Vehicle Perception Assistant Using OCR and LLM Traffic Sign Context

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Solution Overview

Problem

Conventional vehicle technologies limit information provided to operators, increasing cognitive load and potentially leading to safety risks and accidents due to distractions and unsafe driving conditions.

Innovation Solution

The use of a Large Language Model (LLM) to generate natural language responses that provide context-specific information to operators, including alerts about traffic signs, parking availability, weather, and the driver's alertness level, to streamline the driving experience and reduce cognitive load.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If conventional technologies provide limited information to operators, then device complexity is reduced, but operator cognitive load increases and safety risks worsen

Engineering Contradiction:

Improveinformation processing system complexityVSAvoiddriving safety

Core Design Contradiction:

Device complexityVSReliability

Solution Approach 1:

The patent introduces a virtual assistant as an intermediary between the complex vehicle systems and the operator. This intermediary processes information from multiple sources (sensors, traffic signs, weather data) and presents it in a simplified, natural language format, reducing the cognitive load on the operator while maintaining comprehensive safety monitoring.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces traditional mechanical information presentation methods (visual displays, audio warnings) with an AI-based natural language processing system. The LLM-generated responses provide context-specific information in conversational form, making complex data more accessible and reducing operator mental effort.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Object-affected harmful factors

If conventional technologies provide limited information to operators, then distractions are reduced, but cognitive load increases and safety risks worsen

Engineering Contradiction:

ImprovedistractionsVSAvoiddriving safety

Core Design Contradiction:

Object-affected harmful factorsVSReliability

Solution Approach 1:

The patent applies local quality by providing context-specific information only when and where it is relevant to the operator's current situation. The LLM analyzes the operational context and generates responses tailored to specific scenarios (e.g., traffic sign alerts, parking availability, weather conditions), avoiding generic or irrelevant information that would constitute distractions.

Inventive Principle:

Principle #3Local quality

3Reliability

If rich context-specific information is provided to operators via language model, then cognitive load is reduced and safety improves, but device complexity increases

Engineering Contradiction:

Improvedriving safetyVSAvoidlanguage model system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent implements a universal virtual assistant system that handles multiple functions through a single LLM-based interface. The same language model processes diverse information types (traffic signs, weather, parking, operator alertness) and generates appropriate responses, consolidating what would otherwise require multiple specialized systems into one multi-functional platform.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Reliability

If language model generates natural language responses with context-specific information, then operator alertness is improved, but use of energy increases

Engineering Contradiction:

Improveoperator alertnessVSAvoidcomputational energy consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The patent applies partial action by having the LLM generate responses selectively based on operational context and operator needs. Rather than continuously generating information, the system activates language model processing only when relevant events occur (e.g., detecting a traffic sign, monitoring operator alertness thresholds), reducing unnecessary computational energy consumption while maintaining operator alertness when needed.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20250136130A1Machine operation assistance using language model-augmented perception

Publication Date: 2025.05.01 NVIDIA CORP

AI summary

Various embodiments of the present disclosure relate to operator assistance based on extracting natural language characters from one or more sensed objects. For instance, particular embodiments may generate a natural language utterance based on extracting natural language text in a nearby traffic sign. In an illustrative example, particular embodiments may detect, via object detection and within image data, one or more regions of the image data depicting the traffic sign. Particular embodiments can then extract one or more first natural language characters represented in the traffic sign based at least on performing optical character recognition within the one or more regions of the image data in response to detecting the one or more regions of the image data depicting the traffic sign.