Passenger Cognitive Discomfort under Proxemic and Kinematic Factors during Expressway Overtaking Scenarios: A Field Study
Year: 2026
Type: article
Abstract: Passenger cognitive comfort, a low-stress psychological state driven by perceived safety during vehicle motion, has received limited real-world quantification, particularly for proxemic factors that are central to autonomous vehicle (AV) behavior design. Using an instrumented vehicle on a six-lane Indian expressway, this study analyzed 5,705 overtaking and being-overtaken events from 50 participants, combining immediate 7-point discomfort ratings with synchronized proxemic and kinematic measurements. Ratings were categorized into low (LD: 1–2), moderate (MD: 3–4), and high (HD: 5–7) discomfort using Rasch-based grouping. Cross-tabulation and Pearson chi-square tests confirmed significant associations between discomfort and all examined factors, with lateral clearance (LC) showing the strongest effect. A chi-square automatic interaction detection decision tree (10-fold cross-validated; 80.7% classification) identified LC as the dominant comfort envelope (χ 2 = 876.5, p < 0.01) with a sharp, nonlinear gradient. At LC ≤ 0.8 m, high discomfort reached 44.3% and MD+HD rose to 81.2%, whereas at LC > 3.3 m, high discomfort was eliminated (0.0%) and low discomfort increased to 95.0%. The model merged adjacent LC bands with statistically similar discomfort profiles, yielding interpretable clearance regimes. Within the intermediate regime (1.3–2.3 m), contextual moderation became critical: heavy goods vehicles (HGV) increased the likelihood of high discomfort, especially for rear-seat passengers (HD 13.9% vs. 7.3% front; ∼1.9×). For car/light commercial vehicle interactions, the maneuver type primarily affected low and moderate discomfort. These regime-based findings provide actionable rules for comfort-aware AV motion planning (such as clearance prioritization, HGV-aware behavior, occupant-sensitive tuning) and driver training (e.g., lateral buffer discipline), enhancing perceived safety and acceptance.
Authors: Mohan Kumar Somavarapu, Avijit Maji, Nan Zheng
Language: English
Subfield: Human Factors and Ergonomics
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,650
OpenAlex ID: W7225796001