Vehicle Language Processing with Confidence-Based Command Interpretation
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Solution Overview
Problem
Current vehicle systems face challenges in accurately and efficiently determining vehicle paths and interacting with the environment, particularly in autonomous and semi-autonomous modes, due to limitations in processing spoken language commands and integrating sensor data effectively.
Innovation Solution
A method utilizing a natural language understanding system with automatic speech recognition and deep neural networks to process spoken language commands, determine confidence levels, and control vehicle operations such as steering and braking, based on sensor data and vehicle noise models, to calculate and execute path polynomials for safe navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a natural language understanding system with deep neural networks is used to process spoken language commands, then the accuracy of command interpretation is improved, but the device complexity increases
Solution Approach 1:
The system divides spoken language processing into distinct stages: automatic speech recognition (ASR) for converting audio to text, natural language understanding (NLU) for interpreting intent, and command execution for acting on the interpreted command. This segmentation allows each module to be optimized independently while maintaining overall system accuracy.
Solution Approach 2:
The patent introduces an intermediary processing layer that converts spoken commands into structured intermediate representations before execution. This intermediary layer acts as a buffer between the complex neural network processing and the simpler vehicle control systems, reducing the complexity burden on the execution layer while maintaining high interpretation accuracy.
2Reliability
If vehicle noise models are integrated into the speech recognition system, then the reliability of command recognition in noisy environments is improved, but the loss of information increases due to processing overhead
Solution Approach 1:
The system performs preliminary noise characterization by integrating vehicle noise models before speech recognition occurs. These models pre-compute expected noise profiles for different vehicle operating conditions (engine noise, road noise, wind noise), allowing the ASR system to pre-adjust its processing parameters and reduce the need for extensive real-time noise analysis, thereby minimizing information loss.
Solution Approach 2:
The system implements feedback loops where the recognized command and confidence level are fed back into the noise model analysis. If confidence is low, the system can request clarification or adjust noise model parameters based on the actual acoustic environment, improving reliability while managing information processing efficiency through adaptive feedback rather than exhaustive analysis.
3Measurement precision
If confidence levels are determined for each word of the spoken language command, then the measurement precision of command interpretation is improved, but the loss of time increases due to additional processing
Solution Approach 1:
The system applies partial confidence level analysis by determining confidence scores for only the most critical words in a command (e.g., action verbs and key objects) rather than every single word. This selective approach maintains sufficient precision for safe command execution while significantly reducing the processing time and computational overhead compared to analyzing all words equally.
Solution Approach 2:
The patent applies different levels of confidence level detail to different parts of the spoken command based on their importance. Critical words that directly affect vehicle control receive detailed confidence analysis, while less critical words (such as articles or prepositions) receive simplified or no confidence assessment, optimizing the balance between precision and processing time through localized quality variation.
Data Source
AI summary
A computing system can determine a vehicle command based on a received spoken language command and determined confidence levels. The computing system can operate a vehicle based on the vehicle command. The computing system can further determine the spoken language command by processing audio spectrum data corresponding to spoken natural language with an automatic speech recognition (ASR) system.