# DeepCrashTest: Turning Dashcam Videos into Virtual Crash Tests for Automated Driving Systems

arXiv: https://arxiv.org/abs/2003.11766 · HTML: https://mlsanity.com/paper/2003.11766/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2020.

## Built on by (most cited first, 6 of 6)

- [From Dashcam Videos to Driving Simulations: Stress Testing Automated Vehicles against Rare Events](https://mlsanity.com/paper/2411.16027/) (2024)
- [Formalizing and Evaluating Requirements of Perception Systems for Automated Vehicles using Spatio-Temporal Perception Logic](https://mlsanity.com/paper/2206.14372/) (2022)
- [Decictor: Towards Evaluating the Robustness of Decision-Making in Autonomous Driving Systems](https://mlsanity.com/paper/2402.18393/) (2024)
- [My Precious Crash Data: Barriers and Opportunities in Encouraging Autonomous Driving Companies to Share Safety-Critical Data](https://mlsanity.com/paper/2504.17792/) (2025)
- [OpenVO: Open-World Visual Odometry with Temporal Dynamics Awareness](https://mlsanity.com/paper/2602.19035/) (2026)
- [Dash2Sim: Closed-Loop Driving Simulation from in-the-wild Dashcam Videos](https://mlsanity.com/paper/2606.07366/) (2026)

## Abstract (arXiv)

The goal of this paper is to generate simulations with real-world collision scenarios for training and testing autonomous vehicles. We use numerous dashcam crash videos uploaded on the internet to extract valuable collision data and recreate the crash scenarios in a simulator. We tackle the problem of extracting 3D vehicle trajectories from videos recorded by an unknown and uncalibrated monocular camera source using a modular approach. A working architecture and demonstration videos along with the open-source implementation are provided with the paper.