# CMTS: Conditional Multiple Trajectory Synthesizer for Generating Safety-critical Driving Scenarios

arXiv: https://arxiv.org/abs/1910.00099 · HTML: https://mlsanity.com/paper/1910.00099/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2019.

## Built on by (most cited first, 19 of 19)

- [ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehicles](https://mlsanity.com/paper/2405.14062/) (2024)
- [SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles](https://mlsanity.com/paper/2206.09682/) (2022)
- [CausalAF: Causal Autoregressive Flow for Safety-Critical Driving Scenario Generation](https://mlsanity.com/paper/2110.13939/) (2021)
- [AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusion](https://mlsanity.com/paper/2410.08453/) (2024)
- [Diverse Critical Interaction Generation for Planning and Planner Evaluation](https://mlsanity.com/paper/2103.00906/) (2021)
- [Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration](https://mlsanity.com/paper/2309.09408/) (2023)
- [Traffic Scene Generation from Natural Language Description for Autonomous Vehicles with Large Language Model](https://mlsanity.com/paper/2409.09575/) (2024)
- [CaDRE: Controllable and Diverse Generation of Safety-Critical Driving Scenarios using Real-World Trajectories](https://mlsanity.com/paper/2403.13208/) (2024)
- [TAE: A Semi-supervised Controllable Behavior-aware Trajectory Generator and Predictor](https://mlsanity.com/paper/2203.01261/) (2022)
- [LD-Scene: LLM-Guided Diffusion for Controllable Generation of Adversarial Safety-Critical Driving Scenarios](https://mlsanity.com/paper/2505.11247/) (2025)
- [Kinematics-aware Trajectory Generation and Prediction with Latent Stochastic Differential Modeling](https://mlsanity.com/paper/2309.09317/) (2023)
- [Analyzing and Enhancing Closed-loop Stability in Reactive Simulation](https://mlsanity.com/paper/2208.04559/) (2022)
- [Augmenting Safety-Critical Driving Scenarios while Preserving Similarity to Expert Trajectories](https://mlsanity.com/paper/2404.13347/) (2024)
- [AuthSim: Towards Authentic and Effective Safety-critical Scenario Generation for Autonomous Driving Tests](https://mlsanity.com/paper/2502.21100/) (2025)
- [AnchorDrive: LLM Scenario Rollout with Anchor-Guided Diffusion Regeneration for Safety-Critical Scenario Generation](https://mlsanity.com/paper/2603.02542/) (2026)
- [Semantic and Articulated Pedestrian Sensing Onboard a Moving Vehicle](https://mlsanity.com/paper/2309.06313/) (2023)
- [Dynamically Expanding Capacity of Autonomous Driving with Near-Miss Focused Training Framework](https://mlsanity.com/paper/2406.02865/) (2024)
- [Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario](https://mlsanity.com/paper/2507.15587/) (2025)
- [TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation](https://mlsanity.com/paper/2606.29097/) (2026)

## Abstract (arXiv)

Naturalistic driving trajectories are crucial for the performance of autonomous driving algorithms. However, most of the data is collected in safe scenarios leading to the duplication of trajectories which are easy to be handled by currently developed algorithms. When considering safety, testing algorithms in near-miss scenarios that rarely show up in off-the-shelf datasets is a vital part of the evaluation. As a remedy, we propose a near-miss data synthesizing framework based on Variational Bayesian methods and term it as Conditional Multiple Trajectory Synthesizer (CMTS). We leverage a generative model conditioned on road maps to bridge safe and collision driving data by representing their distribution in the latent space. By sampling from the near-miss distribution, we can synthesize safety-critical data crucial for understanding traffic scenarios but not shown in neither the original dataset nor the collision dataset. Our experimental results demonstrate that the augmented dataset covers more kinds of driving scenarios, especially the near-miss ones, which help improve the trajectory prediction accuracy and the capability of dealing with risky driving scenarios.