CVPR 2019 Challenges on Domain Adaptation in Autonomous Driving

We all dream of a future in which autonomous cars can drive us to every corner of the world. Numerous researchers and companies are working day and night to chase this dream by overcoming scientific and technological barriers. One of the greatest challenges we still face is developing machine learning models that can be trained in a local environment and also perform well in new, unseen situations. For example, self-driving cars may utilize perception models to recognize drivable areas from images. Companies in Silicon Valley can build and perfect such a model using large local datasets from the Bay Area for training. However, if the same model were deployed in a snowy area such as Boston, it would likely perform miserably, because it has never seen snow before. Boston, during winter, and Silicon Valley, during any time of the year, can be labeled as separate domains for perception models, since they present clear differences in climate and challenges in perception. In other cases, domains may be much closer in nature, such as a city street and a nearby highway. The process of transferring knowledge and models between different domains in machine learning is called domain adaptation.

A large number of papers on domain adaptation of perception models have appeared in top publishing venues for machine learning and computer vision. However, most of these works focus on image classification and semantic segmentation. Hardly any attention has been paid to instance-level tasks, such as object detection and tracking, even though localization of nearby objects is arguably more important for autonomous driving. To foster the study of domain adaptation of perception models, Berkeley DeepDrive and Didi Chuxing are co-hosting two competitions in CVPR 2019 Workshop on Autonomous Driving. The challenges will focus on domain adaptation of object detection and tracking based on the BDD100K, from Berkeley DeepDrive, and D2-City, from Didi Chuxing, datasets. The domain of BDD100K covers US scenes, while D2-City was collected on China’s streets. The competitions ask participants to transfer object detectors from BDD100K to D2-City and object trackers from D2-city to BDD100K. More information about the challenges can be found on our website and D2-City.

Following our introduction of the BDD100K dataset, we have been busy working to provide more temporal annotations. Above is an example of object tracking annotation, created by our open-source annotation platform Scalabel. Some of the tracking labels are used in the domain adaptation challenge for object tracking. More data will be released this summer. Of course, we also have object tracking at night.

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