Canadian Adverse Driving Conditions dataset

By Matthew A. Pitropov, Danson Evan Garcia, Jason Rebello, Michael H. W. Smart, Carlos Wang, Krzysztof Czarnecki, Steven L. Waslander. Int. J. Robotics Res., 2020

305 citations recorded by the source corpus.

The Canadian Adverse Driving Conditions (CADC) dataset was collected with the Autonomoose autonomous vehicle platform, based on a modified Lincoln MKZ. The dataset, collected during winter within the Region of Waterloo, Canada, is the first autonomous driving dataset that focuses on adverse driving conditions specifically. It contains 7,000 frames of annotated data from 8 cameras (Ximea MQ013CG-E2), lidar (VLP-32C), and a GNSS+INS system (Novatel OEM638), collected through a variety of winter weather conditions. The sensors are time synchronized and calibrated with the intrinsic and extrinsic calibrations included in the dataset. Lidar frame annotations that represent ground truth for 3D object detection and tracking have been provided by Scale AI.

Subfields: Automotive Engineering, Environmental Engineering, Instrumentation

View Paper: https://www.semanticscholar.org/paper/3fa5acebaa27401976ed900157ebce1c2d9fd0cb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Luc Van Gool
2. Deva Ramanan
3. Dengxin Dai
4. Alexander Carballo
5. Christos Sakaridis

In Environmental Engineering

1. Luc Van Gool
2. Deva Ramanan
3. Dengxin Dai
4. Alexander Carballo
5. James Hays

Citations in the corpus

• ACDC: The Adverse Conditions Dataset with Correspondences for Semantic Driving Scene Understanding (2021, background): “While more recent large-scale sets [2,30] that cover adverse conditions, such as Waymo Open [42] and nuScenes [3], include bounding boxes, they still lack dense pixel-level semantic annotations, which are vital for real-world autonomous agents [63].”
• Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting (2023, methodology): “New sensor datasets for 3D object detection [4, 39, 34, 35, 21, 28, 16, 12, 36, 31] have led to influential detection methods such as anchor-based approaches like PointPillars [23], and more recent anchor-free approaches such as (1)This count includes private submissions not posted to the public lea”
• Perception and sensing for autonomous vehicles under adverse weather conditions: A survey (2021, methodology): “Canadian adverse driving conditions dataset (CADCD) [58].”