Browse Topic: Platooning
A smart highway tunnels lighting system based on the technology of cloud platform and Internet of Things(IoTs) has been designed to address the common problems of high energy consumption and low level of intelligence in China's highway tunnel lighting system. The highway tunnel lighting system consists of four layers of architecture: platform management layer, local management layer, middle layer and terminal layer. The system collects real-time brightness, lamp brightness, traffic volume and other data outside the tunnel through various sensors deployed on site, and then uploads the collected data to the main controller through LoRa IoTs. The main controller combines the brightness calculation method of the lighting design rules to control the brightness of the tunnel lighting in real time, achieving real-time adjustment of the brightness of the tunnel LED lights and the brightness outside the tunnel, and realizing a safe and energy-saving lighting effect of "lights on when the car comes, lights on when the car goes, and lights follow the car". The experimental results show that the energy-saving rate of the system has reached about 70%, which has achieved good energy-saving and emission reduction effects, and has significant economic, social, and ecological benefits.
In the next years, the global hydrogen vehicle market is expected to grow at a very high rate. Consequently, it is necessary for scholars and professionals to study and test specific components in order to rise motor efficiency leveraging the new features of connectivity available in smart roads. In particular, our research is focused on the developement of an engine control module driven by evaluation of usage characteristics (e.g., driving style) and "connected-to-x" scenarios using the standard engine control approach. Moreover, the module proposed enables the implementation of "fast running" models to improve the response of vehicles and make the best possible use of H2-powered engine characteristics. That said, in this paper is proposed a new approach to implement the control module, using Support Vector Machine (SVM) as the machine learning algorithm to detect driving style, and consequently modify the parameters of the engine. We choose SVM because i) it is less prone to overfitting; and ii) SVM memory efficiency enables the design of a low-cost, compact size controller board. The first step of our research, described in this paper, is to test the algorithm proposed and verify its performance using the usual machine learning metrics. An open source dataset has been used for training and testing of our SVM-based algorithm and the promising results achieved are shown. As part of future work, this experimental control module will be installed on an H2-powered motor on test bench to assess its functionality and allow proper tuning.
Letter from the Guest Editors
In traffic scenarios, the spacing between vehicles plays a key role, as the actions of one vehicle can significantly impact others, particularly with regards to energy conservation. Accordingly, modern vehicles are equipped with inter-vehicle communication systems to maintain specific distances between vehicles. The aerodynamic forces experienced by both leading vehicles (leaders) and following vehicles (followers) are connected to the flow patterns in the wake region of the leaders. Therefore, improving our understanding of the turbulent characteristics associated with vehicles platooning is important. This paper investigates the effects of inter-vehicle distances on the flow structure of two vehicles: a small SUV as the leader and a larger light commercial van as the follower, using a Delayed Detached Eddy Simulation (DDES) CFD technique. The study focuses on three specific inter-vehicle distances: S = 0.28 L, 0.4L, and 0.5L, where S represents the spacing between the two vehicles and L is the length of the leader. Realistic flow conditions are simulated with an average velocity of 31.3 m/s. A comprehensive analysis is conducted by studying the influence of various yaw angles: 0°, -3° and -6°, each representing the vehicle’s alignment with the flow, and effects of 0.33m and 0.66m vehicles’ offsets. This study represents the correlation between the vehicle’s orientation and the aerodynamic forces. The findings indicate the unique flow characteristics at various inter-vehicle distances. These results are then compared to a scaled model tested in a wind tunnel at different inter-vehicle distances. The study demonstrates that changing the vehicle distance results in variations in the length of the recirculation region and flow characteristics behind the vehicles, subsequently impacting the drag and lift coefficients of the leader and the follower. In addition, within a specific range of vehicle distances, the two vehicles can benefit from platooning in terms of drag reduction and consequently less energy consumption. The study also investigates the drag coefficients of both the leader and follower at different yaw angles and vehicles’ offsets. The results highlight that drag coefficients increase at higher yaw angles. Furthermore, the paper shows the distributions of mean velocity, static pressure, turbulence characteristics and 3D vortical structures around the leader and the follower. These results provide valuable information of the complex flow behavior and improve our understanding of the aerodynamic forces around the vehicles during platooning. Such information helps the ongoing efforts to optimize vehicles’ energy consumption.
To facilitate the construction of a robust transport infrastructure, it is essential to implement a digital transformation of the current highway system. The concept of digital twins, which are virtual replicas of physical assets, offers a novel approach to enhancing the operational efficiency and predictive maintenance capabilities of highway networks. The present study begins with an exhaustive examination of the demand for the smart highway digital twin model, underscoring the necessity for a comprehensive framework that addresses the multifaceted aspects of digital transformation. The framework, as proposed, is composed of six integral components: spatiotemporal data acquisition and processing, multidimensional model development, model integration, application layer construction, model iteration, and model governance. Each element is critical in ensuring the fidelity and utility of the digital twin, which must accurately reflect the dynamic nature of highway systems. The methodology for constructing a smart highway digital twin model is explored through a systematic approach that encompasses three pivotal stages. The first stage involves the comprehensive perception of spatiotemporal data, the foundation for any digital twin. The second stage pertains to entity modeling, where the physical assets of the highway system are digitized, thus creating a virtual representation that can be manipulated and analyzed. The final stage is real-time state modeling, which enables the digital twin to simulate the current state of the highway system, thereby providing real-time feedback and predictive analytics. This work aims to contribute to the theoretical and technical discourse surrounding smart highway digital twins, offering insights that can inform the development and practical application of such models. By adhering to the proposed framework and methodology, workers in the transportation sector can leverage the potential of digital twins to enhance safety, efficiency, and sustainability within the highway infrastructure ecosystem.
The deployment of autonomous urban buses brings with it the hope of addressing concerns associated with safety and aging drivers. However, issues related autonomous vehicle (AV) positioning and interactions with road users pose challenges to realizing these benefits. This report covers unsettled issues and potential solutions related to the operation of autonomous urban buses, including the crucial need for all-weather localization capabilities to ensure reliable navigation in diverse environmental conditions. Additionally, minimizing the gap between AVs and platforms during designated parking requires precise localization. Next-gen Urban Buses: Autonomy and Connectivity addresses the challenge of predicting the intentions of pedestrians, vehicles, and obstacles for appropriate responses, the detection of traffic police gestures to ensure compliance with traffic signals, and the optimization of traffic performance through urban platooning—including the need for advanced communication and coordination technology to maintain stability and reliability in high-traffic scenarios. Click here to access the full SAE EDGETM Research Report portfolio.
This article offers an algorithmic solution for moving a homogeneous platoon of position-controlled vehicles on a curved path with varying speeds and in the presence of communication losses and delays. This article considers a trajectory-based platooning with the leader–following communication topology, where the lead vehicle communicates its reference position and orientation to each autonomous follower vehicle. A follower vehicle stores this communicated information for a specific period as a virtual trail of the lead vehicle starting from the lead vehicle’s initial position and orientation. An algorithm uses this trail to find the follower vehicle’s reference position and orientation on that trail, such that the follower vehicle maintains a constant distance from the lead vehicle. The proposed algorithm helps form a platoon where each vehicle can traverse a curve with varying speeds. In contrast, in the existing literature, most of the solutions for vehicle platooning on a curved path demonstrate constant speed motion. This article considers delayed information as lost. Therefore, the communication losses and delays are considered as packet drops. The algorithm uses estimates of the lost information of the lead vehicle’s trail to generate the reference path for the follower vehicles. Analysis of the algorithm shows a relationship between the platoon’s performance and the extent of packet drops. Simulation results agree with the relationship.
This research investigates platoon dispersion characteristics in mixed-traffic flow of autonomous and human-driven vehicles. It presents a cellular automata-based platoon dispersion model. The study’s key findings are as follows: platoon dispersion initially increases and then decreases with the rise in autonomous vehicle proportions. When the autonomous vehicle proportion is approaching 100%, platoon dispersion descends rapidly and is completely eliminated while the proportion is 100%. Compared to platoon consisting entirely of human-driven vehicles, the peak value of standard deviation of vehicle speed is 1.71 times and the travel time drops by 38.19% when the proportion is 1. Moreover, the lane-changing behavior enhances platoon speed, acceleration, and space utilization at micro- and macrolevels by optimizing space resource allocation within the platoon. The study employs a two-lane mixed-flow platoon dispersion model that assumes uniform vehicle characteristics and prioritizes maximizing travel efficiency for autonomous vehicles. These findings bear significant implications for transportation planning and management, providing valuable insights for policymakers, transportation engineers, and researchers.
Truck platooning is an emerging technology that exploits the drag reduction experienced by bluff bodies moving together in close longitudinal proximity. The drag-reduction phenomenon is produced via two mechanisms: wake-effect drag reduction from leading vehicles, whereby a following vehicle operates in a region of lower apparent wind speed, thus reducing its drag; and base-drag reduction from following vehicles, whereby the high-pressure field forward of a closely-following vehicle will increase the base pressure of a leading vehicle, thus reducing its drag. This paper presents a physics-guided empirical model for calculating the drag-reduction benefits from truck platooning. The model provides a general framework from which the drag reduction of any vehicle in a heterogeneous truck platoon can be calculated, based on its isolated-vehicle drag-coefficient performance and limited geometric considerations. The model is adapted from others that predict the influence of inter-vehicle distance for vehicle platoons, but extends the concept to account for cross winds and for lateral offsets between sequential vehicles, thus permitting its use for a range of modelling and simulation applications. Good agreement with experimental data sets from wind-tunnel and track tests is demonstrated in the paper.
Vehicular automation in the form of a connected and automated vehicle platoon is demanding as it aims to increase traffic flow and driver safety. Controlling a vehicle platoon on a curved path is challenging, and most solutions in the existing literature demonstrate platooning on a straight path or curved paths at constant speeds. This article proposes an algorithmic solution with leader-following (LF) communication topology and constant distance (CD) spacing for platooning homogeneous position-controlled vehicles (PCVs) on a curved path, with each vehicle capable of cornering at variable speeds. The lead vehicle communicates its reference position and orientation to all the follower vehicles. A follower vehicle stores this information as a virtual trail of the lead vehicle for a specific period. An algorithm uses this trail to find the follower vehicle’s reference path by solving an optimization problem. This algorithm is feasible and maintains a constant inter-vehicle distance. The PCVs can be holonomic or nonholonomic. For simulations, this article considers a holonomic four-wheel independent steering four-wheel independent drive (4WIS4WID) PCV for platooning. This vehicle has superior maneuverability and traction and can extend the applications of vehicle platoons from highways to paths with smaller radii of curvature. Simulation of a five-vehicle platoon suggests a satisfactory performance of the proposed approach. This article also presents an alternate curved platooning approach where the lead vehicle communicates its reference longitudinal and lateral velocities and yaw rate to a follower vehicle. The follower vehicle directly follows these communicated signals for platooning. This approach does not store the communicated signals and also cuts the cost of the position controller for the follower vehicles. Simulation results show that this alternative approach is applicable to constant-speed motion.
ABSTRACT To improve robustness of autonomous vehicles, deployments have evolved from a single intelligent system to a combination of several within a platoon. Platooning vehicles move together as a unit, communicating with each other to navigate the changing environment safely. While the technology is robust, there is a large dependence on data collection and communication. Issues with sensors or communication systems can cause significant problems for the system. There are several uncertainties that impact a system’s fidelity. Small errors in data accuracy can lead to system failure under certain circumstances. We define stale data as a perturbation within a system that causes it to repetitively rely on old data from external data sources (e.g. other cars in the platoon). This paper conducts a fault injection campaign to analyze the impact of stale data in a platooning model, where stale data occurs in the car’s communication and/or perception system. The fault injection campaign accounts for different occurrences of a communication error. Our analysis provides an understanding of the sensitivity of each model parameter in causing system failures (e.g. a crash between vehicles within the platooning model). By understanding which parameters are most influential to the fidelity of the model, we enable the ability to make platooning algorithms safer. Citation: A. St. Louis and J. C. Calhoun, “Exploring the Impact of Data Uncertainties in Autonomous Ground Vehicle Platooning,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 15-17, 2023.
Traditional live testing of autonomous ground vehicles can be augmented through use of digital twins of the test environment, the vehicle mobility models, and the vehicle sensors. These digital twins combined with the autonomous software under test allow testers to inject faults, weather, obstacles, find edge case scenarios, and collect information to understand the decision making of the autonomous software under test. With this new capability, autonomous ground vehicles can now be tested in four stages. The first stage is testing the autonomous software using digital twins. In this stage with the help of a High-Performance Computer thousands of scenarios can be run. Once issues are communicated and addressed, stage two, hardware in the loop testing can begin. Hardware in the loop uses simulators that already exist to test systems such as autonomous convoys with a virtual leader and a live follower. Stage three employs a live virtual constructive approach by using one vehicle to test a convoy on the live track. Stage four allows traditional live testing to occur but with the help of a hardware and software applique kit to prevent the autonomous software from doing anything unsafe on the test track.
In the context of global warming and energy shortage crisis, how to deal with vehicle speed planning and energy management strategies using intelligent connected information is one of the most significant ways to improve traffic efficiency and vehicle fuel economy. In this paper, a hierarchical model predictive control algorithm based on the connected environment is designed for the study of series hybrid electric vehicles (HEVs). The higher level and the lower level controller share information with each other and solve two different problems aiming at improving its fuel efficiency. V2X (Vehicle to Everything) information is used as an input for the high-level controller to establish a model predictive framework to plan the future speed and improve its stability of the whole vehicle platoon. The low-level MPC provides a real-time HEV energy management strategy. The result shows that our hierarchical algorithm can achieve the vehicle platoon follow-up control while maintaining the fuel economy.
Governmental regulations and customer demand for more energy-efficient vehicles are driving the development of new solutions in the automotive sector. One way of improving energy efficiency is by reducing the aerodynamic drag. A possible solution to achieve this is the concept of vehicles driving in close proximity, which is now becoming feasible considering the advances in vehicle automation and communication. This study focuses on the behavior of aerodynamic forces and flow effects in a two-truck platoon when more realistic road conditions, such as lateral offset and yaw, are present. The study is primarily numerical, but the results are validated against an experimental campaign conducted earlier by the authors. The main findings are that the drag of the leading truck is mostly governed by the base pressure of its trailer and that the truck sees only minor changes when a lateral offset is added, except at very short intervehicle distances. For larger yaw angles, the leading truck sees an increase in the effective yaw angle for the trailer, resulting in lower efficiency of the platoon. The behavior of the trailing truck is much more complex, with several different effects being observed when yaw and lateral offset are added. When yaw is added, the movement and increased intensity of the stagnation region adds to drag, while the change in effective yaw angle lowers the drag of the vehicle. This causes the benefit of platooning to vary with both yaw angle and vehicle distance. The addition of a lateral offset in the leeward direction can partially compensate for the negative effects of yaw on drag.
ABSTRACT Leader-follower autonomous vehicle systems have a vast range of applications which can increase efficiency, reliability, and safety by only requiring one manned-vehicle to lead a fleet of unmanned followers. The proper estimation and duplication of a manned-vehicle’s path is a critical component of the ongoing development of convoying systems. Auburn University’s GAVLAB has developed a UWB-ranging based leader-follower GNC system which does not require an external GPS reference or communication between the vehicles in the convoy. Experimental results have shown path-duplication accuracy between 1-5 meters for following distances of 10 to 50 meters. Citation: K. Thompson, B. Jones, S. Martin, and D. Bevly, “GPS-Independent Autonomous Vehicle Convoying with UWB Ranging and Vehicle Models,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 16-18, 2022.
There is “no business case” for platooning, or the electronic coupling of two or more trucks in close formation. That was the assessment of Daimler Trucks in 2019 when it decided to pause its years-long platooning development activities. The OEM determined that for U.S. long-distance applications, where conditions were expected to be ideal, the fuel savings were less than stellar and diminished further when the platoon got “disconnected” and trucks had to accelerate to reconnect. Instead, the company turned its full attention to developing highly automated (SAE Level 4) trucks. The fate of Peloton Technology, a company all-in on platooning but that ceased operations in 2021, is another indicator that perhaps platooning's promise has faded.
Items per page:
50
1 – 50 of 180