Quantification of Side-Visibility in Passenger Car Transportation: A Detailed Assessment on Influencing Variables of Vehicle Architecture with Mathematical Formulation and Statistical Analysis

Human-vehicle interaction is of paramount importance in designing the architecture of a passenger car. Driver visibility is one of the key metrics which influences overall situational awareness, enabling the drivers to understand and anticipate the behavior of other road users and adapt accordingly. Through a combination of real-world testing and simulation models, this paper investigates the impact of architectural parameters on side-visibility of drivers in passenger car. We collected 130 primary data points through a static visibility assessment with 65 volunteers across 4 cars capturing the meta-data of volunteers, vehicle architectural parameters and visibility on either side (driver and codriver). We scanned the volunteer’s anthropometry inside the vehicle and precisely mapped them to virtual vehicle environment. We introduce a metric, LVR (Lateral Visibility Range) to quantify side-visibility and arrived at theoretical formulations by integrating the architectural variables associated with visibility performance. LVR is applied across 367 benchmarked passenger cars, clustered segment-wise. We identified unique combinations of architectural variables that lead to exceeding performance in side-visibility. Theoretical formulations are validated with test data, confirming its stability and robustness. The results presented in this paper are useful in setting up vehicle targets and specifications for side-visibility in the early architectural phase.