Vehicle Language Processing via Deep Neural Network Translation
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Solution Overview
Problem
Current vehicle systems face challenges in accurately and timely acquiring environmental information for safe and efficient operation, especially in autonomous and semi-autonomous modes, as they rely on sensor data alone without effectively integrating natural language inputs for navigation and communication.
Innovation Solution
A method utilizing deep neural networks to translate spoken natural language commands into intermediate constructed language commands, determining vehicle commands, and translating responses back into spoken natural language, allowing vehicles to operate based on cognitive maps and sensor data, incorporating path polynomials for navigation while handling multiple languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicle systems rely on sensor data alone for autonomous operation, then the system structure remains relatively simple, but the ability to accurately and timely acquire environmental information for safe operation is insufficient
Solution Approach 1:
The patent introduces natural language processing as an intermediary layer between sensors and vehicle control systems. The NLP system processes spoken commands and translates them into actionable vehicle commands, enabling more intuitive and reliable human-vehicle interaction while maintaining system modularity
Solution Approach 2:
The vehicle system is enhanced with multi-functional capabilities by integrating natural language processing alongside traditional sensor-based autonomous driving functions. This allows the system to handle both environmental perception through sensors and human communication through language processing, improving overall operational reliability
2Adaptability or versatility
If vehicle systems integrate natural language processing for navigation and communication, then the ability to understand occupant commands improves, but the system complexity increases
Solution Approach 1:
The natural language processing system is segmented into distinct functional modules: audio input processing, natural language interpretation, command translation, and response generation. This modular segmentation manages complexity by allowing each component to be developed and optimized independently while maintaining clear interfaces
Solution Approach 2:
The patent uses an intermediary processing layer that translates natural language into structured vehicle commands. This intermediary NLP system acts as a buffer between the complex natural language input and the structured vehicle control systems, managing the complexity transition
3Adaptability or versatility
If multiple languages are supported for natural language commands, then the versatility of vehicle communication improves, but the processing complexity and response time may increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and identifying the language of incoming commands before full interpretation. This early language detection allows the system to prepare appropriate processing pathways in advance, reducing overall processing time for multilingual commands
Solution Approach 2:
The patent applies local quality by optimizing processing resources based on the detected language. Different language processing pathways can be activated selectively, allowing the system to allocate computational resources efficiently and minimize processing time for each specific language scenario
Data Source
AI summary
A computing system can translate a spoken natural language command into an intermediate constructed language command with a first deep neural network and determine a vehicle command and an intermediate constructed language response with a second deep neural network based on receiving vehicle information. The computing system can translate the intermediate constructed language response into a spoken natural language response with a third deep neural network and operate a vehicle based on the vehicle command.