# DDD17: End-To-End DAVIS Driving Dataset

arXiv: https://arxiv.org/abs/1711.01458 · HTML: https://mlsanity.com/paper/1711.01458/

## Key facts

- Cited by 84 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2017.

## Built on by (most cited first, 30 of 84)

- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks](https://mlsanity.com/paper/2302.08890/) (2023)
- [ESS: Learning Event-based Semantic Segmentation from Still Images](https://mlsanity.com/paper/2203.10016/) (2022)
- [Recent Event Camera Innovations: A Survey](https://mlsanity.com/paper/2408.13627/) (2024)
- [A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook](https://mlsanity.com/paper/2401.01454/) (2024)
- [Beyond Classification: Directly Training Spiking Neural Networks for Semantic Segmentation](https://mlsanity.com/paper/2110.07742/) (2021)
- [Event Camera Data Pre-training](https://mlsanity.com/paper/2301.01928/) (2023)
- [EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge Distillation](https://mlsanity.com/paper/2111.12341/) (2021)
- [TUM-VIE: The TUM Stereo Visual-Inertial Event Dataset](https://mlsanity.com/paper/2108.07329/) (2021)
- [OpenESS: Event-based Semantic Scene Understanding with Open Vocabularies](https://mlsanity.com/paper/2405.05259/) (2024)
- [Bridging the Gap between Events and Frames through Unsupervised Domain Adaptation](https://mlsanity.com/paper/2109.02618/) (2021)
- [TimeReplayer: Unlocking the Potential of Event Cameras for Video Interpolation](https://mlsanity.com/paper/2203.13859/) (2022)
- [Dual Transfer Learning for Event-based End-task Prediction via Pluggable Event to Image Translation](https://mlsanity.com/paper/2109.01801/) (2021)
- [EVIMO2: An Event Camera Dataset for Motion Segmentation, Optical Flow, Structure from Motion, and Visual Inertial Odometry in Indoor Scenes with Monocular or Stereo Algorithms](https://mlsanity.com/paper/2205.03467/) (2022)
- [Embracing Events and Frames with Hierarchical Feature Refinement Network for Object Detection](https://mlsanity.com/paper/2407.12582/) (2024)
- [Ev-TTA: Test-Time Adaptation for Event-Based Object Recognition](https://mlsanity.com/paper/2203.12247/) (2022)
- [Learning Optical Flow from Event Camera with Rendered Dataset](https://mlsanity.com/paper/2303.11011/) (2023)
- [HALSIE: Hybrid Approach to Learning Segmentation by Simultaneously Exploiting Image and Event Modalities](https://mlsanity.com/paper/2211.10754/) (2022)
- [Exploring Event-driven Dynamic Context for Accident Scene Segmentation](https://mlsanity.com/paper/2112.05006/) (2021)
- [Motion Robust High-Speed Light-Weighted Object Detection With Event Camera](https://mlsanity.com/paper/2208.11602/) (2022)
- [Open-sourced Data Ecosystem in Autonomous Driving: the Present and Future](https://mlsanity.com/paper/2312.03408/) (2023)
- [Event Camera Data Dense Pre-training](https://mlsanity.com/paper/2311.11533/) (2023)
- [Emergent Visual Sensors for Autonomous Vehicles](https://mlsanity.com/paper/2205.09383/) (2022)
- [Dataset and Benchmark: Novel Sensors for Autonomous Vehicle Perception](https://mlsanity.com/paper/2401.13853/) (2024)
- [SEVD: Synthetic Event-based Vision Dataset for Ego and Fixed Traffic Perception](https://mlsanity.com/paper/2404.10540/) (2024)
- [A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services](https://mlsanity.com/paper/2304.14271/) (2023)
- [TUMTraf Event: Calibration and Fusion Resulting in a Dataset for Roadside Event-Based and RGB Cameras](https://mlsanity.com/paper/2401.08474/) (2024)
- [Ad-datasets: a meta-collection of data sets for autonomous driving](https://mlsanity.com/paper/2202.01909/) (2022)
- [EventFly: Event Camera Perception from Ground to the Sky](https://mlsanity.com/paper/2503.19916/) (2025)
- [Accurate and Efficient Event-based Semantic Segmentation Using Adaptive Spiking Encoder-Decoder Network](https://mlsanity.com/paper/2304.11857/) (2023)

## Abstract (arXiv)

Event cameras, such as dynamic vision sensors (DVS), and dynamic and active-pixel vision sensors (DAVIS) can supplement other autonomous driving sensors by providing a concurrent stream of standard active pixel sensor (APS) images and DVS temporal contrast events. The APS stream is a sequence of standard grayscale global-shutter image sensor frames. The DVS events represent brightness changes occurring at a particular moment, with a jitter of about a millisecond under most lighting conditions. They have a dynamic range of >120 dB and effective frame rates >1 kHz at data rates comparable to 30 fps (frames/second) image sensors. To overcome some of the limitations of current image acquisition technology, we investigate in this work the use of the combined DVS and APS streams in end-to-end driving applications. The dataset DDD17 accompanying this paper is the first open dataset of annotated DAVIS driving recordings. DDD17 has over 12 h of a 346x260 pixel DAVIS sensor recording highway and city driving in daytime, evening, night, dry and wet weather conditions, along with vehicle speed, GPS position, driver steering, throttle, and brake captured from the car's on-board diagnostics interface. As an example application, we performed a preliminary end-to-end learning study of using a convolutional neural network that is trained to predict the instantaneous steering angle from DVS and APS visual data.