The Needs and Trends of Urban Simulation Platforms- A Review

2021 ◽  
Author(s):  
Yun-Tsui Chang ◽  
Aritra Pal ◽  
Jürgen Hackl ◽  
Shang-Hsien Hsieh
Keyword(s):  
Urban Simulation

Bounding Volume Hierarchies for Detecting Collision in Urban Simulation

2008 ◽  
Author(s):  
Hamzah Asyrani Sulaiman ◽  
Abdullah Bade ◽  
Daut Daman ◽  
Mohd Shahrizal Sunar
Keyword(s):  
Bounding Volume ◽  
Urban Simulation ◽  
Bounding Volume Hierarchies

scholarly journals Deep reinforcement learning based control for Autonomous Vehicles in CARLA

2022 ◽  
Author(s):  
Óscar Pérez-Gil ◽  
Rafael Barea ◽  
Elena López-Guillén ◽  
Luis M. Bergasa ◽  
Carlos Gómez-Huélamo ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Vehicle Control ◽  
Data Sources ◽  
Simulation Environment ◽  
Urban Simulation ◽  
Policy Gradient ◽  
Almost All ◽  
Control Layer

AbstractNowadays, Artificial Intelligence (AI) is growing by leaps and bounds in almost all fields of technology, and Autonomous Vehicles (AV) research is one more of them. This paper proposes the using of algorithms based on Deep Learning (DL) in the control layer of an autonomous vehicle. More specifically, Deep Reinforcement Learning (DRL) algorithms such as Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) are implemented in order to compare results between them. The aim of this work is to obtain a trained model, applying a DRL algorithm, able of sending control commands to the vehicle to navigate properly and efficiently following a determined route. In addition, for each of the algorithms, several agents are presented as a solution, so that each of these agents uses different data sources to achieve the vehicle control commands. For this purpose, an open-source simulator such as CARLA is used, providing to the system with the ability to perform a multitude of tests without any risk into an hyper-realistic urban simulation environment, something that is unthinkable in the real world. The results obtained show that both DQN and DDPG reach the goal, but DDPG obtains a better performance. DDPG perfoms trajectories very similar to classic controller as LQR. In both cases RMSE is lower than 0.1m following trajectories with a range 180-700m. To conclude, some conclusions and future works are commented.

scholarly journals The Grey Zone Test Range Integrated Urban Simulation Environment.

2020 ◽  
Author(s):  
Andjelka Kelic ◽  
Walter Beyeler ◽  
Roger Mitchell ◽  
Michael Bernard ◽  
Casey Doyle ◽  
...  
Keyword(s):  
Grey Zone ◽  
Simulation Environment ◽  
Test Range ◽  
Urban Simulation

scholarly journals Cellular Automata and Urban Simulation: Where Do We Go from Here?

2001 ◽  
Vol 28 (2) ◽  
pp. 163-168 ◽  
Author(s):  
Paul M Torrens ◽  
David O'Sullivan
Keyword(s):  
Cellular Automata ◽  
Urban Simulation
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