Scenario-Based Training Weight Tuning for Autonomous Driving Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous driving vehicles face performance degradation due to overfitting in specific driving scenarios when learning-based components are trained with excessive data, leading to suboptimal decision-making.
Innovation Solution
A framework for scenario-based training data weight tuning using algorithms like random search, grid search, or Bayesian search to optimize the allocation of training datasets across different driving scenarios, ensuring balanced data distribution to prevent overfitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If excessive training data is allocated to a specific driving scenario, then the model may achieve high performance on that scenario, but overfitting occurs leading to performance degradation
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the weight parameters of training datasets for different driving scenarios. Instead of using fixed data allocation, the system optimizes weight parameters through iterative training processes, allowing the model to adapt to varying scenario complexities without overfitting. This resolves the contradiction by transforming the static data allocation into a dynamic parameter optimization problem.
Solution Approach 2:
The system implements dynamics by making the training data weight distribution adaptive rather than static. The weights are continuously adjusted during the training process based on model performance feedback, enabling the system to dynamically balance between fitting specific scenarios well and maintaining generalization capability across all scenarios.
2Ease of operation
If uniform data distribution is used across all driving scenarios, then training is simplified, but performance varies significantly across different scenario types
Solution Approach 1:
The patent applies local quality by assigning different weight values to training datasets corresponding to different driving scenarios. This allows the training process to treat each scenario according to its specific characteristics and complexity requirements, rather than applying a uniform distribution. Complex scenarios receive higher weights requiring more training attention, while simpler scenarios receive lower weights, thereby achieving consistent performance across diverse scenarios.
3Measurement precision
If manual weight adjustment is performed for each driving scenario, then performance can be optimized, but the complexity of the training process increases significantly
Solution Approach 1:
The system implements self-service by enabling the training process to automatically determine optimal weight distributions for different scenarios without manual intervention. The framework includes automated mechanisms that analyze scenario characteristics and adjust weights accordingly, reducing the complexity burden on operators while maintaining optimized performance across all driving scenarios.
Data Source
AI summary
According to various embodiments, the disclosure discloses systems, methods and media for formulating training datasets for learning-based components in an autonomous driving vehicle (ADV). In an embodiment, an exemplary method includes allocating training datasets for training a learning-based model in the ADV, each training dataset being allocated to one of multiple predefined driving scenarios; determining a weight of each training dataset out of the training datasets; and optimizing the weight of each training dataset in one or more iterations according to a predetermined algorithm until a performance of the learning-based model reaches a predetermined threshold. The predetermined algorithm is one of a random search algorithm, a grid search algorithm, or a Bayesian algorithm.