# Test Automation for Interactive Scenarios via Promptable Traffic Simulation

arXiv: https://arxiv.org/abs/2506.01199 · HTML: https://mlsanity.com/paper/2506.01199/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 19 references that are in the map.
- First posted on arXiv 1 Jun 2025.

## Builds on (most cited first, 13 of 13)

- [Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset](https://mlsanity.com/paper/2104.10133/) (2021)
- [Guided Conditional Diffusion for Controllable Traffic Simulation](https://mlsanity.com/paper/2210.17366/) (2022)
- [Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior](https://mlsanity.com/paper/2112.05077/) (2021)
- [Language-Guided Traffic Simulation via Scene-Level Diffusion](https://mlsanity.com/paper/2306.06344/) (2023)
- [LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving](https://mlsanity.com/paper/2101.06547/) (2021)
- [ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehicles](https://mlsanity.com/paper/2405.14062/) (2024)
- [DeepReach: A Deep Learning Approach to High-Dimensional Reachability](https://mlsanity.com/paper/2011.02082/) (2020)
- [Language Conditioned Traffic Generation](https://mlsanity.com/paper/2307.07947/) (2023)
- [Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluation](https://mlsanity.com/paper/2009.08311/) (2020)
- [SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries](https://mlsanity.com/paper/2401.00391/) (2023)
- [Promptable Closed-loop Traffic Simulation](https://mlsanity.com/paper/2409.05863/) (2024)
- [Language-Driven Interactive Traffic Trajectory Generation](https://mlsanity.com/paper/2405.15388/) (2024)
- [Surprise Potential as a Measure of Interactivity in Driving Scenarios](https://mlsanity.com/paper/2502.05677/) (2025)

## Built on by (most cited first, 1 of 1)

- [AutoODD: Agentic Audits via Bayesian Red Teaming in Black-Box Models](https://mlsanity.com/paper/2509.08638/) (2025)

## Abstract (arXiv)

Autonomous vehicle (AV) planners must undergo rigorous evaluation before widespread deployment on public roads, particularly to assess their robustness against the uncertainty of human behaviors. While recent advancements in data-driven scenario generation enable the simulation of realistic human behaviors in interactive settings, leveraging these models to construct comprehensive tests for AV planners remains an open challenge. In this work, we introduce an automated method to efficiently generate realistic and safety-critical human behaviors for AV planner evaluation in interactive scenarios. We parameterize complex human behaviors using low-dimensional goal positions, which are then fed into a promptable traffic simulator, ProSim, to guide the behaviors of simulated agents. To automate test generation, we introduce a prompt generation module that explores the goal domain and efficiently identifies safety-critical behaviors using Bayesian optimization. We apply our method to the evaluation of an optimization-based planner and demonstrate its effectiveness and efficiency in automatically generating diverse and realistic driving behaviors across scenarios with varying initial conditions.