DET: A High-Resolution DVS Dataset for Lane Extraction

Aarents

IEEE Robot. Autom. Lett.

et al. 2021

149

140

Once an academic venture, autonomous driving has received unparalleled corporate funding in the last decade. Still, operating conditions of current autonomous cars are mostly restricted to ideal scenarios. This means that driving in challenging illumination conditions such as night, sunrise, and sunset remains an open problem. In these cases, standard cameras are being pushed to their limits in terms of low light and high dynamic range performance. To address these challenges, we propose, DSEC, a new dataset that contains such demanding illumination conditions and provides a rich set of sensory data. DSEC offers data from a wide-baseline stereo setup of two color frame cameras and two highresolution monochrome event cameras. In addition, we collect lidar data and RTK GPS measurements, both hardware synchronized with all camera data. One of the distinctive features of this dataset is the inclusion of high-resolution event cameras. Event cameras have received increasing attention for their high temporal resolution and high dynamic range performance. However, due to their novelty, event camera datasets in driving scenarios are rare. This work presents the first high resolution, large scale stereo dataset with event cameras. The dataset contains 53 sequences collected by driving in a variety of illumination conditions and provides ground truth disparity for the development and evaluation of event-based stereo algorithms.

“…N-CARS [34] contains very short (100 ms) sequences for binary classification of cars. DET [37] is a dataset captured with a CeleX-V DVS camera for driving lane extraction. Brisbane-Event-…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

DSEC: A Stereo Event Camera Dataset for Driving Scenarios

Aarents

IEEE Robot. Autom. Lett.

et al. 2021

149

140

“…ADD [32] 0.1 --2D Bounding Box 1MP Detection [33] 4 -yes 0.9 --2D Bounding Box N-CARS [34] 0.01 --Binary Class DDD17 [35] 0.1 -no 0.1 --Vehicle Control, GPS DDD20 [36] 0.1 -no 0.1 --Vehicle Control, GPS DET [37] 1.0 --Lane Extraction Brisbane-Event-VPR [38] or not applicable respectively. ADD, N-CARS and DET do not feature a frame camera.…”

Section: Related Workmentioning

confidence: 99%

DSEC: A Stereo Event Camera Dataset for Driving Scenarios

Gehrig¹,

Aarents²,

Gehrig³

et al. 2021

Preprint

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)

“…DDD17 was limited in road types, weather and daylight conditions. Since then, MVSEC [24], DET [25], Event Camera Driving Sequences [26], and GET1 [27] datasets have been released. These datasets contain useful driving data with various label types, but none of them are E2E labeled with human driving.…”

Section: Arxiv:200508605v1 [Cscv] 18 May 2020mentioning

confidence: 99%

DDD20 End-to-End Event Camera Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering Prediction

Binas

Neil

et al. 2020

Neuromorphic event cameras are useful for dynamic vision problems under difficult lighting conditions. To enable studies of using event cameras in automobile driving applications, this paper reports a new end-to-end driving dataset called DDD20. The dataset was captured with a DAVIS camera that concurrently streams both dynamic vision sensor (DVS) brightness change events and active pixel sensor (APS) intensity frames. DDD20 is the longest event camera end-to-end driving dataset to date with 51h of DAVIS event+frame camera and vehicle human control data collected from 4000km of highway and urban driving under a variety of lighting conditions. Using DDD20, we report the first study of fusing brightness change events and intensity frame data using a deep learning approach to predict the instantaneous human steering wheel angle. Over all day and night conditions, the explained variance for human steering prediction from a Resnet-32 is significantly better from the fused DVS+APS frames (0.88) than using either DVS (0.67) or APS (0.77) data alone.