# Virtual Immersive Reality for Stated Preference Travel Behaviour Experiments: A Case Study of Autonomous Vehicles on Urban Roads

arXiv: https://arxiv.org/abs/1802.06180 · HTML: https://mlsanity.com/paper/1802.06180/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2018.

## Built on by (most cited first, 6 of 6)

- [Choice modelling in the age of machine learning -- discussion paper](https://mlsanity.com/paper/2101.11948/) (2021)
- [Ordinal-ResLogit: Interpretable Deep Residual Neural Networks for Ordered Choices](https://mlsanity.com/paper/2204.09187/) (2022)
- [Debiased machine learning for estimating the causal effect of urban traffic on pedestrian crossing behaviour](https://mlsanity.com/paper/2212.11322/) (2022)
- [Towards a large-scale fused and labeled dataset of human pose while interacting with robots in shared urban areas](https://mlsanity.com/paper/2402.10077/) (2024)
- [A deep causal inference model for fully-interpretable travel behaviour analysis](https://mlsanity.com/paper/2405.01708/) (2024)
- [Vision-based Perception System for Automated Delivery Robot-Pedestrians Interactions](https://mlsanity.com/paper/2508.03541/) (2025)

## Abstract (arXiv)

Stated preference experiments have been known to suffer from the lack of realism. This issue is particularly visible when the scenario doesn't have a well understood prior reference e.g. in case of the autonomous vehicles related scenarios. We present Virtual Immersive Reality Environment (VIRE) that is capable of developing highly realistic, immersive, and interactive choice scenario. We demonstrate the use of VIRE in the pedestrian preferences related to autonomous vehicles and associated infrastructure changes on urban streets of Montr'eal. The results are compared with predominantly used approaches i.e. text-only and visual aid. We show that VIRE results in better understanding of the scenario and consistent results.