Vehicle Spoken Language Processing with GAN
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Solution Overview
Problem
Existing vehicle systems lack efficient and accurate methods to process spoken language commands for autonomous or semi-autonomous vehicle operation, particularly in complex traffic scenarios, relying heavily on pre-programmed criteria rather than real-time human input.
Innovation Solution
A method utilizing a generalized adversarial neural network (GAN) to process mel-frequency samples of spoken language commands, transforming them into vehicle commands, and determining path polynomials for safe navigation, incorporating sensor data and cognitive maps to operate vehicles based on spoken language cues and responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If pre-programmed criteria are used for vehicle operation, then device complexity is reduced, but adaptability to real-time traffic situations deteriorates
Solution Approach 1:
The patent replaces traditional mechanical rule-based control systems with a neural network-based cognitive system. The neural network processes sensor data and spoken language inputs to generate vehicle commands, enabling adaptive behavior without requiring complex pre-programmed logic for every possible traffic scenario.
Solution Approach 2:
The system dynamically adjusts its processing parameters by transforming spoken language into mel-frequency samples and using different neural network processing modes (1D convolution in X-direction, 1D convolution in Y-direction, or full neural network processing) based on the complexity and context of the input command.
2Adaptability or versatility
If spoken language processing is implemented in real-time, then adaptability to human input is improved, but processing time and computational complexity increase
Solution Approach 1:
The system implements a hierarchical processing approach where not all spoken language inputs require full neural network processing. Simple commands can be handled with partial processing (1D convolution only), while complex commands trigger full neural network processing, optimizing the balance between processing accuracy and time consumption.
3Productivity
If mel-frequency samples are compressed into single vectors, then processing efficiency is improved, but information loss increases
Solution Approach 1:
The system extracts only the most salient features from the full mel-frequency spectrogram by performing 1D convolution along the time axis first, then along the frequency axis. This selective extraction process reduces computational burden while preserving the critical information needed for accurate command recognition.
Data Source
AI summary
A computing system can be programmed to receive a spoken language command in response to emitting a spoken language cue and process the spoken language command with a generalized adversarial neural network (GAN) to determine a vehicle command. The computing system can be further programmed to operate a vehicle based on the vehicle command.