# CausalAF: Causal Autoregressive Flow for Safety-Critical Driving Scenario Generation

arXiv: https://arxiv.org/abs/2110.13939 · HTML: https://mlsanity.com/paper/2110.13939/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 54 references that are in the map.
- First posted on arXiv 26 Oct 2021.

## Builds on (most cited first, 30 of 31)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [DAGs with NO TEARS: Continuous Optimization for Structure Learning](https://mlsanity.com/paper/1803.01422/) (2018)
- [Image2StyleGAN++: How to Edit the Embedded Images?](https://mlsanity.com/paper/1911.11544/) (2019)
- [GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation](https://mlsanity.com/paper/2001.09382/) (2020)
- [Neural Autoregressive Flows](https://mlsanity.com/paper/1804.00779/) (2018)
- [Learning Bayesian Networks: The Combination of Knowledge and Statistical Data](https://mlsanity.com/paper/1302.6815/) (2013)
- [CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training](https://mlsanity.com/paper/1709.02023/) (2017)
- [Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects](https://mlsanity.com/paper/1811.11553/) (2018)
- [Physically Realizable Adversarial Examples for LiDAR Object Detection](https://mlsanity.com/paper/2004.00543/) (2020)
- [AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles](https://mlsanity.com/paper/2101.06549/) (2021)
- [Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining](https://mlsanity.com/paper/2006.09191/) (2020)
- [Causal Autoregressive Flows](https://mlsanity.com/paper/2011.02268/) (2020)
- [Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data](https://mlsanity.com/paper/1810.10093/) (2018)
- [SceneGen: Learning to Generate Realistic Traffic Scenes](https://mlsanity.com/paper/2101.06541/) (2021)
- [Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation](https://mlsanity.com/paper/1811.00145/) (2018)
- [Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method](https://mlsanity.com/paper/2003.01197/) (2020)
- [Adversarial Attacks Beyond the Image Space](https://mlsanity.com/paper/1711.07183/) (2017)
- [Meta-Sim2: Unsupervised Learning of Scene Structure for Synthetic Data Generation](https://mlsanity.com/paper/2008.09092/) (2020)
- [Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluation](https://mlsanity.com/paper/2009.08311/) (2020)
- [Accelerated Evaluation of Automated Vehicles Safety in Lane Change Scenarios Based on Importance Sampling Techniques](https://mlsanity.com/paper/1605.04965/) (2016)
- [Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models](https://mlsanity.com/paper/1711.05772/) (2017)
- [CMTS: Conditional Multiple Trajectory Synthesizer for Generating Safety-critical Driving Scenarios](https://mlsanity.com/paper/1910.00099/) (2019)
- [Towards Universal Physical Attacks On Cascaded Camera-Lidar 3D Object Detection Models](https://mlsanity.com/paper/2101.10747/) (2021)

## Built on by (most cited first, 20 of 20)

- [CAT: Closed-loop Adversarial Training for Safe End-to-End Driving](https://mlsanity.com/paper/2310.12432/) (2023)
- [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022)
- [Causal Composition Diffusion Model for Closed-loop Traffic Generation](https://mlsanity.com/paper/2412.17920/) (2024)
- [Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving](https://mlsanity.com/paper/2311.10747/) (2023)
- [Structured Neural Networks for Density Estimation and Causal Inference](https://mlsanity.com/paper/2311.02221/) (2023)
- [FREA: Feasibility-Guided Generation of Safety-Critical Scenarios with Reasonable Adversariality](https://mlsanity.com/paper/2406.02983/) (2024)
- [Action-slot: Visual Action-centric Representations for Multi-label Atomic Activity Recognition in Traffic Scenes](https://mlsanity.com/paper/2311.17948/) (2023)
- [Kinematics-aware Trajectory Generation and Prediction with Latent Stochastic Differential Modeling](https://mlsanity.com/paper/2309.09317/) (2023)
- [Safety-Critical Traffic Simulation with Adversarial Transfer of Driving Intentions](https://mlsanity.com/paper/2503.05180/) (2025)
- [CounterScene: Counterfactual Causal Reasoning in Generative World Models for Safety-Critical Closed-Loop Evaluation](https://mlsanity.com/paper/2603.21104/) (2026)
- [Simulation-Based Counterfactual Causal Discovery on Real World Driver Behaviour](https://mlsanity.com/paper/2306.03354/) (2023)
- [Importance Sampling-Guided Meta-Training for Intelligent Agents in Highly Interactive Environments](https://mlsanity.com/paper/2407.15839/) (2024)
- [Controllable Collision Scenario Generation via Collision Pattern Prediction](https://mlsanity.com/paper/2510.12206/) (2025)
- [ECSAS: Exploring Critical Scenarios from Action Sequence in Autonomous Driving](https://mlsanity.com/paper/2209.10078/) (2022)
- [Dance of the ADS: Orchestrating Failures through Historically-Informed Scenario Fuzzing](https://mlsanity.com/paper/2407.04359/) (2024)
- [LAMBDA: Covering the Multimodal Critical Scenarios for Automated Driving Systems by Search Space Quantization](https://mlsanity.com/paper/2412.00517/) (2024)
- [Navigating the Edge with the State-of-the-Art Insights into Corner Case Identification and Generation for Enhanced Autonomous Vehicle Safety](https://mlsanity.com/paper/2503.00077/) (2025)
- [Learning Causal Structure Distributions for Robust Planning](https://mlsanity.com/paper/2508.06742/) (2025)
- [Semantic and Articulated Pedestrian Sensing Onboard a Moving Vehicle](https://mlsanity.com/paper/2309.06313/) (2023)
- [From Attacks to Curricula: Learnability-Guided Adversarial Training for Safe Autonomous Driving](https://mlsanity.com/paper/2606.14032/) (2026)

## Abstract (arXiv)

Generating safety-critical scenarios, which are crucial yet difficult to collect, provides an effective way to evaluate the robustness of autonomous driving systems. However, the diversity of scenarios and efficiency of generation methods are heavily restricted by the rareness and structure of safety-critical scenarios. Therefore, existing generative models that only estimate distributions from observational data are not satisfying to solve this problem. In this paper, we integrate causality as a prior into the scenario generation and propose a flow-based generative framework, Causal Autoregressive Flow (CausalAF). CausalAF encourages the generative model to uncover and follow the causal relationship among generated objects via novel causal masking operations instead of searching the sample only from observational data. By learning the cause-and-effect mechanism of how the generated scenario causes risk situations rather than just learning correlations from data, CausalAF significantly improves learning efficiency. Extensive experiments on three heterogeneous traffic scenarios illustrate that CausalAF requires much fewer optimization resources to effectively generate safety-critical scenarios. We also show that using generated scenarios as additional training samples empirically improves the robustness of autonomous driving algorithms.