Resilient machine learning for networked cyber physical systems: A survey for machine learning security to securing machine learning for CPS
Cyber Physical Systems (CPS) are characterized by their ability to integrate the physical and
information or cyber worlds. Their deployment in critical infrastructure have demonstrated a …
information or cyber worlds. Their deployment in critical infrastructure have demonstrated a …
[HTML][HTML] A comprehensive survey on the application of deep and reinforcement learning approaches in autonomous driving
Abstract Recent advances in Intelligent Transport Systems (ITS) and Artificial Intelligence
(AI) have stimulated and paved the way toward the widespread introduction of Autonomous …
(AI) have stimulated and paved the way toward the widespread introduction of Autonomous …
[HTML][HTML] Automated vehicle-involved traffic flow studies: A survey of assumptions, models, speculations, and perspectives
Automated vehicles (AVs) are widely considered to play a crucial role in future transportation
systems because of their speculated capabilities in improving road safety, saving energy …
systems because of their speculated capabilities in improving road safety, saving energy …
Human-like decision making for autonomous driving: A noncooperative game theoretic approach
Considering that human-driven vehicles and autonomous vehicles (AVs) will coexist on
roads in the future for a long time, how to merge AVs into human drivers' traffic ecology and …
roads in the future for a long time, how to merge AVs into human drivers' traffic ecology and …
Deep reinforcement learning for autonomous internet of things: Model, applications and challenges
The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices
around the world, where the IoT devices collect and share information to reflect status of the …
around the world, where the IoT devices collect and share information to reflect status of the …
A survey of deep RL and IL for autonomous driving policy learning
Z Zhu, H Zhao - IEEE Transactions on Intelligent Transportation …, 2021 - ieeexplore.ieee.org
Autonomous driving (AD) agents generate driving policies based on online perception
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …
Harmonious lane changing via deep reinforcement learning
In this paper, we study how to learn a harmonious deep reinforcement learning (DRL) based
lane-changing strategy for autonomous vehicles without Vehicle-to-Everything (V2X) …
lane-changing strategy for autonomous vehicles without Vehicle-to-Everything (V2X) …
Edge computing and its role in Industrial Internet: Methodologies, applications, and future directions
Abstract Proliferation of Industrial Internet has dramatically changed the way we live and
work. It brings convenience to our society and sometimes requires real-time processing of …
work. It brings convenience to our society and sometimes requires real-time processing of …
Autonomous vehicle cut-in algorithm for lane-merging scenarios via policy-based reinforcement learning nested within finite-state machine
Lane-merging scenarios pose highly challenging problems for autonomous vehicles due to
conflicts of interest between the human-driven and cutting-in autonomous vehicles. Such …
conflicts of interest between the human-driven and cutting-in autonomous vehicles. Such …
Reinforcement learning-based motion planning for automatic parking system
J Zhang, H Chen, S Song, F Hu - IEEE Access, 2020 - ieeexplore.ieee.org
In automatic parking motion planning, multi-objective optimization including safety, comfort,
parking efficiency, and final parking performance should be considered. Most of the current …
parking efficiency, and final parking performance should be considered. Most of the current …