External communication displays for connected truck platoons in mixed traffic : a federated simulator study

Truck platooning is anticipated to be the first widespread deployment of connected or automated vehicles (CAV). In addition to familiarizing the public with the function of CAV, a truck platoon has proven benefits to fuel consumption by minimizing drag. These are the primary motivations that have caused companies to develop and deploy these systems, but there are still obstacles opposing their implementation. One obstacle is the issue of communication between CAVs and the surrounding traffic. For example, research has shown that communication between CAV and pedestrians and cyclists is facilitated by using external status displays on the CAVs. In order to investigate the communication between truck platoons and surrounding traffic, a similar model is proposed in this study. The scenario examined in this study involves trucks forming a truck platoon. Two different external displays in addition to a control display were evaluated for how surrounding traffic behaves while the trucks form their platoon. The three displays are the control (no signal), the word "PLATOON," and a graphic of two trucks with a link. Each of the displays were tested using a federated truck simulator and passenger vehicle simulator. The approaching truck was driven by the same human driver up until the completion of platooning while the passenger vehicle was driven by the research participants. The simulation scenario involved a passenger vehicle following a semi-truck while an approaching truck comes up from behind the passenger vehicle to form the platoon. The actions taken by the passenger vehicle to clear the way for the approaching truck were observed and recorded. After the participants were exposed to the signs once, they were provided with an explanation of truck platoons and were able to ask questions before experiencing three displays scenarios again. Overall, the primary performance result was that the text display after being provided with information on truck platoons significantly changed the behavior of the passenger vehicle. Furthermore, as in the AV-Pedestrian studies, participants indicated that the external displays were useful. In conclusion, though the behavior was not drastically affected, the results indicate that the displays provide the passenger vehicle drivers with important information that they want to have and that drivers tend to move out of the way when they learn that a truck platoon is forming around them.by Michael SchoelzIncludes bibliographical reference

Reduced Fuel Emissions through Connected Vehicles and Truck Platooning

Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication enable the sharing, in real time, of vehicular locations and speeds with other vehicles, traffic signals, and traffic control centers. This shared information can help traffic to better traverse intersections, road segments, and congested neighborhoods, thereby reducing travel times, increasing driver safety, generating data for traffic planning, and reducing vehicular pollution. This study, which focuses on vehicular pollution, used an analysis of data from NREL, BTS, and the EPA to determine that the widespread use of V2V-based truck platooning—the convoying of trucks in close proximity to one another so as to reduce air drag across the convoy—could eliminate 37.9 million metric tons of CO2 emissions between 2022 and 2026

Quantum Artificial Intelligence Supported Autonomous Truck Platooning

Truck platooning can potentially increase the operational efficiency of freight movement on U.S. corridors, improving commercial productivity and economic vibrancy. Predicting each leader vehicle trajectory in the autonomous truck platoon using Artificial Intelligence (AI) can enhance platoon efficiency and safety. Reliance on classical AI may not be efficient for this purpose as it will increase the computational burden for each truck in the platoon. However, Quantum Artificial Intelligence (AI) can be used in this scenario to enhance learning efficiency, learning capacity, and run-time improvements. This study developed and evaluated a Long Short-Term Memory Networks (LSTM) model and a hybrid quantum-classical LSTM (QLSTM) for predicting the trajectory of each leader vehicle of an autonomous truck platoon. Both the LSTM and QLSTM provided comparable results. However, Quantum-AI is more efficient in real-time management for an automated truck platoon as it requires less computational burden. The QLSTM training required less data compared to LSTM. Moreover, QLSTM also used fewer parameters compared to classical LSTM. This study also evaluated an autonomous truck platoon\u27s operational efficacy and string stability with the prediction of trajectory from both classical LSTM and QLSTM using the Intelligent Driver Model (IDM). The platoon operating with LSTM and QLSTM trajectory prediction showed comparable operational efficiency. Moreover, the platoon operating with QLSTM trajectory prediction provided better string stability compared to LSTM

Energy-Efficient and Semi-automated Truck Platooning

This open access book presents research and evaluation results of the Austrian flagship project “Connecting Austria,” illustrating the wide range of research needs and questions that arise when semi-automated truck platooning is deployed in Austria. The work presented is introduced in the context of work in similar research areas around the world. This interdisciplinary research effort considers aspects of engineering, road-vehicle and infrastructure technologies, traffic management and optimization, traffic safety, and psychology, as well as potential economic effects. The book’s broad perspective means that readers interested in current and state-of-the-art methods and techniques for the realization of semi-automated driving and with either an engineering background or with a less technical background gain a comprehensive picture of this important subject. The contributors address many questions such as: Which maneuvers does a platoon typically have to carry out, and how? How can platoons be integrated seamlessly in the traffic flow without becoming an obstacle to individual road users? What trade-offs between system information (sensors, communication effort, etc.) and efficiency are realistic? How can intersections be passed by a platoon in an intelligent fashion? Consideration of diverse disciplines and highlighting their meaning for semi-automated truck platooning, together with the highlighting of necessary research and evaluation patterns to address such a broad task scientifically, makes Energy-Efficient and Semi-automated Truck Platooning a unique contribution with methods that can be extended and adapted beyond the geographical area of the research reported

The operational and safety effects of heavy duty vehicles platooning

Abstract Although researchers have studied the effects of platooning, most of the work done so far has focused on fuel consumption. There are a few studies that have targeted the impact of platooning on the highway operations and safety. This thesis focuses on the impact of heavy-duty vehicles (HDVs) platooning on highway characteristics. Specifically, this study aims at evaluating the effects of platooning of HDVs on capacity, safety, and CO2 emissions. This study is based on a hypothetical model that was created using the VISSIM software. VISSIM is a powerful simulation software designed to mimic the field traffic flow conditions. For model validity, the model outputs were compared with recommended values from guidelines such as the Highway Capacity Manual (HCM) (Transportation Research Board, 2016). VISSIM was used to obtain the simulation results regarding capacity. However, in addition to VISSIM, two other software packages were used to obtain outputs that cannot be assessed in VISSIM. MOVES and SSAM are two simulation software packages that were used for emission and safety metrics, respectively. Both software packages depended on input from VISSIM for analysis. It was found that with the presence of HDVs in the model, the capacity, the emission of CO2, and the safety of the roadway would improve positively. A capacity of 4200 PCE/h/ln could be achieved when there are enough HDVs in platoons. Furthermore, more than 3% of the traffic flow emission of CO2 reduction is possible when 100% of the HDVs used in the model are in platoons. In addition to that, a reduction of more than 75% of the total number of conflicts might be obtained. Furthermore, with the analysis of the full factorial method and the Design of Experiment (DOE) conducted by using Excel and Minitab respectively, it was possible to investigate the impact of the platoons’ factors on the highway parameters. Most of these factors affect the parameters significantly. However, the change in the desired speed was found to insignificantly affect the highway parameters, due to the high penetration rate. Keywords: VISSIM, MOVES, SSAM, COM-interface, HDVs, Platooning, Number of Conflict