An overview and experimental study of learning-based optimization algorithms for the vehicle routing problem
The vehicle routing problem (VRP) is a typical discrete combinatorial optimization problem,
and many models and algorithms have been proposed to solve the VRP and its variants …
and many models and algorithms have been proposed to solve the VRP and its variants …
[HTML][HTML] Path planning algorithms in the autonomous driving system: A comprehensive review
This comprehensive review focuses on the Autonomous Driving System (ADS), which aims
to reduce human errors that are the reason for about 95% of car accidents. The ADS …
to reduce human errors that are the reason for about 95% of car accidents. The ADS …
Developing a deep Q-learning and neural network framework for trajectory planning
VSR Kosuru, AK Venkitaraman - European Journal of Engineering and …, 2022 - ej-eng.org
Autonomy field, every vehicle is occupied with some kind or alter driver assist features in
order to compensate driver comfort. Expansion further to fully Autonomy is extremely …
order to compensate driver comfort. Expansion further to fully Autonomy is extremely …
A hybrid deep reinforcement learning for autonomous vehicles smart-platooning
The development of Autonomous Vehicles (AVs) envisions the promising technology of
future Intelligent Transportation Systems (ITS). However, the complex road structures and …
future Intelligent Transportation Systems (ITS). However, the complex road structures and …
Artificial intelligence (AI)-empowered intrusion detection architecture for the internet of vehicles
Recent advances in the Internet of Things (IoT) and the adoption of IoT in vehicular networks
have led to a new and promising paradigm called the Internet of Vehicles (IoV). However …
have led to a new and promising paradigm called the Internet of Vehicles (IoV). However …
Local path planning: Dynamic window approach with virtual manipulators considering dynamic obstacles
M Kobayashi, N Motoi - IEEE Access, 2022 - ieeexplore.ieee.org
Local path planning considering static and dynamic obstacles for a mobile robot is one of
challenging research topics. Conventional local path planning methods generate path …
challenging research topics. Conventional local path planning methods generate path …
Reinforcement learning-empowered mobile edge computing for 6G edge intelligence
Mobile edge computing (MEC) is considered a novel paradigm for computation-intensive
and delay-sensitive tasks in fifth generation (5G) networks and beyond. However, its …
and delay-sensitive tasks in fifth generation (5G) networks and beyond. However, its …
Decision-making strategy on highway for autonomous vehicles using deep reinforcement learning
Autonomous driving is a promising technology to reduce traffic accidents and improve
driving efficiency. In this work, a deep reinforcement learning (DRL)-enabled decision …
driving efficiency. In this work, a deep reinforcement learning (DRL)-enabled decision …
A multiagent deep reinforcement learning approach for path planning in autonomous surface vehicles: The Ypacaraí lake patrolling case
SY Luis, DG Reina, SLT Marín - IEEE Access, 2021 - ieeexplore.ieee.org
Autonomous surfaces vehicles (ASVs) excel at monitoring and measuring aquatic nutrients
due to their autonomy, mobility, and relatively low cost. When planning paths for such …
due to their autonomy, mobility, and relatively low cost. When planning paths for such …
[HTML][HTML] Large-scale vehicle platooning: Advances and challenges in scheduling and planning techniques
Through vehicle-to-vehicle (V2V) communication, autonomizing a vehicle platoon can
significantly reduce the distance between vehicles, thereby reducing air resistance and …
significantly reduce the distance between vehicles, thereby reducing air resistance and …