Abstract
Communication delays within connected and autonomous vehicles (CAVs) pose significant risks. It is imperative to address these issues to ensure the safe and effective operation of CAVs. However, the exploration of communication delays on CAV operations and their energy use remains sparse in the literature. To fill the research gap, this study leverages the facilities at America Center of Mobility (ACM) Smart City Test Center to implement and evaluate a CAV merging control algorithm through vehicle-in-the-loop testing. This study aims at achieving three main objectives: (1) develop and implement a CAV merging control strategy in the experimental test bed through vehicle-in-the-loop testing, (2) propose analytical models to quantify the impacts of communication delay on the variability of CAV speed and energy consumption based on field experiment data, and (3) create a predictive model for energy usage considering various CAV attributes and dynamics, e.g., speed, acceleration, yaw rate, and communication delays. To our knowledge, this is one of the first attempts at evaluating the impacts of communication delays on CAV merging operational control with field data, making critical advancement in the field. The results suggest that communication delay has a more substantial effect on energy consumption under high-speed volatility compared to low-speed volatility. Among all factors examined, acceleration is the dominant characteristic that influences energy usage. It also revealed that even minor improvements in communication delay can yield tangible improvements in energy efficiency. The results provide guidance on CAV field experiments and the influence of communication delays on CAV operation and energy consumption.
| Original language | English |
|---|---|
| Article number | 100136 |
| Journal | Communications in Transportation Research |
| Volume | 4 |
| DOIs | |
| State | Published - Dec 2024 |
Funding
Research on merging control for CAVs is an active area of study. The strategy will depend on many factors, e.g., the level of autonomy of the vehicles, the availability of communication, the traffic management systems, level of penetration of CAVs, and the regulations and standards in place. There are various CAV merging control strategies. Zhu et al. (2022) provided a comprehensive review of merging control strategies for merging CAVs at freeway on-ramps, including ram petering, variable speed limits, cooperative merging, platooning, and reservation-based merging. The paper also highlighted the importance of Vehicle-to-Infrastructure (V2I) communication in supporting effective merging. Li et al. (2023) proposed a communication-based soft actor-critic algorithm to enhance the cooperation of merging CAVs in heavy traffic. The algorithm included a local module and a global module. The local module aimed to control individual vehicle while global module facilitated communication and cooperation among vehicles. Xiong et al. (2022) proposed a merging control method that considered the uncertainty of human driving when a CAV merged to a lane with human-driven vehicle. The method involved two stages. First, the CAV predicted the trajectories of the surrounding vehicles in the human-driven lane and chooses an optimal gap to merge. Second, the CAV adjusted its speed and position to match the optimal gap to minimize the risk of collision. Di et al. (2023) proposed a predictive control strategy integrating variable speed limits, ramp metering, and lane changing to optimize traffic flow and reduce traffic congestion. The system considered the differences on driving behaviors between cAVs and connected vehicles (CVs) to optimize overall traffic flow. Xiao and Cassandras (2021) developed a decentralized merging control strategy for CAVs that ensures safe merging maneuvers. The proposed strategy utilized a model predictive control (MPC) approach that considered the kinematic constraints of CAVs as well as the minimum distance between CAVs to avoid collision. Tang et al. (2022) developed a hierarchical cooperative merging control model for CAVs that enabled flexible merging positions. The proposed model contained two layers. The upper layer coordinated merging process with a system-level multi-objective optimization algorithm, while the lower layers controlled individual CAV to implement merging command using a model predictive control approach.Unlike simulation platforms, real-world testbed for CAV operation has many advantages. These test sites provide an environment to evaluate the performance and effectiveness of different control strategies for CAVs. There are several testing facilities around the world that are famous for the research in CAVs. For example, Mcity is a 32-acre testing facility in Michigan, USA that simulates real-world urban and suburban driving scenarios (Mcity, 2023). Its test track includes a variety of road types, e.g., roundabouts, tunnels, and pedestrian crossings, to create a diverse testing environment. Mcity also has a range of sensors and equipment installed to collect data on CAV performance, e.g., cameras, LiDAR, and GPS system. The ACM Smart City Test Center is a testing and product development facility for CAV, also located in Michigan, USA (American Center of Mobility, 2024). The facility includes a range of communication infrastructure to support the testing of CAVS. Singapore Autonomous Vehicle Initiative (SAVI) is a research program aiming at developing fully autonomous vehicle capable of navigating complex urban environments (Quek, 2017). The initiative supports the development and deployment of autonomous vehicles in Singapore, including establishment of regulatory framework and construction of a test facility for CAVs called CENRAN.This funding for this research was provided by US Department of Energy (DOE) under the project EEMS082:Validation of Connected and Automated Mobility System. The authors are thankful for the support. This funding for this research was provided by US Department of Energy (DOE) under the project EEMS082:Validation of Connected and Automated Mobility System. The authors are thankful for the support.
Keywords
- Communication delay
- Connected and autonomous vehicles (CAVs)
- Field experimental data
- Merging control
- Vehicle-in-the-loop testing