Immersive and Multimodal Interfaces for Radar and Spatial Data Visualization in Critical Operational Environments: A Scoping Review

In safety-critical domains such as aviation, autonomous driving, and defense, operators must process complex spatial and radar data under severe time pressure. Traditional two-dimensional interfaces often force a “head-down” posture, increasing cognitive workload and impairing situational awareness. Extended reality and multimodal interfaces—incorporating gesture, voice, and haptic feedback—offer a promising paradigm to mitigate these limitations by enabling natural, egocentric data visualization. This scoping review systematically maps the empirical evidence on immersive and multimodal interfaces designed for radar and spatial data visualization in critical operational environments. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines, a systematic search was conducted across five major databases for articles published between 2015 and 2025. Out of 538 unique records screened, 54 studies met the eligibility criteria and underwent structured data charting. The findings reveal a technological ecosystem heavily dominated by augmented reality and virtual reality, supplemented by non-extended reality multimodal baselines (n = 8) to evaluate sensory load distribution. While subjective metrics such as the NASA Task Load Index (n = 17, 31.4%) dominate current evaluation practices, there is a notable scarcity of objective real-time physiological biosensors (n = 7, 13%). Crucially, the synthesized data challenges uncritical technological optimism: while multimodal extended reality effectively mitigates visual bottlenecks, certain modalities like mid-air gestures frequently induce physical fatigue and a documented speed–accuracy trade-off. To fully realize the potential of immersive decision support systems, future research must prioritize standardized, ecologically valid evaluation frameworks and explore artificial intelligence-driven adaptive interfaces capable of dynamically modulating information density based on operator workload.