Domain Adaptation Autonomous Driving
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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.
Domain adaptation autonomous driving. These methods overlook a change in environments in the real world as time goes by. 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. A new unsupervised domain adaptation method is proposed in this paper to solve the object detection problem in the field of autonomous driving.
Numerous researchers and companies are working day and night to chase this dream by overcoming scientific and technological barriers. Extensive experiments are implemented to certify the efficacy of the proposed method. Most of the existing uda methods however have focused on a single step domain adaptation synthetic to real.
Unmanned aerial vehicle uav autonomous driving gets popular attention in machine learning field. Especially autonomous navigation in outdoor environment has been in trouble since acquiring massive dataset of various environments is difficult and environment always changes dynamically. The challenges will focus on domain adaptation of object detection and tracking based on the bdd100k from berkeley deepdrive and d 2 city from didi chuxing datasets.
Thus developing a domain adaptation method for sequentially changing target. Most of the existing uda methods however have focused on a single step domain adaptation synthetic to real. Thus developing a domain adaptation method for sequentially changing target.
In this paper we apply domain adaptation with adversarial learning framework to uav autonomous navigation. These methods overlook a change in environments in the real world as time goes by.
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