Browse Topic: Platooning
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
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
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
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
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
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
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
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
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
This article explores a wide variety of energy-saving transportation concepts that exploit the unique characteristics of electric vehicles. The confluence of three emerging concepts in transportation, namely electric vehicles, autonomous navigation, and networked vehicles, is shown to present unprecedented opportunities for the optimization of transportation efficiency, especially for passenger and commercial road vehicles. The article addresses both urban and highway driving situations and some of the associated optimization problems. After the introduction of a suitable powertrain model, several optimization problems of practical importance are formulated. It is shown that if the only term in the cost function is transportation energy, and all other conditions are formulated as constraints, substantial energy cost reductions are possible. In particular, the case of energy-optimal speed trajectories is highlighted, and it is shown that for urban driving, energy savings can exceed 50
Platooning is a key research area where increased focus and interest is shown in order to maximize the transport efficiency of road vehicles. Although the key benefits are projected as increased fuel efficiency especially when it comes to commercial vehicles, allied benefits such as convoy pack efficiency, traffic throughput rate, increased life cycle of components and a source of monetary benefit when using a subscription model are areas which need to be explored. Existing literature points to control strategies predominantly focused on longitudinal control and traffic management in bottlenecks. Estimating that the application of platooning concepts will penetrate across commercial vehicle segments as well as passenger vehicles leads to the logical need for a modular control approach for aspects such as realizing influence of convoy speed on selection of vehicle position, relative position of vehicles, convoy dynamics based on weak link approach and sharing monetary benefits based on
The work described in this paper is a continuation of an investigation into the effects of systematic changes in upper-body geometry on the aerodynamic drag of passenger-car-like bluff-body models in close longitudinal proximity and operating in platoon formations. The original work, presented in SAE paper 2019-01-0659, showed measurements of the aerodynamic drag of individual models within three-model platoons and for which each model was tested in three different upper-body configurations This provided a data-set of 27 platoon configurations to compare with the three baseline conditions of the isolated models. The work contrasted with other published platooning research in which the spacing, between homogeneous models in the platoons, was the only variable to be considered. In this publication the results of further wind tunnel tests, using the same models as before but in two-model platoons, providing a further 9 test configurations are compared with the original data. In addition
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