# Adv-BMT: Bidirectional Motion Transformer for Safety-Critical Traffic Scenario Generation

arXiv: https://arxiv.org/abs/2506.09485 · HTML: https://mlsanity.com/paper/2506.09485/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 31 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Builds on (most cited first, 15 of 15)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset](https://mlsanity.com/paper/2104.10133/) (2021)
- [DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets](https://mlsanity.com/paper/2108.09640/) (2021)
- [MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion](https://mlsanity.com/paper/2306.03083/) (2023)
- [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)
- [Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluation](https://mlsanity.com/paper/2009.08311/) (2020)
- [BehaviorGPT: Smart Agent Simulation for Autonomous Driving with Next-Patch Prediction](https://mlsanity.com/paper/2405.17372/) (2024)
- [Scenario Diffusion: Controllable Driving Scenario Generation With Diffusion](https://mlsanity.com/paper/2311.02738/) (2023)
- [SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries](https://mlsanity.com/paper/2401.00391/) (2023)
- [WcDT: World-centric Diffusion Transformer for Traffic Scene Generation](https://mlsanity.com/paper/2404.02082/) (2024)
- [DragTraffic: Interactive and Controllable Traffic Scene Generation for Autonomous Driving](https://mlsanity.com/paper/2404.12624/) (2024)

## Built on by (most cited first, 11 of 11)

- [BridgeSim: Unveiling the OL-CL Gap in End-to-End Autonomous Driving](https://mlsanity.com/paper/2604.10856/) (2026)
- [ADV-0: Closed-Loop Min-Max Adversarial Training for Long-Tail Robustness in Autonomous Driving](https://mlsanity.com/paper/2603.15221/) (2026)
- [Understanding Real-World Traffic Safety through RoadSafe365 Benchmark](https://mlsanity.com/paper/2602.07212/) (2026)
- [SaFeR: Safety-Critical Scenario Generation for Autonomous Driving Test via Feasibility-Constrained Token Resampling](https://mlsanity.com/paper/2603.04071/) (2026)
- [World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning](https://mlsanity.com/paper/2607.10630/) (2026)
- [EvoDrive: Pareto Evolution for Safety-Critical Autonomous Driving via Self-Improving LLM Agents](https://mlsanity.com/paper/2606.03678/) (2026)
- [From Attacks to Curricula: Learnability-Guided Adversarial Training for Safe Autonomous Driving](https://mlsanity.com/paper/2606.14032/) (2026)
- [CCFM: Collision-Constrained Flow Matching for Safety-Critical Scenario Generation](https://mlsanity.com/paper/2607.04451/) (2026)
- [SkyDrive: Learning to Drive in a New City from Aerial Traffic Monitoring](https://mlsanity.com/paper/2608.25142/) (2026)
- [Collision Snapshot Guided Time-Reversed Safety-Critical Scenario Generation](https://mlsanity.com/paper/2609.06433/) (2026)
- [DreamStream: Towards Policy-Oriented Generative Simulation for End-to-End Driving](https://mlsanity.com/paper/2609.26792/) (2026)

## Abstract (arXiv)

Scenario-based testing is essential for validating the performance of autonomous driving (AD) systems. However, such testing is limited by the scarcity of long-tailed, safety-critical scenarios in existing datasets collected in the real world. To tackle the data issue, we propose the Adv-BMT framework, which augments real-world scenarios with diverse and realistic adversarial traffic interactions. The core component of Adv-BMT is a bidirectional motion transformer (BMT) model to perform inverse traffic motion predictions, which takes agent information in the last time step of the scenario as input, and reconstructs the traffic in the inverse of chronological order until the initial time step. The Adv-BMT framework is a two-staged pipeline: it first conducts adversarial initializations and then inverse motion predictions. Different from previous work, we do not need any collision data for pretraining, and are able to generate realistic and diverse collision interactions. Our experimental results validate the quality of generated collision scenarios by Adv-BMT: training in our augmented dataset would reduce episode collision rates by 20%. Demo and code are available at: https://metadriverse.github.io/adv-bmt/.