Autonomous driving is the task of driving a vehicle without human conduction.
( Image credit: Exploring the Limitations of Behavior Cloning for Autonomous Driving )
Despite the advancement in the technology of autonomous driving cars, the safety of a self-driving car is still a challenging problem that has not been well studied.
One fundamental building block of an autonomous vehicle is the ability to build a map of the environment and localize itself on such a map.
One of the main factors that contributed to the large advances in autonomous driving is the advent of deep learning.
This paper describes the implementation of a control system based on ten different hand gestures, providing a useful approach for the implementation of better user-friendly human-machine interfaces.
This paper investigates how a Bayesian RL technique, based on an ensemble of neural networks with additional randomized prior functions (RPF), can be used to estimate the uncertainty of decisions in autonomous driving.
In this paper, we now take it a step further by introducing CMRNet++, which is a significantly more robust model that not only generalizes to new places effectively, but is also independent of the camera parameters.
In this paper, we complement the exiting datasets with a new, large, and diverse dehazing dataset containing real outdoor scenes from HD 3D videos.
Furthermore, to deal with the occlusion in X-ray images detection, we propose the De-occlusion Attention Module (DOAM), a plug-and-play module that can be easily inserted into and thus promote most popular detectors.