A Virtual Reality Training System for Automotive Engines Assembly and Disassembly
Not on the map. Why?
Abstract (arXiv)
Automotive engine assembly and disassembly are common and crucial programs in the automotive industry. Traditional education trains students to learn automotive engine assembly and disassembly in lecture courses and then to operate with physical engines, which are generally low effectiveness and high cost. In this work, we developed a multi-layer structured Virtual Reality (VR) system to provide students with training in automotive engine (Buick Verano) assembly and disassembly. We designed the VR training system with The VR training system is designed to have several major features, including replaceable engine parts and reusable tools, friendly user interfaces and guidance, and bottom-up designed multi-layer architecture, which can be extended to various engine models. The VR system is evaluated with controlled experiments of two groups of students. The results demonstrate that our VR training system provides remarkable usability in terms of effectiveness and efficiency. Currently, our VR system has been demonstrated and employed in the courses of Chinese colleges to train students in automotive engine assembly and disassembly. A free-to-use executable file (Microsoft Windows) and open-source code are available at https://github.com/LadissonLai/SUSTech_VREngine for facilitating the development of VR systems in the automotive industry. Finally, a video describing the operations in our VR training system is available at https://www.youtube.com/watch?v=yZe4YTwwAC4
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 1 of its 33 references that are in the map. First posted on arXiv 2 Nov 2023.
Builds on
1 of 33 references.
References · 1
- 2019Content Generation for Workforce Trainingcited by 1
Built on by
Citing papers per year: 2024: 6; 2025: 3.
9 papers.
- 2024Automatic parking planning control method based on improved A* algorithmcited by 1
- 2025ML-SceGen: A Multi-level Scenario Generation Frameworkcited by 1
- 2024Automated Parking Planning with Vision-Based BEV Approachcited by 0
- 2024Acceleration method for generating perception failure scenarios based on editing Markov processcited by 0
- 2024Deep Reinforcement Learning for Adverse Garage Scenario Generationcited by 0
- 2024Research on Reliable and Safe Occupancy Grid Prediction in Underground Parking Lotscited by 0
- 2024Development of Occupancy Prediction Algorithm for Underground Parking Lotscited by 0
- 2025Manboformer: Learning Gaussian Representations via Spatial-temporal Attention Mechanismcited by 0
- 2025Text2VR: Automated instruction Generation in Virtual Reality using Large language Models for Assembly Taskcited by 0
- 2025Text2VR: Automated instruction Generation in Virtual Reality using Large language Models for Assembly Task19 Jul 2025
- 2025Manboformer: Learning Gaussian Representations via Spatial-temporal Attention Mechanism6 Mar 2025
- 2025ML-SceGen: A Multi-level Scenario Generation Framework18 Jan 2025
- 2024Development of Occupancy Prediction Algorithm for Underground Parking Lots2 Sep 2024
- 2024Research on Reliable and Safe Occupancy Grid Prediction in Underground Parking Lots2 Jul 2024
- 2024Deep Reinforcement Learning for Adverse Garage Scenario Generation1 Jul 2024
- 2024Acceleration method for generating perception failure scenarios based on editing Markov process1 Jul 2024
- 2024Automated Parking Planning with Vision-Based BEV Approach24 May 2024
- 2024Automatic parking planning control method based on improved A* algorithm24 May 2024