Driving Policy Determination via Saliency-Based Data Filtering
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Solution Overview
Problem
Current autonomous driving systems face challenges in determining effective driving policies due to the need for large amounts of accurate and salient data, which is time-consuming and expensive to obtain, especially in complex driving environments.
Innovation Solution
A method that records vehicle sensor data, extracts driver behavior data, and determines driving policies by correlating vehicle events with driver behavior, enabling the development of models that emulate and improve upon human driving skills, particularly in complex environments, and can be used to train autonomous vehicle control systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large quantities of accurate and salient data are collected through manual techniques, then training data quality improves, but time and expense increase significantly
Solution Approach 1:
The system automatically determines data saliency using machine learning models that analyze sensor data patterns, eliminating the need for manual human labeling. The computational system self-evaluates which data points are most relevant for training, performing the filtering function that would otherwise require human experts.
Solution Approach 2:
Manual human techniques for data filtering and saliency determination are replaced with automated computational algorithms. The system uses machine learning models to automatically identify and prioritize salient training data, substituting the mechanical process of human review with an automated digital system.
2Measurement precision
If manual filtering and labeling techniques are used to determine data saliency, then data quality improves, but expense increases
Solution Approach 1:
The computational system automatically performs data filtering and saliency determination using embedded machine learning algorithms, eliminating the need for expensive human expert intervention. The system serves itself by autonomously identifying which training data points are most valuable.
Solution Approach 2:
The patent replaces expensive manual human techniques with automated computational methods. Machine learning models process and evaluate data saliency, substituting the costly human expertise with an automated digital system that scales more efficiently.
3Reliability
If conventional training methods are used, then system complexity remains manageable, but autonomous driving safety and effectiveness decrease
Solution Approach 1:
The patent introduces an intermediary layer of machine learning models that bridge raw sensor data and driving policy determination. These intermediate processing layers analyze patterns in sensor data and generate saliency scores, creating a structured approach to handling complex data while improving safety outcomes.
Solution Approach 2:
The training system is segmented into distinct functional components: sensor data collection, pattern recognition models, saliency determination algorithms, and policy generation modules. This segmentation allows each component to be optimized independently while working together to improve overall system reliability.
Data Source
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AI summary
Systems and methods for driving. A driving data set for each of plurality of human-driven vehicles is determined. For each driving data set, exterior scene features of an exterior scene of the respective vehicle are extracted from the exterior image data. A driving response model is trained based on the exterior scene features and the vehicle control inputs from the selected driving data sets.