Driving Simulator Trajectory-Level Analysis of Truck Drivers' Behavioral Alteration in Connected Vehicles Environment Under Fog with Complex Roadway Geometry

Foggy weather increases crash likelihood when coinciding with roadway geometry changes inconsistent with drivers' expectations. The situation might be exacerbated for heavy trucks having to evade critical safety events because of the vehicles' maneuverability limitations, imposing prime safety challenges on major freight corridors like Interstate-80 (I-80) in the U.S. Aligned with the connected vehicle (CV) pilot program on I-80 in Wyoming, this study intends to unveil how CV technology alleviates safety concerns in this regard. To this aim, a with/without analysis approach was performed utilizing a high-fidelity truck driving simulator. Twenty-three professional truck drivers were recruited to drive the simulator in CV scenario with traveler information messages, including foggy weather ahead and an advisory speed of 45 mph, and in a non-CV counterpart without notifications. Longitudinal and lateral drivers' behaviors were quantified by kinematic-based surrogate measures of safety (K-SMoS) characterized on vehicles' trajectory, including longitudinal speed, lateral speed, steering, their corresponding spatial standard deviations, and the coefficient of variation of longitudinal speed. The central tendency and dispersion of K-SMoS distributions were compared between CVs and non-CVs throughout the simulated roadway. Results showed immediate truck drivers' compliance to CV notifications, which was more apparent in their longitudinal driving behaviors. On a horizontal curve with poor visibility, statistically significant reductions in central tendency and dispersion of K-SMoS distributions up to 67% in CVs were observed, minimizing the crash risk in CV environments. Besides, findings revealed that exposure to the CV notifications minimized drivers' behavior uncertainty, manifesting in their improved situational awareness and enhancing the safety performance of the traffic stream.