New Framework Enhances Scenario Generation for Autonomous Driving

Researchers at TUM-AVS have developed Chat2Scenic, a new framework that automatically generates test scenarios for autonomous vehicles by translating regulatory documents into executable simulations. The system addresses a major bottleneck in autonomous driving development: creating diverse, compliant test cases that match real-world regulatory requirements.

How Chat2Scenic Works

The framework uses an iterative Retrieval-Augmented Generation (RAG) approach to convert written regulations into the Scenic programming language, which creates 3D driving scenarios. When engineers input regulatory text describing traffic situations, Chat2Scenic breaks down the requirements and generates corresponding code that can run in simulation environments.

The system operates through multiple refinement cycles. It first interprets the regulatory language, generates initial Scenic code, then checks the output for errors and compliance issues. If problems exist, the framework automatically revises the code until it produces a working scenario that matches the original regulatory intent.

This differs from traditional scenario generation methods that require manual coding or rely on limited pre-built templates. Chat2Scenic can handle complex regulatory language and produce scenarios that specifically test edge cases mentioned in safety standards.

The Scenario Generation Challenge

Autonomous vehicle companies must validate their systems against thousands of potential driving situations before deployment. Current approaches either use human engineers to manually code each test scenario or employ basic randomization that may miss important edge cases specified in regulations.

Manual scenario creation is time-intensive and prone to interpretation errors when translating regulatory text into code. Engineers might spend weeks creating scenarios for a single regulatory requirement, then discover their interpretation doesn't match the intended test case.

Existing automated tools typically generate scenarios through random parameter variations rather than systematic interpretation of regulatory requirements. This creates coverage gaps where important mandated test cases never get generated.

Technical Implementation

Chat2Scenic builds on large language models trained to understand both natural language regulations and the Scenic domain-specific language. The RAG component retrieves relevant regulatory context during generation, ensuring scenarios remain compliant with specific standards.

The iterative refinement process includes syntax checking, semantic validation, and compliance verification. When the system detects issues, it automatically adjusts parameters like vehicle positions, speeds, or environmental conditions until the scenario executes properly and meets regulatory specifications.

The [arXiv / TUM-AVS](https://arxiv.org/abs/2607.14387) paper demonstrates the framework generating executable scenarios from various international driving regulations, including complex multi-vehicle interactions and environmental conditions.

This innovation pressures existing scenario generation tools to incorporate regulatory compliance features, while making comprehensive autonomous vehicle testing more accessible to smaller companies that lack extensive simulation engineering teams.