Driving Datasets Literature Review
arXiv:1910.11968
Abstract
This report is a survey of the different autonomous driving datasets which have been published up to date. The first section introduces the many sensor types used in autonomous driving datasets. The second section investigates the calibration and synchronization procedure required to generate accurate data. The third section describes the diverse driving tasks explored by the datasets. Finally, the fourth section provides comprehensive lists of datasets, mainly in the form of tables.
References in corpus (25)
- YOLOv3: An Incremental Improvement
- Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges
- A Review on Deep Learning Techniques Applied to Semantic Segmentation
- Hand-Eye Calibration
- The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception
- Object Detection in 20 Years: A Survey
- The EuroCity Persons Dataset: A Novel Benchmark for Object Detection
- Deep Learning for Generic Object Detection: A Survey
- Spatial As Deep: Spatial CNN for Traffic Scene Understanding
- Learning a Driving Simulator
- Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture
- Joint Attention in Autonomous Driving (JAAD)
- CityPersons: A Diverse Dataset for Pedestrian Detection
- Multi-View 3D Object Detection Network for Autonomous Driving
- 3D Object Proposals using Stereo Imagery for Accurate Object Class Detection
- A Commute in Data: The comma2k19 Dataset
- The StreetLearn Environment and Dataset
- The Stixel world: A medium-level representation of traffic scenes
- Combining Stereo Disparity and Optical Flow for Basic Scene Flow
- Scene Flow Estimation: A Survey
- LiDAR point clouds correction acquired from a moving car based on CAN-bus data
- BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving
- Highway Driving Dataset for Semantic Video Segmentation
- On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach
- IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments