Search Results (42)

Platooning technology, which reduces fuel consumption by decreasing aerodynamic drag, is emerging as a key solution for enhancing road efficiency and environmental sustainability in logistics. Conventional vehicle-to-vehicle communication has limitations when forming platoons across multiple trucking companies. To overcome these limitations, a hub-based platooning system has been proposed, enabling coordinated vehicle platoons through hubs distributed along highways. This study develops a mathematical model to optimize platoon formation at hubs, considering the reality that uncertainty in vehicle arrival times can be resolved as vehicles approach the hub and use vehicle-to-hub communication. The model applies robust optimization techniques to consider worst-case vehicle arrival scenarios and examine how the range of data exchange points—where exact arrival times become known—affects platoon efficiency. Numerical experiments demonstrate that if the range of data exchange points is sufficiently wide, optimal efficiency can be achieved even under uncertainty. Sensitivity analysis also confirms that reducing uncertainty enhances energy savings efficiency. This study provides practical insights into forming vehicle platoons in uncertain environments, contributing to the economic and environmental benefits of the logistics industry. Future studies could extend the model to multiple hubs and consider stochastic disruptions, such as communication failures. Full article

Thanks to advancements in automated driving technology, autonomous trucks (ATs) can form platoons with minimal inter-vehicle distances on highways, significantly reducing air drag and fuel consumption for fleets. Given the dispersed distribution and small quantities of cargo, fleet operators should manage ATs to enable cargo consolidation during platooning. In this way, fleet operators can enhance operational efficiency and reduce fuel consumption. This study addresses the AT scheduling and platooning problem considering cargo consolidation. The problem is the scheduling of ATs to transport cargo while consolidating cargo and forming platoons between two terminals, all while minimizing operational costs. A mixed-integer linear programming (MILP) model is formulated for the proposed problem. In addition, we conduct extensive numerical experiments to evaluate the proposed model. The results show that Gurobi can solve instances with different sizes to optimality or near-optimality. Impact analysis is also conducted to explore the influences of several factors, such as maximal platoon size and the load capacity of AT, on the system performance and to provide managerial insights. Full article

The platooning of cars and trucks is a pertinent approach for autonomous driving due to the effective utilization of roadways. The decreased gas consumption levels are an added merit owing to sustainability. Conventional platooning depended on Dedicated Short-Range Communication (DSRC)-based vehicle-to-vehicle communications. The computations were executed by the platoon members with their constrained capabilities. The advent of 5G has favored Intelligent Transportation Systems (ITS) to adopt Multi-access Edge Computing (MEC) in platooning paradigms by offloading the computational tasks to the edge server. In this research, vital parameters in vehicular platooning systems, viz. latency-sensitive radio resource management schemes, and Age of Information (AoI) are investigated. In addition, the delivery rates of Cooperative Awareness Messages (CAM) that ensure expeditious reception of safety-critical messages at the roadside units (RSU) are also examined. However, for latency-sensitive applications like vehicular networks, it is essential to address multiple and correlated objectives. To solve such objectives effectively and simultaneously, the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework necessitates a better and more sophisticated model to enhance its ability. In this paper, a novel Cascaded MADDPG framework, CMADDPG, is proposed to train cascaded target critics, which aims at achieving expected rewards through the collaborative conduct of agents. The estimation bias phenomenon, which hinders a system’s overall performance, is vividly circumvented in this cascaded algorithm. Eventually, experimental analysis also demonstrates the potential of the proposed algorithm by evaluating the convergence factor, which stabilizes quickly with minimum distortions, and reliable CAM message dissemination with 99% probability. The average AoI quantity is maintained within the 5–10 ms range, guaranteeing better QoS. This technique has proven its robustness in decentralized resource allocation against channel uncertainties caused by higher mobility in the environment. Most importantly, the performance of the proposed algorithm remains unaffected by increasing platoon size and leading channel uncertainties. Full article

This study proposes a guidance strategy based on human–machine transition (HMT) for cooperative adaptive cruise control (CACC) truck platoon actuator failures. Existing research on the CACC platoon mainly focuses on upper-level planning and rarely considers platoon planning failures caused by actuator failures. This study proposes that the truck in the platoon creates sufficient space on the target lane through HMT when the actuator fails, thereby promoting lane changes for the entire team. The effectiveness of the proposed strategy is evaluated using the Simulation of Urban Mobility (SUMO) simulation. The results demonstrate that under conditions ensuring the normal operation of traffic flow, this guidance strategy enhances the platoon’s lane-changing capability. In addition, this strategy exhibits stronger robustness and efficiency in different traffic densities. This guidance strategy provides valuable insights into improving the driving efficiency of CACC truck platoons in complex road environments. Full article

Intelligent truck platoons can benefit road transportation due to the short gap and better fuel economy, but they are also subject to dynamic uncertainties and external disturbances. Therefore, this paper develops a novel robust control algorithm for connected truck platoons. By introducing a linearized expression method of platoon error dynamics based on state measurement, the state feedback mechanism combined with a nonsmooth controller for a truck platoon is proposed in the development of the distributed control method. The state-feedback controller can drive the nominal platoon system to the state of second-order consensus, and the nonsmooth controller counterparts the uncertainties and disturbances. The convergence and string stability of the proposed control algorithm are demonstrated both theoretically and experimentally, and the effectiveness and robustness are also verified by simulation tests. Full article

Understanding the spatiotemporal characteristics of merging behavior is crucial for the advancement of autonomous driving technology. This study aims to analyze on-ramp vehicle merging patterns, and investigate how various factors, such as merging scenarios and vehicle types, influence driving behavior. Initially, a framework based on a high-definition (HD) map is developed to extract trajectory information in a meticulous manner. Subsequently, eight distinct merging patterns are identified, with a thorough examination of their behavioral characteristics from both temporal and spatial perspectives. Merging behaviors are examined temporally, encompassing the sequence of events from approaching the on-ramp to completing the merge. This study specifically analyzes the target lane’s spatial characteristics, evaluates the merging distance (ratio), investigates merging speed distributions, compares merging patterns and identifies high-risk situations. Utilizing the latest aerial dataset, exiD, which provides HD map data, the study presents novel findings. Specifically, it uncovers patterns where the following vehicle in the target lane chooses to accelerate and overtake rather than cutting in front of the merging vehicle, resulting in Time-to-Collision (TTC) values of less than 2.5 s, indicating a significantly higher risk. Moreover, the study finds that differences in merging speed, distance, and duration can be disregarded in patterns where vehicles are present both ahead and behind, or solely ahead, suggesting these patterns could be integrated for simulation to streamline analysis and model development. Additionally, the practice of truck platooning has a significant impact on vehicle merging behavior. Overall, this study enhances the understanding of merging behavior, facilitating autonomous vehicles’ ability to comprehend and adapt to merging scenarios. Furthermore, this research is significant in improving driving safety, optimizing traffic management, and enabling the effective integration of autonomous driving systems with human drivers. Full article

The increasing demand for efficient and safe transportation systems has led to the development of autonomous vehicles and vehicle platooning. Truck platooning, in particular, offers numerous benefits, such as reduced fuel consumption, enhanced traffic flow, and increased safety. In this paper, we present a drone-based decentralized framework for truck platooning in highway monitoring scenarios. Our approach employs multiple drones, which communicate with the trucks and make real-time decisions on whether to form a platoon or not, leveraging Model Predictive Control (MPC) and Unscented Kalman Filter (UKF) for drone formation control. The proposed framework integrates a simple truck model in the existing drone-based simulation, addressing the truck dynamics and constraints for practical applicability. Simulation results demonstrate the effectiveness of our approach in maintaining the desired platoon formations while ensuring collision avoidance and adhering to the vehicle constraints. This innovative drone-based truck platooning system has the potential to significantly improve highway monitoring efficiency, traffic management, and safety. Our drone-based truck platooning system is primarily designed for implementation in highway monitoring and management scenarios, where its enhanced communication and real-time decision-making capabilities can significantly contribute to traffic efficiency and safety. Future work may focus on field trials to validate the system in real-world conditions and further refine the algorithms based on practical feedback and evolving vehicular technologies. Full article

This article focuses on the development and assessment of a PID-based computationally cost-efficient longitudinal control algorithm for platooning trucks. The study employs a linear controller with a nested architecture, wherein the inner loop regulates relative velocities while the outer loop governs inter-vehicle distances within platoon vehicles. The design of the proposed PID controller entails a comprehensive focus on system identification, particularly emphasizing actuation dynamics. The simulation framework used in this study has been established through the integration of TruckSim^® and Simulink^®, resulting in a co-simulation environment. Simulink^® serves as the platform for control action implementation, while TruckSim^® simulates the vehicle’s dynamic behavior, thereby closely replicating real world conditions. The significant effort in fine-tuning the PID controller is described in detail, including the system identification of the linearized longitudinal dynamic model of the truck. The implementation is followed by an extensive series of simulation tests, systematically evaluating the controller’s performance, stability, and robustness. The results verify the effectiveness of the proposed controller in various leading truck operational scenarios. Furthermore, the controller’s robustness to large fluctuations in road grade and payload weight, which is commonly experienced in commercial vehicles, is evaluated. The simulation results indicate the controller’s ability to compensate for changes in both road grade and payload. Additionally, an initial assessment of the controller’s efficiency is conducted by comparing the commanded control efforts (total torque on wheels) along with the total fuel consumed. This initial analysis suggests that the controller exhibits minimal aggressive tendencies. Full article

Truck platooning is a promising approach for reducing fuel consumption, improving road safety, and optimizing transport logistics. This paper presents a drone-based decentralized truck platooning system that leverages the advantages of Ultra-Wideband (UWB) technology for precise positioning, robust communication, and real-time control. Our approach integrates UWB sensors on both trucks and drones, creating a scalable and resilient platooning system that can handle dynamic traffic conditions and varying road environments. The decentralized nature of the proposed system allows for increased flexibility and adaptability compared to traditional centralized platooning approaches. The core platooning algorithm employs multi-objective optimization, taking into account fuel efficiency, travel time, and safety. We propose a strategy for the formation and management of platoons based on UWB sensor data with an emphasis on maintaining optimal inter-vehicle secure distances and compatibility between trucks. Simulation results demonstrate the effectiveness of our approach in achieving efficient and stable platooning while addressing the challenges posed by real-world traffic scenarios. The proposed drone-based decentralized platooning system with UWB technology paves the way for the next generation of intelligent transportation systems that are more efficient, safer, and environment friendly. Full article

With the development of autonomous driving technology, truck platooning control has become a reality. Truck platooning can improve road capacity by maintaining a minor headway. Platooning systems can significantly reduce fuel consumption and emissions, especially for trucks. In this study, we designed a Platoon-MAPPO algorithm to implement truck platooning control based on multi-agent reinforcement learning for a platooning facing an on-ramp scenario on highway. A centralized training, decentralized execution algorithm was used in this paper. Each truck only computes its actions, avoiding the data computation delay problem caused by centralized computation. Each truck considers the truck status in front of and behind itself, maximizing the overall gain of the platooning and improving the global operational efficiency. In terms of performance evaluation, we used the traditional rule-based platooning following model as a benchmark. To ensure fairness, the model used the same network structure and traffic scenario as our proposed model. The simulation results show that the algorithm proposed in this paper has good performance and improves the overall efficiency of the platoon while guaranteeing traffic safety. The average energy consumption decreased by 14.8%, and the road occupancy rate decreased by 43.3%. Full article

In recent decades, the automotive industry has moved towards the development of advanced driver assistance systems to enhance the comfort, safety, and energy saving of road vehicles. The increasing connection and communication between vehicles (V2V) and infrastructure (V2I) enables further opportunities for their optimisation and allows for additional features. Among others, vehicle platooning is the coordinated control of a set of vehicles moving at a short distance, one behind the other, to minimise aerodynamic losses, and it represents a viable solution to reduce the energy consumption of freight transport. To achieve this aim, a new generation of adaptive cruise control is required, namely, cooperative adaptive cruise control (CACC). The present work aims to compare two CACC controllers applied to a platoon of heavy-duty electric trucks sharing the same linear spacing policy. A control technique based on reinforcement learning (RL) algorithm, with a deep deterministic policy gradient, and a classic linear quadratic control (LQC) are investigated. The comparative analysis of the two controllers evaluates the ability to track inter-vehicle distance and vehicle speed references during a standard driving cycle, the string stability, and the transient response when an unexpected obstacle occurs. Several performance indices (i.e., acceleration and jerk, battery state of charge, and energy consumption) are introduced as metrics to highlight the differences. By appropriately selecting the reward function of the RL algorithm, the analysed controllers achieve similar goals in terms of platoon dynamics, energy consumption, and string stability. Full article

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. Full article

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. Full article

This paper presents an exploration into the capabilities of an adaptive PID controller within the realm of truck platooning operations, situating the inquiry within the context of Cognitive Radio and AI-enhanced 5G and Beyond 5G (B5G) networks. We developed a Deep Learning (DL) model that emulates an adaptive PID controller, taking into account the implications of factors such as communication latency, packet loss, and communication range, alongside considerations of reliability, robustness, and security. Furthermore, we harnessed a Large Language Model (LLM), GPT-3.5-turbo, to deliver instantaneous performance updates to the PID system, thereby elucidating its potential for incorporation into AI-enabled radio and networks. This research unveils crucial insights for augmenting the performance and safety parameters of vehicle platooning systems within B5G networks, concurrently underlining the prospective applications of LLMs within such technologically advanced communication environments. Full article

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. Full article