Domain Adaptation Autonomous Driving

These methods overlook a change in environments in the real world as time goes by.

Domain adaptation autonomous driving. 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. We all dream of a future in which autonomous cars can drive us to every corner of the world. Especially autonomous navigation in outdoor environment has been in trouble since acquiring massive dataset of various environments is difficult and environment always changes dynamically. Numerous researchers and companies are working day and night to chase this dream by overcoming scientific and technological barriers.

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. 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. In this paper we apply domain adaptation with adversarial learning framework to uav autonomous navigation. 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. A new unsupervised domain adaptation method is proposed in this paper to solve the object detection problem in the field of autonomous driving. These methods overlook a change in environments in the real world as time goes by. Unsupervised domain adaptation uda is essential for autonomous driving due to a lack of labeled real world road images.

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.