Autonomous driving (AD) agents generate driving policies based on online perception
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …

Currently, deep learning and IoT collaboration is heavily invading automotive applications
especially in autonomous driving throughout successful assistance functionalities. Crash …

Cyber-Physical Systems (CPS) continuously interact with their physical environments
through embedded software controllers that observe the environments and determine …

Few prior works study self-driving cars by deep learning with IoT collaboration. SDC-Net,
which is an end-to-end multitask self-driving car camera cocoon IoT-based system, is one of …

Vehicles can follow roads based on a forward-looking camera, but this has to be done
reliably in all circumstances. In daily traffic, they can encounter many unforeseen situations …

Artificial intelligence (AI) enhanced control system deployments are emerging as a viable
substitute to more traditional control system. In particular, deep learning techniques offer an …

The decision and planning system for autonomous driving in urban environments is hard to
design. Current methods predominantly rely on manually designed driving policies, which …

The proposed model is a novel deep learning model that combines end-to-end and multi-
task learning approaches for point-to-point navigation in autonomous driving. Our model …

An improved imitation learning algorithm based on Markov chain is proposed, aiming to
enhance the performance of autonomous driving in complex environments. Imitation …

All real-world applications require ongoing learning since static pre-trained models cannot
handle non-stationary data distributions. This extensive study is particularly interested in …