Truck Platooning Research Articles

Controller Design for Optimizing Fuel Consumption of Truck Platoon on Hilly Roads

Platoons consisting of automated and connected vehicles show great potential in reducing fuel or energy consumption. However, the fuel consumption optimization problem for truck platoons traveling on hilly roads has not been investigated thoroughly. To address that problem, a hierarchical control framework is proposed in this paper as follows: (1) The supervising layer is responsible for generating the fuel-oriented optimal speed profile based on the terrain information; (2) The distributed layer consists of an LQR feedback controller, a DMPC feedforward controller and a tube integration method to integrate the two controllers; it receives the optimal speed profile from the supervising layer and yields the control input to the individual vehicle. In this paper, a novel optimal speed profile generation method is proposed, a novel integration of tube method is applied, and the stability performance is analyzed rigorously. Simulations based on a real hilly road are conducted, and the performance of the proposed controller is evaluated regarding the platoon stability, fuel consumption and computation efficiency. The results of the simulation show that the controller is capable of maintaining the string stability of the truck platoon and reducing fuel consumed on hilly roads while improving computation efficiency.

Aggregated Zero-Knowledge Proof and Blockchain-Empowered Authentication for Autonomous Truck Platooning

Platooning technologies enable trucks to drive cooperatively and automatically, providing benefits including less fuel consumption, greater road capacity, and safety. To establish trust during dynamic platooning formation, ensure vehicular data integrity, and guard platoons against potential attackers in mixed fleet environments, verifying any given vehicle’s identity information before granting it access to join a platoon is pivotal. Besides, due to privacy concerns, truck owners may be reluctant to disclose private vehicular information, which can reveal their business data to untrusted third parties. To address these issues, this is the first study to propose an aggregated zero-knowledge proof and blockchain-empowered system for privacy-preserving identity verification in truck platooning. We provide the correctness proof and the security analysis of our proposed authentication scheme, highlighting its increased security and fast performance. The platooning formation procedure is re-designed to seamlessly incorporate the proposed authentication scheme, including the 1st catch-up and cooperative driving steps. The blockchain performs the role of verifier within the authentication scheme and stores platooning records on its digital ledger to guarantee data immutability and integrity. In addition, the proposed programmable access control policies enable truck companies to define who is allowed to access their platoon records. We implement the proposed system and perform extensive experiments on the Hyperledger platform. The results show that the blockchain can provide low latency and high throughput, the aggregated approach can offer a constant verification time of 500 milliseconds regardless of the number of proofs, and the platooning formation only takes seconds under different strategies. The experimental results demonstrate the feasibility of our design for use in real-world truck platooning.

Evaluation of the Risk of Truck Platoon under Crosswind regarding the Lateral Displacement on Horizontal Curves

Driving safety on horizontal curves under crosswinds has always been a concern for researchers. However, previous studies focused on single vehicles, especially single trucks. The safety of the truck platoon under crosswinds on horizontal curves was rarely examined. To fill such a gap, this study establishes a model for evaluating the safety of truck platoons under crosswinds on curves. After obtaining aerodynamic coefficients, a cosimulation model of a three-truck platoon is established with Trucksim and Simulink. The influencing factors on the lateral displacement of the truck platoon were analyzed, and the safety of the truck platoon under crosswinds on horizontal curves was then quantified with the response surface method. The results show that the fully autonomous platoon is safer than the human-led platoon. In a three-vehicle platoon, the smaller the lateral displacement of the truck, the further back the truck, and the lateral displacement of the vehicle increases with the loading of the truck. The safety index is computed to quantify the reliability of the truck platoon. The results indicate that the safety index increases with a decrease in the mean wind velocity and mean vehicle velocity and increases with an increase in the mean radius. When crosswinds come from the inside of the curve, the safety index increases with an increase in the mean superelevation; for crosswinds from the outside of the curve, the safety index rises with the drop of superelevation. The proposed approach can quantify the safety level of truck platoons under crosswinds on horizontal curves, and the results provide guidance to support the decision-making of transportation management agencies.

Truck platooning reshapes greenhouse gas emissions of the integrated vehicle-road infrastructure system

Reducing greenhouse gas emissions has turned into a pillar of climate change mitigation. Truck platooning is proposed as a strategy to lower emissions from vehicles on roads. However, the potential interactive impacts of this technology on road infrastructure emissions remain unclear. Here, we evaluate the decarbonization effects of truck platooning on the integrated vehicle-road system at a large-scale road network level, spanning 1457 road sections across North America. We show that truck platooning decreases emissions induced by truck operations, but it degrades faster the durability of road infrastructure and leads to a 27.9% rise in road emissions due to more frequent maintenance work. Overall, truck platooning results in a 5.1% emission reduction of the integrated vehicle-road system. In contrast to the benefits of emission reduction, truck platooning leads to additional financial burdens on car users and transportation agencies, calling for the consideration of tradeoffs between emissions and costs and between agencies and users. Our research provides insights into the potential applications of truck platooning to mitigate climate change.

Operational and Infrastructure Readiness for Semi-Automated Truck Platoons on Rural Roads

On highways, truck platooning may reduce fuel consumption, improve road safety and streamline trucking operations. However, most roads worldwide are two-way, two-lane rural roads, i.e., conditions for which truck platooning should be tested to explore the extent of those advantages. This paper reports findings from a field study undertaken in Northern Norway, testing a platoon of three semi-automated trucks on rural roads with tunnels, mountain passes and adverse geometries. Fleet management and distance data, videos, interviews and conversations between participants were used to assess whether platooning was feasible on such roads. The platooning system was used without interventions through most road conditions, and worked well on flat and wide roads with 90 km/h speed limits. However, it struggled in sharp horizontal curves, where the following trucks would speed up before regaining connection to their preceding truck and then brake abruptly to regain the prescribed distance. Moreover, steep uphills were problematic due to inconsistent gear shifting between the trucks. Seemingly, no fuel savings were achieved, due to excessive following distances and suboptimal speed profiles on crest curves. To obtain further insights into the benefits of truck platooning on rural roads, we suggest redoing the field study with V2V-communication, allowing for shorter following distances, and also performing a manual-driven baseline first.

Assessment of the Effect of Different Loading Combinations Due to Truck Platooning and Autonomous Vehicles on the Performance of Asphalt Pavement

Autonomous vehicles and truck platooning have become the future in the transportation field. This new strategy has many benefits because it lowers fuel consumption and CO2 emissions, improves safety, optimizes transport by using roads more effectively, and reduces traffic congestion. In this research, the effect of the controlled positioning of autonomous and non-autonomous truck loadings on the long-term performance of pavement was estimated using different variables such as climate, uniform wandering values of distance between trucks, and percentage of autonomous trucks by using MEPDG/AASHTOWare Pavement ME Design software. This was achieved by first computing the strain and stress of the different loading combinations, resulting in the computation of the failures in the pavement infrastructure and the pavement thickness needed to support each combination. The second part of the research consisted of designing a platoon strategy that was developed for a series of autonomous and connected trucks such that the lateral position of the trucks and the spacing between them could be explicitly optimized to minimize flexible pavement damage. The findings revealed that a small percentage of autonomous trucks can be beneficial to pavement life and that truck platooning following a well-studied skeleton can open a whole new world of pavement design. This can be revolutionary in changing roads around the world to improve traffic and infrastructure.

Platoon Optimization Based on Truck Pairs

Truck platooning technology allows trucks to drive at short headways to save fuel and associated emissions. However, fuel savings from platooning are relatively small, so forming platoons should be convenient and associated with minimum detours and delays. In this paper, we focus on developing optimization technology to form truck platoons. We formulate a mathematical program for the platoon routing problem with time windows (PRP-TW) based on a time–space network. We provide polynomial-time algorithms to solve special cases of PRP-TW with two-truck platoons. Based on these special cases, we build several fast heuristics. An extensive set of numerical experiments shows that our heuristics perform well. Moreover, we show that simple two-truck platoons already capture most of the potential savings of platooning. History: Accepted by Pascal van Hentenryck, Area Editor for Computational Modeling: Methods and Analysis. Funding: This work was supported by the Netherlands Organization for Scientific Research (NWO) as part of the Spatial and Transport Impacts of Automated Driving [Grant 438-15-161] project. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2020.0302 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2020.0302 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Code and Data Repository for Platoon Optimization Based on Truck Pairs

Truck platooning technology allows trucks to drive at short headways to save fuel and associated emissions. However, fuel savings from platooning are relatively small so forming platoons should be convenient and associated with minimum detours and delays. In this paper, we focus on developing optimization technology to form truck platoons. We formulate a mathematical program for the platoon routing problem with time windows (PRP-TW) based on a time-space network. We provide polynomial time algorithms to solve special cases of PRP-TW with two-truck platoons. Based on these special cases, we build several fast heuristics. An extensive set of numerical experiments show that our heuristics perform well. Moreover, we show that simple two-truck platoons already capture most of the potential savings of platooning. This repository contains the data and results.

EU ENSEMBLE Project: Specification of an Interoperable Solution for a Support Function for Platooning

Truck platooning is the linking of two or more trucks in convoy, using connectivity technology and automated driving support systems. For the wide deployment of platooning, multi-brand, i.e. interoperable, solutions are paramount. To achieve this, the European ENSEMBLE (ENabling SafE Multi-Brand pLatooning for Europe) project has been conducted. In this project, amongst others, all European truck manufacturers worked together to define harmonized multi-brand specifications for two different levels of platooning: a Platooning Support Function (PSF) and a Platooning Autonomous Function (PAF). The PSF aims towards fast deployment, whereas the PAF gives an outlook on the future of platooning when more advanced automated driving technologies become available. This paper describes the definition and specifications for the first level of platooning, the Platooning Support Function. It comprises a set of use cases, specifications and requirements for the common functionality. Part of the specifications is an interaction protocol, which also encompasses a security framework. To verify the definitions and to expedite standardization, all truck manufacturers implemented the PSF and this was tested in several multi-brand settings to conclude that the specifications result in technically feasible solutions.

An exact algorithm for the multi-trip container drayage problem with truck platooning

Container Drayage Problem (CDP) refers to the optimization problem of routing and scheduling a set of container trucks around a container terminal. Conventionally, a driver is stuck to one container truck and allowed to perform multiple trips (multi-trip) to the terminal within their working time. The recent development of automation technologies enables semi-autonomous trucks to follow the leading human-driven truck as a platoon on the road; therefore, truck platooning can save human labor and reduce the fuel cost of following trucks through aerodynamic drag reduction. In this paper, we study a multi-trip container drayage problem with truck platooning (MT-CDP-TP), where multi-trip, truck platooning, and fuel cost reduction are simultaneously considered in a CDP. Despite the operational benefits brought by the MT-CDP-TP, the problem is challenging to solve due to its NP-hardness when formulated as a multi-trip pickup and delivery problem with load-dependent cost. We propose a Branch-and-Price-and-Cut (BPC) algorithm, with a route-based set partitioning model and tight linear relaxations, to yield the exact solutions. Valid inequalities are generated based on a graph structure, where each node represents a feasible route, and each arc stands for the conflict between two routes. Moreover, we design a tailored pulse propagation algorithm with novel pruning procedures based on the dual information from the master problem and valid inequalities to solve the pricing problem efficiently. Extensive numerical experiments are conducted for performance validation, and the computational results show that the proposed exact algorithm can solve instances with up to 100 task nodes (i.e., 50 customers) and facilitate reducing the labor cost and fuel consumption by a wide margin in container drayage operations.

Using Low-Cost Radar Sensors and Action Cameras to Measure Inter-Vehicle Distances in Real-World Truck Platooning

Many modern vehicles collect inter-vehicle distance data from radar sensors as input to driver assistance systems. However, vehicle manufacturers often use proprietary algorithms to conceal the collected data, making them inaccessible to external individuals, such as researchers. Aftermarket sensors may circumvent this issue. This study investigated the use of low-cost radar sensors to determine inter-vehicle distances during real-world semi-automated truck platooning on two-way, two-lane rural roads. Radar data from the two follower trucks in a three-truck platoon were collected, synchronized and filtered. The sensors measured distance, relative velocity and signal-to-noise ratio. Dashboard camera footage was collected, coded and synchronized to the radar data, providing context about the driving situation, such as oncoming trucks, roundabouts and tunnels. The sensors had different configuration parameters, suggested by the supplier, to avoid signal interference. With parameters as chosen, sensor ranges, inferred from maximum distance measurements, were approximately 74 and 71 m. These values were almost on par with theoretical calculations. The sensors captured the preceding truck for 83–85% of the time where they had the preceding truck within range, and 95–96% of the time in tunnels. While roundabouts are problematic, the sensors are feasible for collecting inter-vehicle distance data during truck platooning.

Cost allocation of cooperative autonomous truck platooning: Efficiency and stability analysis

This study investigates the cost allocation of cooperative autonomous truck (AT) platooning among carriers. Motivated by the fuel consumption saving from truck platooning, carriers can collaborate for creating more platooning opportunities in an alliance. The formation of carrier alliance is associated with two key conditions: (i) efficiency: the total cost is allocated to each carrier without a budget deficit, and (ii) stability: all carriers find it beneficial to stay in the grand alliance and do not form sub-alliance. Since an alliance satisfying these two conditions may not always exist, we aim to find a cost allocation such that the violation of efficiency and stability conditions is minimized. We first propose a cooperative AT platooning problem for a trade-off between fuel consumption savings and schedule deviation cost. Then, we model the problem as a cooperative AT platooning game and formulate cost allocation models. The exact row-generation solution method is developed to computationally calculate the cost allocation. Extensive numerical experiments demonstrate the properties of proposed models and the effectiveness of solution methods. The results show that the developed row-generation solution method can solve the instances of up to 100 requests and 20 carriers within a two-hour time limit, and the schedule deviation coefficients significantly impact the performance of optimal cost allocation.

Platoon Cooperation Across Carriers: From System Architecture to Coordination

Truck platooning is a well-studied technology that has the potential to reduce both the environmental impact and operational costs of trucks. The technology has matured over the last 20 years, and the commercial rollout of platooning is approaching. Cooperation across carriers is essential for the viability of platooning; otherwise, many platooning opportunities are lost. We first present a cross-carrier platooning system architecture in which many carriers cooperate in forming platoons through a platoon-hailing service. Then, we present a cross-carrier platoon coordination approach in which each carrier optimizes its platooning plans according to the predicted plans of other carriers. A profit-sharing mechanism to even out the platooning profit in each platoon is embedded in the platoon coordination approach. Finally, a simulation study over the Swedish road network is performed to evaluate the potential of platooning under realistic conditions. The simulation study shows that the energy consumption of trucks in Sweden can be reduced by 5.4% due to platooning and that cooperation across carriers is essential to achieve significant platooning benefits.

Longitudinal Multiple Presence of Trucks on Continuous Bridge Spans

In designing and evaluating roadway bridges in the surface transportation infrastructure, truck load is a major variable to account for. For daily operation, truck load also is a primary factor. Trucks on continuous spans were studied, with a focus on their behavior of longitudinal multiple presence in the same lane(s). Current US specifications include provisions for how to account for such multiple presence, and these provisions have evolved over the years. However, the basis for the provisions and their changes have not been well documented. An unprecedentedly large number of high-quality in-motion truck weight data were gathered in eight states across the US and used to investigate trucks’ longitudinal multiple presence behavior. A new approach to analyzing each platoon of trucks recorded in traffic was developed for this purpose. Each random platoon’s total load effect was identified, covering every truck in the platoon, to obtain reliable statistics of the maximum load effects. Results show that longitudinal multiple presence increases with truck volume and span length. Relevant current AASHTO provisions therefore were found to be conservative or overconservative. Accordingly, it was concluded that lower longitudinal multiple presence factor values for evaluation and design are more justifiable. These results may be used by bridge owners and engineers for realistic load rating and design, for more-uniform structural safety and cost effectiveness. This is consistent with the top two goals of the AASHTO Committee on Bridges and Structures’ strategic plan for extending bridge service life and assessing bridge condition.

Truck platoon analysis for autonomous trucks

Selection of optimum platoon pattern based on types of trucks inside the platoon, the number of trucks in the platoon, headway distance, interplatoon distance as well as the use of different lateral wander modes for autonomous trucks has been analyzed. The objective of this research is to study the impacts of axle configurations, truck grouping, headway distance and lateral wander options on the performance of truck platoons. Four different headway distances from 2 to 5 m are compared. The first platoon PT-1 only consists of semi trailers, the second platoon PT-2 only consists of rigid body trucks and the third platoon PT-3 consists of equally distributed random traffic mix. Analysis has been conducted using the dload subroutine for projecting zero wander and uniform wander movements for each truck in the platoon on a three layered pavement crossection at vehicle speeds of 90 km/h for a total of 15 years of pavement lifetime consisting of 1.4 million equivalent single axle loads in finite element software ABAQUS. Results show that PT-3 platoon yields the minimum accumulation of damaging strains when compared against other platoon types. A headway distance of 5 m is suggested when using a zero wander mode and 3 m when using a uniform wander mode. In case of zero wander mode, fatigue life of the pavement decreases by 1.2 years and the use of uniform wander mode delays the rutting by 1.6 years, thereby increasing lifetime of the pavement.

Truck platooning impact on existing Bridges’ load ratings

Implementation of Autonomous and Connected Trucks (ACTs) will have major impacts on mobility, safety, and infrastructure service life. Although truck platooning will increase fuel efficiency and improve transportation services, the platooning configuration is expected to accelerate damage to the existing infrastructure. This damage, if accumulates, will cost the country billions of dollars to fix and will affect the mobility of people and goods. This study aimed to develop a well-defined framework for assessing and a data-driven solution for addressing the impact of truck platoons on existing bridges in order to be prepared for future implementation of ACT and to preserve the current bridge inventory. An extensive parametric study of 29,600 computer simulations was conducted to address the effect of a wide range of platooning configurations on the load rating of existing bridges using three different load rating methodologies (ASR, LFR, LRFR). The findings were then used to develop engineering guidelines that engineers can use to find the optimum parameters for any bridge case. The study also provided a potential implementation methodology that bridge owners can use to evaluate existing bridges for regular truck platooning applications. The number of trucks and their spacing have been shown to be critical parameters influencing bridge load ratings. Bridges rated using ASR or LFR have experienced a reduction in their rating factors for bending moments and shear forces. On the other hand, LRFR bridges have shown better results, especially for short spans and widely spaced trucks. Finally, the developed design charts were used to find the change in the load rating of a bridge case study and compared to AASHTO load ratings’ equations; the results obtained were identical.

Research on Truck Lane Management Strategies for Platooning Speed Optimization and Control on Multi-Lane Highways

Automated truck platooning has become an increasingly popular research subject, and its applicability to highways is considered one of the earliest possible landing scenarios for automated driving. However, there is a lack of research regarding the combination of truck platooning technology and truck lane management strategy on multilane highways in the environment of a cooperative vehicle–infrastructure system (CVIS). For highway weaving sections under the CVIS environment, this paper proposes a truck platooning optimal speed control model based on multi-objective optimization. Through a combination of model predictive control and the cell transmission model, this approach considers the bottleneck cell traffic flow, overall vehicle travel time, and truck platooning fuel consumption as objectives. By conducting experiments on a mixed traffic flow simulation platform, the multi-lane management strategies and optimal speed control effect were evaluated through different scenarios. This study also determined the appropriate proportion of truck platooning for an exclusive lane and to increase truck lanes, thus providing effective lane management decision support for highway managers.

Optimizing truck platooning transportation planning: an application to forestry products supply chains

The Fourth Industrial Revolution offers new opportunities for improving the efficiency and agility of supply chain operations such as transportation. This study explores the impact of integrating truck platooning technology in forestry products supply networks. Companies need to know how and where to use truck platooning in transportation networks to get optimum benefits from truck platooning in supply chains. To this end, a Mixed Integer Linear Programming model was developed. Decisions to be made include the selection of the potential terminal locations, the number of ordinary and platooning trucks needed in the transportation network, the origin and destination of products, and their flow in direct and backhaul routes. The objective is to minimize the overall transportation cost, including terminal location costs, fixed costs for ordinary and platoon trucks, fuel, and driver costs. A total of 27 randomly generated instances are used to test the optimization model. We considered several scenarios to analyze different combinations of using or not backhauling, having or not truck platoons in the network, and allowing or not the truck platoons to visit the forest areas. The results show that the potential savings of combining the two types of trucks are in the range of 1%–12% in the scenarios in which truck platooning transportation is allowed only between terminal and mill nodes. This savings could reach more than 20% when the truck platoons are allowed to visit forest areas, depending on how many forest areas are visited. The number of drivers can be reduced by 3% to more than 30%. In addition, using truck platooning and backhauling together could reduce fuel consumption by 15.6% on average.

Truck Platooning

Truck platooning is a technology that allows trucks to drive in a convoy one behind the other, communicating with one another and adapting themselves to the convoy. There are various configurations with differing degrees of autonomy but, in most cases, there is still a driver on board, which distinguishes the concept from fully autonomous vehicles. In this article, Dominik Juling examines the advantages, hurdles and limits of military truck platooning in Europe, and provides examples of projects and technologies that are already operational. The benefits include fewer drivers, increased safety for the crew and reduced resource consumption.◼

Backstepping method based controller design for third-order truck platoon robust to dynamic uncertainty and external disturbance

Backstepping method based controller design for third-order truck platoon robust to dynamic uncertainty and external disturbance