Tailored Immersive Environments: Advancing Neurodivergent Support Through Virtual Reality

Every day life tasks can present significant challenges for neurodivergent individuals, particularly those with Autism Spectrum Disorders (ASD) who are characterized by specific sensitivities. This contribution describes a virtual reality system that allows neurodivergent individuals to experience everyday situations in order to practice and implement strategies for overcoming their daily challenges. The key strength of the proposed system is the automatic personalization of the virtual environment, based on both the individual’s abilities and their specific training needs. The proposed method has been evaluated on four synthetic user profiles, also proposing a metric able to evaluate the variance of the features within the same difficulty level. The results show that the method can produce a significant number of scenarios for the various difficulty levels. Furthermore, within the same difficulty, there is a wide variance of the non-constrained features for the specific profile.

💡 Research Summary

The paper presents EASE VR (Empowering Accessible Social Engagement in Virtual Reality), a virtual‑reality training platform designed to help neurodivergent individuals—particularly those on the autism spectrum—practice everyday urban crossing tasks in a safe, controllable environment. Recognizing that rapid urbanisation creates sensory‑rich, cognitively demanding settings that can overwhelm individuals with atypical sensory filtering or heightened cognitive load, the authors argue that existing VR therapies lack sufficient personalization and rely heavily on therapist‑driven configuration.

EASE VR addresses this gap by automatically tailoring each scenario to a user’s specific sensory and cognitive profile. The core of the system is a parametric “Urban Crosswalk” scenario whose environment is defined by twelve configurable features: crossing type (short, long, double), lighting (day/night), weather (sunny/rain), pedestrian flow (low/medium/high), vehicle flow (low/medium/high), sudden sound distractors (church bell, helicopter, car waiting at red light), background noise levels (ambulance, baby crying, dogs barking), and traffic‑light complexity (none, basic, button, countdown, combined, broken). Each feature ϕi is normalized to the interval