RAG Framework for Realistic Autonomous Driving Scenario Generation

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Solution Overview

Problem

Current simulation methods for developing autonomous vehicles struggle to generate realistic driving scenarios, especially in complex interactions among naturalistic agents, due to limitations in data retrieval and scenario generation.

Innovation Solution

The proposed system employs a Retrieval Augmented Generation (RAG) framework, which retrieves preexisting scenarios from a database and combines them with additional guidance to generate new, realistic scenarios for training autonomous vehicles.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If memorization of training datasets is used to generate simulations, then the generation process is simple, but the ability to generate previously unseen or unencountered scenarios is limited

Engineering Contradiction:

Improveability to generate previously unseen scenariosVSAvoidcomplexity of scenario generation system

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system performs preliminary encoding of training scenarios into embeddings and stores them in a database before actual scenario generation is needed. This pre-processing allows the system to quickly retrieve and combine relevant scenario elements when generating new scenarios, enabling versatility without requiring complex real-time processing

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces scenario embeddings as an intermediary representation that bridges raw training data and generated scenarios. These embeddings capture essential scenario characteristics in a compressed form, allowing the system to retrieve, manipulate, and combine scenario elements efficiently while maintaining the ability to generate novel scenarios

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If complex interactions among naturalistic agents are modeled, then the realism of scenarios improves, but the difficulty of detecting and measuring increases

Engineering Contradiction:

Improverealism of generated scenariosVSAvoiddifficulty of measuring complex interactions

Core Design Contradiction:

ReliabilityVSDifficulty of detecting and measuring

Solution Approach 1:

The system segments complex scenario interactions into distinct recoverable elements that can be independently encoded and manipulated. By dividing scenarios into manageable components (such as individual agent behaviors, environmental factors, and interaction patterns), the system maintains realism while enabling systematic analysis and measurement of specific interaction types

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent implements feedback mechanisms where generated scenarios are evaluated against realism criteria and measurement metrics. This feedback loop allows the system to iteratively improve scenario realism while simultaneously developing and refining measurement capabilities for complex interactions, turning the measurement difficulty into a progressive improvement process

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250200245A1Retrieval augmented generation of scenarios using neural networks

Publication Date: 2025.06.19 NVIDIA CORP

AI summary

Apparatuses, systems, and techniques to retrieve a set of retrieved scenarios using at least one example scenario, to use at least one first neural network to combine at least the set of retrieved scenarios to obtain combined information, and to use at least one second neural network to infer a new scenario based at least in part on the combined information. In at least one embodiment, scenarios are retrieved from a set of real-world driving scenarios and the new scenario is used to generate a simulation of automobile traffic.