Future traffic scenarios will involve autonomous vehicles (AVs) interacting with other road users, including cyclists. For this interaction to be safe and socially acceptable, there is a need for development and testing environments that enable scenarios between AVs and cyclists including ethical, logistical, and safety constraints. While human-in-the-loop transport simulation offers an alternative, achieving high behavioral validity requires maximizing user presence and control. This study evaluates a modular, multi-sensory cycling simulator built on the CARLA autonomous driving engine. Through a fully randomized and counterbalanced within-subjects design with 31 participants, we analyzed four simulator configurations incorporating combinations of stereoscopic visualization, speed-dependent haptic eolic feedback, first-person visual self-embodiment, and 3D spatialized audio. Non parametric statistical evaluations revealed significant differences across the groups (χ2(3) = 60.47, p < 0.001, W = 0.650). Post-hoc pairwise comparisons indicated that, while head-mounted displays (HMDs) provide a level of spatial orientation, the concurrent integration of visual self-embodiment and speed-dependent airflow acts as a unifying sensory cue, significantly increasing system coherence. Conversely, the addition of 3D spatialised audio yielded no statistically significant improvement (Z = −0.06, p = 1.000), suggesting a sensory saturation effect. Cross-dimensional analysis confirmed a positive correlation between perceived presence and interface control (ρ = 0.693, p < 0.001). These findings suggest that a combination of visual feedback, haptic airflow, and self-embodiment may be enough to provide a realistic environment for cyclist–AV interaction studies including multiple agents.

GAMBOA VILLAFRUELA Carlos Javier;  HERNÁNDEZ PARRA Noelia;  FERNANDEZ LLORCA David; 

2026-07-24

MDPI

JRC147038

2076-3417 (online),   

https://www.mdpi.com/2076-3417/16/14/7296,    https://publications.jrc.ec.europa.eu/repository/handle/JRC147038,   

10.3390/app16147296 (online),