# DeepMF: Deep Motion Factorization for Closed-Loop Safety-Critical Driving Scenario Simulation

arXiv: https://arxiv.org/abs/2412.17487 · HTML: https://mlsanity.com/paper/2412.17487/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Adversarial scenarios and safety testing, one of 9 directions in End-to-end autonomous driving.
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 23 Dec 2024.

## Placed in

- End-to-end autonomous driving / [Adversarial scenarios and safety testing](https://mlsanity.com/driving/adversarial-scenarios-and-safety-testing/)

## Builds on (most cited first, 2 of 2)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)

## Abstract (arXiv)

Safety-critical traffic scenarios are of great practical relevance to evaluating the robustness of autonomous driving (AD) systems. Given that these long-tail events are extremely rare in real-world traffic data, there is a growing body of work dedicated to the automatic traffic scenario generation. However, nearly all existing algorithms for generating safety-critical scenarios rely on snippets of previously recorded traffic events, transforming normal traffic flow into accident-prone situations directly. In other words, safety-critical traffic scenario generation is hindsight and not applicable to newly encountered and open-ended traffic events.In this paper, we propose the Deep Motion Factorization (DeepMF) framework, which extends static safety-critical driving scenario generation to closed-loop and interactive adversarial traffic simulation. DeepMF casts safety-critical traffic simulation as a Bayesian factorization that includes the assignment of hazardous traffic participants, the motion prediction of selected opponents, the reaction estimation of autonomous vehicle (AV) and the probability estimation of the accident occur. All the aforementioned terms are calculated using decoupled deep neural networks, with inputs limited to the current observation and historical states. Consequently, DeepMF can effectively and efficiently simulate safety-critical traffic scenarios at any triggered time and for any duration by maximizing the compounded posterior probability of traffic risk. Extensive experiments demonstrate that DeepMF excels in terms of risk management, flexibility, and diversity, showcasing outstanding performance in simulating a wide range of realistic, high-risk traffic scenarios.