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.

Highlights

  • Traffic safety, as a global issue, has been drawing considerable concentration from the public during the past decades

  • For a connected automated vehicles (CAVs), cooperative adaptive cruise control (CACC) is a predominant approach to deal with inter-vehicle longitudinal driving behaviors by applying detected parameters from the preceding vehicle and real-time information received via vehicle-to-vehicle (V2V) communication

  • An investigation of the safety influences of CAV market penetration rate (MPR) at intersections with different signalization degrees was conducted using surrogate safety measures (SSMs), in which the results showed a difference in SSM changing trend with low or high CAV penetration [23]

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Summary

Introduction

As a global issue, has been drawing considerable concentration from the public during the past decades. Rear-end crashes happen due to improper vehicular longitudinal single-lane motions. With the rapid and continuous development of sensing and communication technologies, connected automated vehicles (CAVs) equipped with on-board sensors and wireless communication devices provides unprecedented opportunities to drastically improve traffic safety [2,3,4]. For a CAV, cooperative adaptive cruise control (CACC) is a predominant approach to deal with inter-vehicle longitudinal driving behaviors by applying detected parameters from the preceding vehicle and real-time information received via vehicle-to-vehicle (V2V) communication. CACC can lose cooperation and degrade to ACC, owing to the incomplete information caused by both receivers (such as communication failure during transmission) or senders (such as a human driven vehicles (HDVs) without sending functions) [5,6]. Communication degradation can affect a vehicle’s car-following behaviors and safety

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