves the contradiction between generalization and consistency.
2Measurement precision
If the prediction model is trained with data assuming agile behavior, then prediction accuracy for agile scenarios is improved, but the behavior planner cannot generate valid planning due to collision with predicted trajectories
Solution Approach 1:
The patent employs feedback mechanisms where the behavior planner's performance and planning validity are used to evaluate the prediction model's outputs. When distribution mismatch causes invalid planning (e.g., collision trajectories), this information feeds back into the training process by adjusting the weighting factors for relevant training data elements. This feedback loop ensures that the prediction model progressively aligns its predictions with the behavior planner's intended behavior while maintaining accuracy.
3Ease of manufacture
If uniform weighting is applied to all training data elements, then training simplicity is maintained, but the prediction model fails to align with the specific behavior planner's driving style
Solution Approach 1:
The patent applies preliminary action by pre-calculating and assigning weighting factors to training data elements before the actual training process begins. These weighting factors are determined based on metadata associated with each training data element, such as its source and compatibility with the behavior planner. This preliminary differentiation simplifies the training process by avoiding complex dynamic adjustments during training, while still achieving precise behavior alignment through the pre-established weighting scheme.
Data Source
AI summary
A computer-implemented method is for training an artificial intelligence (AI) based prediction model for a given behavior planner that plans a future behavior of an at least partially automated self-driving vehicle based on aggregated scene-specific information. The prediction model is trained to predict a future development of a traffic scene based on aggregated scene-specific information. At least one training data set with training data elements generated from scene-specific information from training scenes is used for training. The behavior planner is used to determine a weighting for each training data element, which determines an extent to which the respective training data element is taken into account when training the prediction model.