DReyeVR: Democratizing Virtual Reality Driving Simulation for Behavioural & Interaction Research
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Abstract (arXiv)
Simulators are an essential tool for behavioural and interaction research on driving, due to the safety, cost, and experimental control issues of on-road driving experiments. The most advanced simulators use expensive 360 degree projections systems to ensure visual fidelity, full field of view, and immersion. However, similar visual fidelity can be achieved affordably using a virtual reality (VR) based visual interface. We present DReyeVR, an open-source VR based driving simulator platform designed with behavioural and interaction research priorities in mind. DReyeVR (read "driver") is based on Unreal Engine and the CARLA autonomous vehicle simulator and has features such as eye tracking, a functional driving heads-up display (HUD) and vehicle audio, custom definable routes and traffic scenarios, experimental logging, replay capabilities, and compatibility with ROS. We describe the hardware required to deploy this simulator for under 5000 USD, much cheaper than commercially available simulators. Finally, we describe how DReyeVR may be leveraged to answer an interaction research question in an example scenario.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 2 of its 35 references that are in the map. First posted on arXiv 6 Jan 2022.
Builds on
2 of 35 references.
References · 2
- 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
- 2017AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehiclescited by 517
Built on by
Citing papers per year: 2023: 3; 2024: 3; 2025: 2.
8 papers.
- 2024Enhancing End-to-End Autonomous Driving Systems Through Synchronized Human Behavior Datacited by 3
- 2024What Did My Car Say? Impact of Autonomous Vehicle Explanation Errors and Driving Context On Comfort, Reliance, Satisfaction, and Driving Confidencecited by 3
- 2023Effects of Explanation Specificity on Passengers in Autonomous Drivingcited by 2
- 2025AEGIS: Human Attention-based Explainable Guidance for Intelligent Vehicle Systemscited by 2
- 2023Towards Driving Policies with Personality: Modeling Behavior and Style in Risky Scenarios via Data Collection in Virtual Realitycited by 1
- 2023Natural-language-driven Simulation Benchmark and Copilot for Efficient Production of Object Interactions in Virtual Road Scenescited by 1
- 2025DISC: Dataset for Analyzing Driving Styles In Simulated Crashes for Mixed Autonomycited by 1
- 2024A Transparency Paradox? Investigating the Impact of Explanation Specificity and Autonomous Vehicle Perceptual Inaccuracies on Passengerscited by 0
- 2025AEGIS: Human Attention-based Explainable Guidance for Intelligent Vehicle Systems8 Apr 2025
- 2025DISC: Dataset for Analyzing Driving Styles In Simulated Crashes for Mixed Autonomy28 Jan 2025
- 2024What Did My Car Say? Impact of Autonomous Vehicle Explanation Errors and Driving Context On Comfort, Reliance, Satisfaction, and Driving Confidence9 Sep 2024
- 2024Enhancing End-to-End Autonomous Driving Systems Through Synchronized Human Behavior Data20 Aug 2024
- 2024A Transparency Paradox? Investigating the Impact of Explanation Specificity and Autonomous Vehicle Perceptual Inaccuracies on Passengers16 Aug 2024
- 2023Natural-language-driven Simulation Benchmark and Copilot for Efficient Production of Object Interactions in Virtual Road Scenes7 Dec 2023
- 2023Effects of Explanation Specificity on Passengers in Autonomous Driving2 Jul 2023
- 2023Towards Driving Policies with Personality: Modeling Behavior and Style in Risky Scenarios via Data Collection in Virtual Reality8 Mar 2023