Unravelling the Impacts of Parameters on Surrogate Safety Measures for a Mixed Platoon

Abstract

With the precedence of connected automated vehicles (CAVs), car-following control technology is a promising way to enhance traffic safety. Although a variety of research has been conducted to analyze the safety enhancement by CAV technology, the parametric impact on CAV technology has not been systematically explored. Hence, this paper analyzes the parametric impacts on surrogate safety measures (SSMs) for a mixed vehicular platoon via a two-level analysis structure. To construct the active safety evaluation framework, numerical simulations were constructed which can generate trajectories for different kind of vehicles while considering communication and vehicle dynamics characteristics. Based on the trajectories, we analyzed parametric impacts upon active safety on two different levels. On the microscopic level, parameters including controller dynamic characteristics and equilibrium time headway of car-following policies were analyzed, which aimed to capture local and aggregated driving behavior’s impact on the vehicle. On the macroscopic level, parameters incorporating market penetration rate (MPR), vehicle topology, and vehicle-to-vehicle environment were extensively investigated to evaluate their impacts on aggregated platoon level safety caused by inter-drivers’ behavioral differences. As indicated by simulation results, an automated vehicle (AV) suffering from degradation is a potentially unsafe component in platoon, due to the loss of a feedforward control mechanism. Hence, the introduction of connected automated vehicles (CAVs) only start showing benefits to platoon safety from about 20% CAV MPR in this study. Furthermore, the analysis on vehicle platoon topology suggests that arranging all CAVs at the front of a mixed platoon assists in enhancing platoon SSM performances.

Surrogate Safety Measures: Review and Assessment in Real-World Mixed Traditional and Autonomous Vehicle Platoons

“…( [45]. Deviation in speed, acceleration, and jerk can be attributed as DV [46,47](See Appendix Table A1). Ding et al [48] introduced an average damping ratio (ADR) to show how vehicular oscillations are damped or amplified through a vehicle platoon.…”

“…Conversely, Ye & Yamamoto [47] did not consider any TTC threshold. Instead, the authors showed that the distribution of TTC values for different mixed traffic conditions varied based on the market penetration rate of CAVs.…”

Investigating the Impact of Automated Vehicles on the Safety and Operation of Low-Speed Urban Networks

“…Transportation networks face many challenges such as high travel times, trafc congestion, crashes, and environmental externalities such as emissions [1][2][3][4]. Congestion is the root of other problems such as increased fuel consumption and increased travel time.…”