SLAV-Sim: A Framework for Self-Learning Autonomous Vehicle Simulation: SLAV-Sim

Jacob Crewe, ADITYA SUNIL HUMNABADKAR, YONGHUAI LIU, AMR AHMED, ARDHENDU BEHERA*

*Corresponding author for this work

Research output: Contribution to journalArticle (journal)peer-review

7 Downloads (Pure)

Abstract

With the advent of autonomous vehicles, sensors and algorithm testing have become crucial parts of the autonomous vehicle development cycle. Having access to real-world sensors and vehicles is a dream for researchers and small-scale original equipment manufacturers (OEMs) due to the software and hardware development life-cycle duration and high costs. Therefore, simulator-based virtual testing has gained traction over the years as the preferred testing method due to its low cost, efficiency, and effectiveness in executing a wide range of testing scenarios. Companies like ANSYS and NVIDIA have come up with robust simulators, and open-source simulators such as CARLA have also populated the market. However, there is a lack of lightweight and simple simulators catering to specific test cases. In this paper, we introduce the SLAV-Sim, a lightweight simulator that specifically trains the behaviour of a self-learning autonomous vehicle. This simulator has been created using the Unity engine and provides an end-to-end virtual testing framework for different reinforcement learning (RL) algorithms in a variety of scenarios using camera sensors and raycasts.
Original languageEnglish
Article number8649
Pages (from-to)1-22
JournalSensors
Volume23
Issue number20
Early online date23 Oct 2023
DOIs
Publication statusPublished - 23 Oct 2023

Keywords

  • Simulators
  • Reinforcement Learning
  • Autonomous Vehicle
  • Vehicle Behaviour Modelling
  • Testing Platform
  • testing platform
  • vehicle behaviour modelling
  • autonomous vehicle
  • simulators
  • reinforcement learning

Research Centres

  • Centre for Intelligent Visual Computing Research
  • Data and Complex Systems Research Centre

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