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A novel mountain driving unity simulated environment for autonomous vehicles


Li, X and Cao, Z and Bai, Q, A novel mountain driving unity simulated environment for autonomous vehicles, Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2-9 February 2021, Virtual Conference, Online (In Press) [Refereed Conference Paper]

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Copyright 2021, Association for the Advancement of Artificial Intelligence

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The simulated driving environment provides a low cost and time-saving platform to test the performance of the autonomous vehicle by linkage with existing machine learning approaches. However, most of existing simulated driving environments focus on building flat roads in urban areas. Still, they neglected to endeavour the tough steep, curvy hill roads, such as mountain paths around suburban areas. In this study, by deploying in Unity engine, we developed the first complex mountain driving simulated environment with characterizing continuous curves and up/downhill. Then, two state-of-art reinforcement learning (RL) algorithms are used to train a vehicle agent and test the performance of autonomous vehicles in our developed simulated environment. Also, we set 5 different levels of vehicle’s speeds and observe the cumulative rewards during the vehicle agent training. Our demonstration presents the developed environment supports for complex mountain scenario configurations and RL-based autonomous vehicles, and our findings show that the vehicle agent could achieve high cumulative rewards during the training stage, suggesting that our work is a potential new simulation environment for autonomous vehicles research. The demonstration video can be viewed via the link:

Item Details

Item Type:Refereed Conference Paper
Keywords:driving, autonomous vehicles
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Autonomous agents and multiagent systems
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Artificial intelligence
UTAS Author:Li, X (Mr Xiaohu Li)
UTAS Author:Cao, Z (Dr Zehong Cao)
UTAS Author:Bai, Q (Dr Quan Bai)
ID Code:141886
Year Published:In Press
Deposited By:Information and Communication Technology
Deposited On:2020-12-01
Last Modified:2021-04-07

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