EV-TTC: Event-Based Time-To-Collision Estimation

May 7, 2025 ยท View on GitHub

Paper

EV-TTC is a high-speed Time-To-Collision (TTC) estimation system created using a fast multi-temporal scale event representation and a slim dilated convolution network. This repository provides the complete pipeline for TTC estimation, including C++ ROS2 nodes, neural network training, and dataset preparation.

https://github.com/user-attachments/assets/4d9b3bb7-925e-4cbf-9c4e-5b32667ced9f

Key Contributions

  1. High-Speed Multi-Temporal Scale Filter
    • Runs with a latency of 3.3ms at 75 Million Events Per Second on a Jetson Orin NX 16GB with events from 720x720 image resolution.
  2. EV-Slim Neural Network
    • A lightweight, high-speed network optimized with TensorRT for minimal latency.
  3. T2CEFT^2CEF Dataset
    • A new dataset created from the M3ED dataset, consisting of ground truth optical flow and TTC for event camera sequences.

Repository Structure

1. ev_ttc/: ROS2 Node for Real-Time TTC Estimation

This folder contains the C++ ROS2 implementation for real-time TTC estimation. For more details, refer to the ev_ttc README.

2. model/: Neural Network Training and Inference

This folder contains the PyTorch Lightning pipeline for training and evaluating the EV-Slim neural network. For more details, refer to the model README.

3. TTCEF/: Dataset Preparation

This folder contains scripts for preparing the T2CEFT^2CEF dataset from the M3ED dataset. For more details, refer to the TTCEF README.

Citation

@ARTICLE{ev_ttc,
  author={Bisulco, Anthony and Kumar, Vijay and Daniilidis, Kostas},
  journal={IEEE Robotics and Automation Letters}, 
  title={EV-TTC: Event-Based Time to Collision under Low Light Conditions}, 
  year={2025},
  pages={1-8},
  doi={10.1109/LRA.2025.3565150}}

If you use T2CEFT^2CEF, please additionally cite M3ED:

@INPROCEEDINGS{m3ed,
  author={Chaney, Kenneth and Cladera, Fernando and Wang, Ziyun and Bisulco, Anthony and Hsieh, M. Ani and Korpela, Christopher and Kumar, Vijay and Taylor, Camillo J. and Daniilidis, Kostas},
  booktitle={IEEE Conf. Comput. Vis. Pattern Recog. Workshop}, 
  title={{M3ED}: Multi-Robot, Multi-Sensor, Multi-Environment Event Dataset}, 
  month={July},
  year={2023}}