AI Vehicle Control Training for Safety Margin Optimization
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Solution Overview
Problem
Complex transportation systems face challenges in classifying and optimizing system-level interactions and behaviors due to the integration of complex processes, mechanical systems, and human elements, where existing AI technologies struggle to effectively predict and optimize interactions.
Innovation Solution
A hybrid neural network system is employed to classify social media data affecting transportation systems, predict effects on these systems, and optimize vehicle states by utilizing convolutional neural networks and other AI components to analyze and respond to various data sources, including social media posts, traffic conditions, and user behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a hybrid neural network system is used to classify and predict transportation system interactions, then the accuracy of predicting system-level interactions is improved, but the device complexity increases
Solution Approach 1:
The hybrid neural network system is segmented into multiple specialized neural networks, each designed to process specific types of data or perform specific functions within the transportation system. This segmentation allows the system to achieve high prediction accuracy for complex interactions while managing overall system complexity through modular architecture.
Solution Approach 2:
The hybrid neural network system is designed with multi-functional capabilities that allow it to handle diverse transportation system interactions (chemical processes, mechanical systems, human behaviors) using a unified framework. This universality improves prediction accuracy across different interaction types while avoiding the need for separate specialized systems for each interaction type.
2Ease of operation
If social media data from multiple sources is analyzed to optimize vehicle operation, then the user experience is improved, but the loss of information increases due to data volume
Solution Approach 1:
The system extracts only the most relevant and meaningful information from the vast volume of social media data sourced from multiple platforms. By selectively extracting key insights rather than processing all raw data, the system improves user experience through personalized optimization while minimizing information loss and computational burden.
Solution Approach 2:
The system performs preliminary processing and filtering of social media data before main analysis, pre-identifying relevant information patterns and trends. This preliminary action reduces the volume of data requiring detailed processing, thereby improving user experience while preserving critical information and reducing computational requirements.
Data Source
AI summary
A system may collect human operator interactions with a vehicle control system interface operatively connected to a vehicle, and may collect vehicle response and operating conditions associated at least contemporaneously with the human operator interaction. Environmental information is collected contemporaneously with the human operator interaction. An artificial intelligence system is trained to control the vehicle with an optimized margin of safety while mimicking the human operator, the training including instructing the artificial intelligence system to take input from an environment data collection module about instances of environmental information associated with the contemporaneously collected vehicle response and operating conditions, where the optimized margin of safety is achieved by training the artificial intelligence system to control the vehicle based on a set of human operator interaction data collected from interactions of an expert human vehicle operator and a set of outcome data from a set of vehicle safety events.