A Novel Mountain Driving Unity Simulated Environment for Autonomous Vehicles

Authors

  • Xiaohu Li University of Tasmania, Australia
  • Zehong Cao University of Tasmania, Australia
  • Quan Bai University of Tasmania, Australia

Keywords:

Mountain Driving Environment, Autonomous Vehicles, Deep Reinforcement Learning

Abstract

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: https://youtu.be/0wSqGeCn-NU.

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Published

2021-05-18

How to Cite

Li, X., Cao, Z., & Bai, Q. (2021). A Novel Mountain Driving Unity Simulated Environment for Autonomous Vehicles. Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 16075-16077. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/18016