Heterogeneous Vehicles Research Articles

Ecological Cooperative Adaptive Cruise Control for Heterogenous Vehicle Platoons Subject to Time Delays and Input Saturations

Public concerns about energy crisis and environmental issues lead to higher fuel economy standards and more stringent limitations on greenhouse gas emissions for ground vehicles, and ecological cooperative adaptive cruise control (eco-CACC) is considered to be effective in reducing fuel consumption and greenhouse gas emissions of vehicle platoons. In this paper, an eco-CACC strategy is presented for heterogeneous platoons with time delays and input saturations to achieve platoon stability, fuel economy, riding comfort and driving efficiency. The proposed eco-CACC strategy includes distributed linear feedback control (DLFC) for following vehicles and model predictive control (MPC) for the leading one. To obtain platoon stability, the DLFC protocol is designed using state errors between an ego vehicle and its preceding one, and the frequency domain method is employed to derive the sufficient conditions of platoon stability for the DLFC protocol. Then, to achieve fuel economy, passenger comfort and driving efficiency without violating the different input saturations of vehicles, the constrained fuel consumption optimization problem of the leading vehicle (CFCO-LV) is formulated based on delay MPC, where the weighted sum of the overall fuel consumption and velocity band-stop functions of vehicle platoon is minimized. To quickly obtain the MPC controller, an improved particle swarm optimization (PSO) algorithm is used to solve the CFCO-LV problem. Simulations are implemented to validate the effectiveness of the proposed eco-CACC strategy, and the simulation results demonstrate that, compared with benchmark, the proposed strategy can save 2.19%~8.24% of fuel for the heterogeneous platoon under different velocity ranges.

Distributed Eco-Driving Control of a Platoon of Electric Vehicles Through Riccati Recursion

This paper presents a distributed optimization procedure for the cooperative eco-driving control problem of a platoon of electric vehicles subject to safety and travel time constraints. Individual optimal trajectories are generated for each platoon member to account for heterogeneous vehicles and for the road slope. By rearranging the problem variables, the Riccati recursion can be applied along the chain-like structure of the platoon and be used to solve the problem by repeatedly transmitting information up and down the platoon. Since each vehicle is only responsible for its own part of the computations, the proposed control strategy is privacy-preserving and could therefore be deployed by any group of vehicles to form a platoon spontaneously while driving. The energy efficiency of this control strategy is evaluated in numerical experiments for platoons of electric trucks with different masses and rated motor powers.

Vehicle Routing Problem Model with Practicality

Truck platooning has recently become an essential issue in automatic driving. Though truck platooning can increase safety and reduce fuel consumption and carbon emissions, the practical vehicle routing problem involved in truck platooning has not been sufficiently addressed. Therefore, we design a mixed-integer linear programming model for the routing problem in truck platooning considering the deadline of vehicles, continuous-time units, different fuel reduction rates, traffic congestion avoidance, and heterogeneous vehicles. In addition, a forward–backward heuristic called the “greedy heuristic” is presented for reasonable computation time. To validate the model’s performance, several parameters, such as the percentage of fuel reduction, percentage of detour vehicles, and percentage of platooned links (road segments), are considered. Additionally, various cases are considered with varying fuel reduction rates, traffic flow rates, and time windows.

Metaheuristics with variable diversity control and neighborhood search for the Heterogeneous Site-Dependent Multi-depot Multi-trip Periodic Vehicle Routing Problem

The planning of vehicle routes is a major issue involved in supply chains. In real environment, we can find situations involving a very large number of clients or constraints which indicate that exact methods should be avoided. In this context, this paper presents two metaheuristcs which are used to solve a complex problem named the Heterogeneous Site-Dependent Multi-depot Multi-trip Periodic Vehicle Routing Problem (HSDMDMTPVRP). The HSDMDMTPVRP is a real problem found in the automotive industry and considers several well-known Vehicle Routing Problems (VRP). The first metaheuristic is an adaptation of the Unified Hybrid Genetic Search (UHGS) which considers an advanced diversity control, feasibility control and a restart mechanism. The second one is a new metaheuristic named Adaptive Variable Neighborhood Race (AVNR) which combines variable neighborhood search and adaptive mechanisms integrated with a shrinking population managed with a diversity mechanism. Both approaches are also used for solving some variants of the VRP: Heterogeneous VRP, Site dependent VRP, Periodic VRP and Multi-trip VRP. Our computational experiments used 398 available instances in the literature with generic code path and also present 20 new instances for the HSDMDMTPVRP. The metaheuristics solved all instances with only one set of parameters and the results outperform or present the same solutions found by several state-of-the-art algorithms, showing the good performance of the approaches. Out of the 398 previously tested literature instances, the proposed metaheuristics found 140 new best-known solutions and 209 of the best-known ones. For the remaining instances, both approaches found results very close to best ones known.

Distributed finite-time neural network observer-based consensus tracking control of heterogeneous underwater vehicles

This note scrutinizes the distributed finite-time tracking problem of heterogeneous multi-agent systems with uncertain dynamics and environmental disturbances. To estimate the exogenous disturbances, an adaptive neural network-based disturbance observer is developed in which the approximation error of the neural network is compensated using adaptation laws. Based on finite-time stability theory, a distributed finite-time observer is proposed for each follower in order to estimate the leader's position. Moreover, an adaptive dynamic sliding mode-based controller is also developed for each follower, which ensures finite-time convergence of the tracking error between the leader and the follower. Incorporating adaptive control into the developed composite control structure leads to reducing the chattering, as well as enhancing the robustness and accuracy. Stability analysis and finite-time convergence of the tracking error are performed using the Lyapunov theory and multiple time scale principle, respectively. Finally, simulations and comparisons are performed in order to verify the effectiveness of the developed control algorithm for a group of autonomous underwater vehicles (AUVs).

Situation-aware communication topologies in heterogeneous platooning scenarios

Communication-based controllers for platooning are often designed for a homogeneous vehicle composition in a normal driving situation. However, in realistic applications, platoons will consist of different vehicles types or brands and thus be heterogeneous, which increases the requirements for safe operation. In addition, controllers designed for the nominal driving situation are not necessarily able to handle disturbances, such as the cut-in of a foreign vehicle or an emergency braking of the entire platoon. The authors’ previous work has shown that for different driving situations (e.g. emergency braking or cut-in of a foreign vehicle) different communication topologies can be beneficial in order to control the platoon. In this paper, a concept for switching the communication topology depending on the current driving situation in a heterogeneous vehicle platoon is presented. The concept is applied for coordinated emergency braking, for the startup from standstill and for the cut-in of a foreign vehicle. For these situations, it is shown that varying the communication topology of an otherwise unchanged local controller can prevent collisions within the platoon. Furthermore, improvements can be achieved by considering the different vehicle types and their position in the vehicle string. For the example driving situations, it is shown how suitable communication topologies can be identified.

Heuristic Surface Path Planning Method for AMV-Assisted Internet of Underwater Things

Ocean exploration is one of the fundamental issues for the sustainable development of human society, which is also the basis for realizing the concept of the Internet of Underwater Things (IoUT) applications, such as the smart ocean city. The collaboration of heterogeneous autonomous marine vehicles (AMVs) based on underwater wireless communication is known as a practical approach to ocean exploration, typically with the autonomous surface vehicle (ASV) and the autonomous underwater glider (AUG). However, the difference in their specifications and movements makes the following problems for collaborative work. First, when an AUG floats to a certain depth, and an ASV interacts via underwater wireless communication, the interaction has a certain time limit and their movements to an interaction position have to be synchronized; secondly, in the case where multiple AUGs are exploring underwater, the ASV needs to plan the sequence of surface interactions to ensure timely and efficient data collection. Accordingly, this paper proposes a heuristic surface path planning method for data collection with heterogeneous AMVs (HSPP-HA). The HSPP-HA optimizes the interaction schedule between ASV and multiple AUGs through a modified shuffled frog-leaping algorithm (SFLA). It applies a spatial-temporal k-means clustering in initializing the memeplex group of SFLA to adapt time-sensitive interactions by weighting their spatial and temporal proximities and adopts an adaptive convergence factor which varies by algorithm iterations to balance the local and global searches and to minimize the potential local optimum problem in each local search. Through simulations, the proposed HSPP-HA shows advantages in terms of access rate, path length and data collection rate compared to recent and classic path planning methods.

Ubiquitous Control Over Heterogeneous Vehicles: A Digital Twin Empowered Edge AI Approach

The forthcoming of automated driving has led to vehicular heterogeneity, where vehicles with different automation levels, including connected and automated vehicles (CAVs), connected vehicles (CVs) and human-driven vehicles (HVs), coexist in the road traffic. However, the design of current traffic control systems fail to account for the vehicular traffic heterogeneity. In addition, the emerging artificial intelligence (AI) based traffic control strategies require large quantities of computation resources and generate high delay, which cannot meet the faultintolerance requirement of the traffic control system. Therefore, there is an urgent need for constructing a new traffic control framework to jointly satisfy the fault-intolerance requirement and realize ubiquitous control over the heterogeneous vehicles. Inspired by this, a digital twin (DT) empowered edge AI framework is proposed in this article. The DT's virtualization capability is utilized to enable virtual risk assessment and performance analysis under the fault-intolerant traffic control system. In addition, the DT's offline learning capability helps the edge AI understand the road traffic data more intelligently to perform enforced optimizations and decisions. Then, a three-layer vehicle control paradigm is discussed under the proposed framework. In the first layer, deep reinforcement learning (DRL) is utilized to intelligently control the CAV acceleration and lane-changing. In the second layer, indirect CV/HV control can be performed by adjusting the target speed and penetration rate of the DRLcontrolled CAVs which are mixed in the traffic flow. In the third layer, vehicle-to-everything (V2X) communications and variable message signs are utilized to realize the direct control of CVs and HVs. Experimental

A distributed robust control strategy for electric vehicles to enhance resilience in urban energy systems

Resilient operation of multi-energy microgrid is a critical concept for decarbonization in modern power system. Its goal is to mitigate the low probability and high damaging impacts of electricity interruptions. Electrical vehicles, as a key flexibility provider, can react to unserved demand and autonomously schedule their operation in order to provide resilience. This paper presents a distributed control strategy for a population of electrical vehicles to enhance resilience of an urban energy system experiencing extreme contingency. Specifically, an iterative algorithm is developed to coordinate the charging/discharging schedules of heterogeneous electrical vehicles aiming at reducing the essential load shedding while considering the local constraints and multi-energy microgrid interconnection capacities. Additionally, the gap between electrical vehicle energy and the required energy level at the departure time is also minimised. The effectiveness of the introduced distributed coordinated approach on energy arbitrage and congestion management is tested and demonstrated by a series of case studies.

Scheduling multi-pattern precooling service resources for post-harvest fruits and vegetables using the adaptive large neighborhood search

This study focuses on the multi-pattern precooling service resources scheduling (MPPSRS) issue recently derived from the practice of small-scale farming. In this issue, a heterogeneous fleet of vehicles belonging to different service pattens is considered to fulfil precooling requests of farmers in the service time windows. The studied issue is regarded as a variant of heterogeneous fleet vehicle routing problem with time windows (HFVRPTW) in which heterogeneous service efficiencies among vehicles are considered. To the best of our knowledge, this is the first time that such a characteristic of vehicles’ heterogenous service efficiencies is considered in vehicle routing problems. This new characteristic increases the potential combination ways of nodes in the temporal aspect, which expands the search space of the problem and leads to a more challenging optimization issue. This study formulates the MPPSRS issue using a mixed-integer programming model in which details from the real world are considered. Motivated by the challenge of computational time, an adaptive large neighborhood search (ALNS) metaheuristic is proposed to solve the model. Results obtained from a case study of the apple industry from Luochuan County, China show advantages of using multi-pattern precooling service resources in reducing total operating cost.

An Analysis on Sustainable One-to-One Pickup and Delivery Problem with Heterogeneous Vehicles

Amaç: Bu çalışmanın temel amacı, birebir toplama ve dağıtım problemi için önerilen modelin çeşitli senaryolar altında emisyon salınımı ve yakıt tüketimi gibi önemli faktörleri göz önünde bulundurarak uygulanabilirliğini ve kullanımından elde edilecek faydaları ortaya koyabilmektir.Yöntem: Bu çalışmada, araç rotalama probleminin alt türlerinden biri olan birebir toplama ve dağıtım problemi ele alınmaktadır. Problemin çözümü için bir Karışık Tam Sayılı Programlama modeli önerilmiştir.Bulgular: Analizler kapsamında, kısa mesafe yük taşımacılığı faaliyetlerinde elektrikli motora sahip araçların kullanımının emisyon salınımları ve maliyet bakımından faydaları ortaya konmuştur. Ek olarak, sürdürülebilirlik performansının daha da yukarıya çekilmesi adına, kullanılan elektrikli araçlar için ihtiyaç duyulan elektrik enerjisini güneş panelleri ile üretmeyi hedefleyen potansiyel bir yatırım için maliyet analizi yapılmıştır. Güneş panellerine yapılacak yatırımın düşük (ortalama %6) bir getiri oranına sahip oluşundan ötürü bu yatırımın finansal açıdan uygun olmadığı sonucuna varılmıştır.Özgünlük: Literatürdeki ilgili çalışmalardan farklı olarak, dağıtımda kullanılacak araçların elektrikli ve dizel motorlu olduğu varsayılarak farklı senaryolar altında sürdürülebilirlik göstergeleri üzerine değerlendirmelerde bulunulmuştur. Ayrıca, güneş panellerine yapılacak yatırım sonucu elde edilecek çevresel fayda ile çalışmanın sürdürülebilir lojistik yönetimi literatürüne katkı sağlayacağı düşünülmektedir.

Distributed integrated sliding mode control via neural network and disturbance observer for heterogeneous vehicle systems with uncertainties

A distributed sliding mode control based on neural network and disturbance observer is investigated for heterogeneous vehicle systems in this paper. The vehicle systems in this paper consider both parameter uncertainty and disturbance. First, the radial basis function neural network is applied to estimate the parameter uncertainty owing to its universal approximation ability, and the disturbance observer is designed to compensate for the external disturbance. Compared with the existing adaptive methods, the disturbance observer can estimate the external disturbance directly and effectively. Then, constant time headway policy is employed to regulate the inter-vehicle distance and improve the string stability. Unlike most existing sliding mode control strategy for vehicle platoons, the string stability is achieved via employing sufficient conditions on the control parameters rather than employing coupled sliding mode control. Afterward, modified constant time headway policy is designed to reduce the effect of nonzero initial inter-vehicle spacing and improve the traffic density. Finally, the simulation results with constant time headway policy and modified constant time headway policy are provided to demonstrate the effectiveness and advantages of the proposed approaches.

Energy-efficient driving scheduling for heterogeneous electric vehicles with consideration of overtaking

Applying individual eco-driving methods within a group of electric vehicles (EVs) is usually restricted by a preceding vehicle because the optimal velocity is different by type of EV. Driving with the same velocity for different types of vehicles is not a fundamental solution because of inefficiency. Overtaking a preceding vehicle allows enhancement of the driving efficiency, though it should be used carefully because energy consumption by the overtaking depends greatly on given conditions: time constraint for overtaking, the distance between intersections, vehicle type, and overtaking scheme. This paper introduces a system-level framework for vehicle overtaking velocity planning and scheduling to maximize the total driving efficiency of all vehicles. The framework starts with an accurate EV powertrain modeling of various vehicle types and derives the optimal velocity and acceleration for overtaking. Simulation results show the energy-efficient velocity planning for overtaking that reduces a driving cost function related to energy and delay. The proposed overtaking framework achieves 15% driving cost savings compared with the conventional group driving.

Research on a hybrid neural network task assignment algorithm for solving multi-constraint heterogeneous autonomous underwater robot swarms.

Studying the task assignment problem of multiple underwater robots has a broad effect on the field of underwater exploration and can be helpful in military, fishery, and energy. However, to the best of our knowledge, few studies have focused on multi-constrained underwater detection task assignment for heterogeneous autonomous underwater vehicle (AUV) clusters with autonomous decision-making capabilities, and the current popular heuristic methods have difficulty obtaining optimal cluster unit task assignment results. In this paper, a fast graph pointer network (FGPN) method, which is a hybrid of graph pointer network (GPN) and genetic algorithm, is proposed to solve the task assignment problem of detection/communication AUV clusters, and to improve the assignment efficiency on the basis of ensuring the accuracy of task assignment. A two-stage detection algorithm is used. First, the task nodes are clustered and pre-grouped according to the communication distance. Then, according to the clustering results, a neural network model based on graph pointer network is used to solve the local task assignment results. A large-scale cluster cooperative task assignment problem and a detection/communication cooperative work mode are proposed, which transform the cooperative cooperation problem of heterogeneous AUV clusters into a Multiple Traveling salesman problem (MTSP) for solving. We also conducted a large number of experiments to verify the effectiveness of the algorithm. The experimental results show that the solution efficiency of the method proposed in this paper is better than the traditional heuristic method on the scale of 300/500/750/1,000/1,500/2,000 task nodes, and the solution quality is similar to the result of the heuristic method. We hope that our ideas and methods for solving the large-scale cooperative task assignment problem can be used as a reference for large-scale task assignment problems and other related problems in other fields.

Cooperative driving of heterogeneous uncertain nonlinear connected and autonomous vehicles via distributed switching robust PID-like control

In this work, the leading-tracking control problem for heterogeneous and uncertain nonlinear autonomous vehicles is addressed. These latter communicate their status information over a wireless communication network and engage in cooperative movements in extra-urban traffic environment. The nonlinear vehicle dynamics are affected by time-varying parameter uncertainties, and the air-drag reduction caused by platooning is explicitly considered. The variation of V2V links as a result of cooperative manoeuvres is modelled by incorporating switching into the communication network topology. To this aim, we develop a novel robust distributed Proportional-Integral-Derivative (PID)-like control protocol which ensures that all vehicles within the platoon robustly track the behaviour of the leader, despite the presence of both unknown parameters uncertainties and network switching. The analytical stability of the proposed control strategy is demonstrated using Lyapunov theory. Sufficient stability conditions are expressed as a set of feasible Linear Matrix Inequalities (LMIs) whose solution allows the robust control gains to be properly tuned. The effectiveness of the approach is evaluated via the Mixed Traffic Simulator (MiTraS) co-simulation platform, which combines MATLAB/Simulink with SUMO microscopic traffic simulator. The exhaustive simulation analysis, involving the Monte Carlo method, is carried out considering different cooperative platooning manoeuvres, and confirms the theoretical derivation.

A location-inventory-routing problem to design a circular closed-loop supply chain network with carbon tax policy for achieving circular economy: An augmented epsilon-constraint approach

The emergence of the circular economy concept has pushed industry owners towards the green supply chain and waste reduction. The closed-loop supply chain originates from the concept of the circular economy and its purpose is to increase efficiency and profitability by reducing waste and energy consumption. Hence, in this research, an integrated bi-objective mixed-integer linear programming model is developed with the aim of optimizing both operational and strategic decisions in a closed-loop supply chain network. The proposed model benefits the location-inventory-routing problem to structure the network, and it applies a carbon tax policy and vehicle scheduling problem to reduce emissions and vehicle waiting time, respectively. Considering problems such as split-delivery, storage possibility, shortage, supplier selection, order allocation, heterogenous vehicles, and uncertain demand has led to the development of a comprehensive model. A stochastic scenario-based approach is used to deal with demand uncertainty, and an augmented epsilon-constraint method is employed to solve the proposed bi-objective model. The applicability of the proposed model and the effectiveness of the bi-objective solution approach for achieving circular economy are examined through its implementation in a cable and wire industry in Iran.

A decomposition approach for the periodic consistent vehicle routeing problem with an application in the cleaning sector

This study is inspired by a challenging logistic problem encountered in the cleaning service sector. The company wishes to solve the consistent vehicle routeing problem over a three-month planning horizon. The company has a heterogeneous vehicle fleet to guarantee multiple frequencies of visits to its customers. The objective is to minimise the number of vehicles used and the total distance travelled. This problem is a generalisation of the periodic vehicle routeing problem. We decompose the problem into two sub-problems, namely, the planning and routeing optimisation sub-problems. We construct a mathematical model for the former and a large neighbourhood search for the latter. We evaluate the performance of our approach using the results of the industrial partner and instances from the literature on problems that are closely related to our case study. Our approach is found to be effective and robust. Our results outperform the existing company's plan in terms of solution quality, and staff convenience, and speed. We also discovered new best solutions on some of the instances from the literature.

An integrated closed loop returnable transport for perishable products in a green inventory routing system using heterogeneous vehicles and uncertainty in to and fro supply

An integrated closed loop returnable transport for perishable products in a green inventory routing system using heterogeneous vehicles and uncertainty in to and fro supply

Подход к расчету интенсивности транспортных потоков при организации наземных перевозок в районе ликвидации последствий чрезвычайных ситуаций по данным дистанционного зондирования Земли

When carrying out emergency recovery work to eliminate the consequences of an emergency, it is necessary to promptly provide information on the state of transport infrastructure facilities, which is possible only when using aerospace survey data. The aim of the study is to increase the efficiency of transportation in the emergency zone using heterogeneous vehicles under the conditions of synchronization of its movement, based on the use of Earth remote sensing data. Data source analysis for traffic flow modeling was carried out. Land transportation typical network graph description is presented. The dependences of the intensity of traffic flows along the edges of the network graph on traffic conditions, infrastructure characteristics and loading and unloading operations, parameters of wheeled, tracked vehicles and railway cars are revealed. It is proposed to use the minimax criterion when calculating the maximum traffic flow intensity along the network graph path and the entire transport network capacity. Based on the analysis of the tasks of monitoring the state of transport infrastructure facilities, the possibility of using Earth remote sensing data to reveal damage and flooding in the emergency zone was assessed. Based on the simulation modeling results of the ground transportation organization in the area of emergency response based on the Earth remote sens-ing data, the applicability of the proposed approach to the traffic flows intensity calculating based on the Earth remote sensing data was confirmed.

Distributed Adaptive Fixed-Time Robust Platoon Control for Fully Heterogeneous Vehicles

This article focuses on the distributed adaptive fixed-time platoon tracking problem for third-order fully heterogeneous nonlinear vehicles. The modified dynamics of each vehicle are constructed, and a practically fixed-time criterion is set up. Moreover, the singularity problem in the fixed-time and finite-time control is addressed well by designing new virtual signals and exploiting the inequality technique and power transformation scheme instead of the approximation method. The distributed nonlinear fixed-time tracking protocol is constructed by virtue of the recursive algorithm, and the design process is simplified by a tracking differentiator. In particular, the upper bound of the settling time has nothing to do with initial conditions. Further, the disturbances are tackled via robust <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> control theory. Finally, simulation tests are contained to illustrate the performance of the proposed protocols.