# SEVD: Synthetic Event-based Vision Dataset for Ego and Fixed Traffic Perception

arXiv: https://arxiv.org/abs/2404.10540 · HTML: https://mlsanity.com/paper/2404.10540/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 44 references that are in the map.
- First posted on arXiv 12 Apr 2024.

## Builds on (most cited first, 20 of 20)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception](https://mlsanity.com/paper/1801.10202/) (2018)
- [HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification](https://mlsanity.com/paper/1803.07913/) (2018)
- [The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM](https://mlsanity.com/paper/1610.08336/) (2016)
- [Learning to Detect Objects with a 1 Megapixel Event Camera](https://mlsanity.com/paper/2009.13436/) (2020)
- [Recurrent Vision Transformers for Object Detection with Event Cameras](https://mlsanity.com/paper/2212.05598/) (2022)
- [Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensors](https://mlsanity.com/paper/1912.12147/) (2019)
- [A Large Scale Event-based Detection Dataset for Automotive](https://mlsanity.com/paper/2001.08499/) (2020)
- [DDD17: End-To-End DAVIS Driving Dataset](https://mlsanity.com/paper/1711.01458/) (2017)
- [Object Tracking by Jointly Exploiting Frame and Event Domain](https://mlsanity.com/paper/2109.09052/) (2021)
- [DDD20 End-to-End Event Camera Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering Prediction](https://mlsanity.com/paper/2005.08605/) (2020)
- [BlinkFlow: A Dataset to Push the Limits of Event-based Optical Flow Estimation](https://mlsanity.com/paper/2303.07716/) (2023)
- [eTraM: Event-based Traffic Monitoring Dataset](https://mlsanity.com/paper/2403.19976/) (2024)
- [Comparing Representations in Tracking for Event Camera-based SLAM](https://mlsanity.com/paper/2104.09887/) (2021)
- [Person Re-Identification without Identification via Event Anonymization](https://mlsanity.com/paper/2308.04402/) (2023)
- [Fast Trajectory End-Point Prediction with Event Cameras for Reactive Robot Control](https://mlsanity.com/paper/2302.13796/) (2023)

## Built on by (most cited first, 15 of 15)

- [Recent Event Camera Innovations: A Survey](https://mlsanity.com/paper/2408.13627/) (2024)
- [Helios: An extremely low power event-based gesture recognition for always-on smart eyewear](https://mlsanity.com/paper/2407.05206/) (2024)
- [Event Quality Score (EQS): Assessing the Realism of Simulated Event Camera Streams via Distances in Latent Space](https://mlsanity.com/paper/2504.12515/) (2025)
- [How Real is CARLAs Dynamic Vision Sensor? A Study on the Sim-to-Real Gap in Traffic Object Detection](https://mlsanity.com/paper/2506.13722/) (2025)
- [A Survey of 3D Reconstruction with Event Cameras](https://mlsanity.com/paper/2505.08438/) (2025)
- [SEBVS: Synthetic Event-based Visual Servoing for Robot Navigation and Manipulation](https://mlsanity.com/paper/2508.17643/) (2025)
- [SEPose: A Synthetic Event-based Human Pose Estimation Dataset for Pedestrian Monitoring](https://mlsanity.com/paper/2507.11910/) (2025)
- [Event-based Graph Representation with Spatial and Motion Vectors for Asynchronous Object Detection](https://mlsanity.com/paper/2507.15150/) (2025)
- [Helios 2.0: A Robust, Ultra-Low Power Gesture Recognition System Optimised for Event-Sensor based Wearables](https://mlsanity.com/paper/2503.07825/) (2025)
- [LiFR-Seg: Anytime High-Frame-Rate Segmentation via Event-Guided Propagation](https://mlsanity.com/paper/2603.21115/) (2026)
- [eSkiTB: A Synthetic Event-based Dataset for Tracking Skiers](https://mlsanity.com/paper/2601.06647/) (2026)
- [DIMOS: Disentangling Instance-level Moving Object Segmentation](https://mlsanity.com/paper/2606.12826/) (2026)
- [Event-RGB Adaptive Tracking for Nighttime Highway Perception](https://mlsanity.com/paper/2607.11646/) (2026)
- [Neuromorphic Object Detection: An In-Depth Study and Future Directions](https://mlsanity.com/paper/2607.23576/) (2026)
- [Cooking beyond Frames: A Stereo Event Camera Dataset in the Kitchen](https://mlsanity.com/paper/2608.04865/) (2026)

## Abstract (arXiv)

Recently, event-based vision sensors have gained attention for autonomous driving applications, as conventional RGB cameras face limitations in handling challenging dynamic conditions. However, the availability of real-world and synthetic event-based vision datasets remains limited. In response to this gap, we present SEVD, a first-of-its-kind multi-view ego, and fixed perception synthetic event-based dataset using multiple dynamic vision sensors within the CARLA simulator. Data sequences are recorded across diverse lighting (noon, nighttime, twilight) and weather conditions (clear, cloudy, wet, rainy, foggy) with domain shifts (discrete and continuous). SEVD spans urban, suburban, rural, and highway scenes featuring various classes of objects (car, truck, van, bicycle, motorcycle, and pedestrian). Alongside event data, SEVD includes RGB imagery, depth maps, optical flow, semantic, and instance segmentation, facilitating a comprehensive understanding of the scene. Furthermore, we evaluate the dataset using state-of-the-art event-based (RED, RVT) and frame-based (YOLOv8) methods for traffic participant detection tasks and provide baseline benchmarks for assessment. Additionally, we conduct experiments to assess the synthetic event-based dataset's generalization capabilities. The dataset is available at https://eventbasedvision.github.io/SEVD