Digital twin (DT) has emerged as a promising technology for improving resource allocation decisions in Internet of Vehicles (IoV) networks. In this paper, we consider an IoV network where mobile edge ...computing (MEC) servers are deployed at the roadside units (RSUs). The IoV network provides ubiquitous connections even in areas uncovered by RSUs with the assistance of unmanned aerial vehicles (UAVs) which can act as a relay between RSUs and task vehicles. A virtual representation of the IoV network is established in the aerial network as DT which captures the dynamics of the entities of the physical network in real-time in order to perform efficient resource allocation for delay-intolerant tasks. We investigate an intelligent delay-sensitive task offloading scheme for the dynamic vehicular environment which provides computation resources via local execution, vehicle-to-vehicle (V2V), and vehicle-to-roadside-unit (V2I) offloading modes based on the energy consumption of the system. Moreover, we also propose a multi-network deep reinforcement learning (DRL)-based resource allocation algorithm (RADiT) in the DT-assisted network for maximizing the utility of the IoV network while optimizing the task offloading strategy. Further, we compare the performance of the proposed algorithm with and without the presence of V2V computation mode. RADiT is further evaluated by comparing it with another benchmark DRL algorithm called soft actor-critic (SAC) and a non-DRL approach called greedy. Finally, simulations are performed to demonstrate that the utility of the proposed RADiT algorithm is higher under every condition compared to its respective conditions in SAC and greedy approach. Consequently, the proposed framework jointly improves energy efficiency and reduces the overall delay of the network. The proposed algorithm with UAV relay further increases the efficiency of the network by increasing the task completion rate.
In this paper, we present a control strategy for trajectory tracking and path following of generic paths for underactuated marine vehicles. Our work is inspired and motivated by previous works on ...ground vehicles. In particular, we extend the definition of the hand position point, introduced for ground vehicles, to autonomous surface vehicles and autonomous underwater vehicles, and then use the hand position point as output for a control strategy based on the input-output feedback linearization method. The presented strategy is able to deal with external disturbances affecting the vehicle, e.g., constant and irrotational ocean currents. Using the Lyapunov analysis, we are able to prove that the closed-loop system has an external dynamics that is globally exponentially stable and an internal dynamics that has ultimately bounded states, both for the trajectory tracking and the path following control problems. Finally, we present a simulation case study and experimental results in order to validate the theoretical results.
•We demonstrate the use of reinforcement learning for eco-driving strategies.•Only minimal data on the traffic situation are provided to the agent.•No explicit prediction of the traffic situation is ...required.•The energy saving potential was determined to be up to 11% compared with a green light optimal speed advice system.
Urban settings are challenging environments to implement eco-driving strategies for automated vehicles. It is often assumed that sufficient information on the preceding vehicle pulk is available to accurately predict the traffic situation. Because vehicle-to-vehicle communication was introduced only recently, this assumption will not be valid until a sufficiently high penetration of the vehicle fleet has been reached. Thus, in the present study, we employed Reinforcement Learning (RL) to develop eco-driving strategies for cases where little data on the traffic situation are available.
An A-segment electric vehicle was simulated using detailed efficiency models to accurately determine its energy-saving potential. A probabilistic traffic environment featuring signalized urban roads and multiple preceding vehicles was integrated into the simulation model. Only information on the traffic light timing and minimal sensor data were provided to the control algorithm. A twin-delayed deep deterministic policy gradient (TD3) agent was implemented and trained to control the vehicle efficiently and safely in this environment.
Energy savings of up to 19% compared with a simulated human driver and up to 11% compared with a fine-tuned Green Light Optimal Speed Advice (GLOSA) algorithm were determined in a probabilistic traffic scenario reflecting real-world conditions. Overall, the RL agents showed a better travel time and energy consumption trade-off than the GLOSA reference.
With the requirements for reducing emissions and improving fuel economy, automotive companies are developing electric, hybrid electric, and plug-in hybrid electric vehicles. Power electronics is an ...enabling technology for the development of these environmentally friendlier vehicles and implementing the advanced electrical architectures to meet the demands for increased electric loads. In this paper, a brief review of the current trends and future vehicle strategies and the function of power electronic subsystems are described. The requirements of power electronic components and electric motor drives for the successful development of these vehicles are also presented.
In this article, for a marine aerial-surface heterogeneous (MASH) system composed by a quadrotor unmanned aerial vehicle (UAV) and an unmanned surface vehicle (USV) with heterogeneity, completely ...unknown dynamics and disturbances, the accurate trajectory-tracking problem is solved by creating a novel coordinated trajectory-tracking control (CTTC) scheme. A family of coordinate transformations are devised to convert the MASH system tracking error dynamics into translation-rotation cascade manners, whereby the heterogeneity is removed and finite-time observers for complex unknowns are facilitated. In conjunction with sliding mode based rotation error dynamics, distributed tracking controllers for the quadrotor UAV and the USV are independently synthesized such that cascade tracking error dynamics are globally asymptotically stable. With the aid of cascade and Lyapunov analysis, the entire CTTC solution to the accurate trajectory-tracking problem of the MASH system is eventually put forward. Simulation results and comprehensive comparisons on a prototype MASH system demonstrate the effectiveness and superiority of the proposed CTTC scheme.
•A proactive longitudinal control algorithm for freeway merging is proposed.•The objective of the optimization procedure is to maximize average travel speed.•A safe time gap is used to maintain safe ...distances from surrounding vehicles.
This paper presents a longitudinal freeway merging control algorithm for maximizing the average travel speed of fully automated connected vehicles. Communication with a roadside unit allows the computation and transmission of optimized trajectories to the equipped vehicles. These vehicles then carry out the trajectories and resume normal operation once they cease communication with the roadside controller. A tool was developed to simulate and carry out the merging algorithm, while interfacing with the optimization software LINGO. A hypothetical merging segment was simulated to evaluate the effectiveness of the merging algorithm, and its performance is compared to conventional vehicle operation. During uncongested conditions the algorithm is able to reduce travel time, increase average travel speed and improve throughput. The capacity of the merge segment is directly related to the safe time gap selected to run the algorithm. Once capacity is reached, queuing forms on both the ramp and mainline segments upstream of the merge area. The algorithm provides safe merging operations during this congested traffic state.
•We introduce and solve the electric vehicle routing problem with energy consumption uncertainty.•We formulate the problem as a robust mixed integer linear program.•We solve small instances to ...optimality using robust optimization techniques.•We develop a two-phase heuristic method based on large neighbourhood search to solve larger instances.•We perform an extensive computational study.
Compared with conventional freight vehicles, electric freight vehicles create less local pollution and are thus generally perceived as a more sustainable means of goods distribution. In urban areas, such vehicles must often perform the entirety of their delivery routes without recharging. However, their energy consumption is subject to a fair amount of uncertainty, which is due to exogenous factors such as the weather and road conditions, endogenous factors such as driver behaviour, and several energy consumption parameters that are difficult to measure precisely. Hence we propose a robust optimization framework to take into account these energy consumption uncertainties in the context of an electric vehicle routing problem. The objective is to determine minimum cost delivery routes capable of providing strong guarantees that a given vehicle will not run out of charge during its route. We formulate the problem as a robust mixed integer linear program and solve small instances to optimality using robust optimization techniques. Furthermore, we develop a two-phase heuristic method based on large neighbourhood search to solve larger instances of the problem, and we conduct several numerical tests to assess the quality of the methodology. The computational experiments illustrate the trade-off between cost and risk, and demonstrate the influence of several parameters on best found solutions. Furthermore, our heuristic identifies 42 new best solutions when tested on instances of the closely related robust capacitated vehicle routing problem.
•We propose a cooperative sorting strategy for CAV platoons.•The optimal sorting cost is found with the A* algorithm.•A coordinate system is designed for further minimizing the sorting time.•A ...distributed and stochastic A* algorithm is proposed to improve algorithm performance.
This paper presents a “cooperative vehicle sorting” strategy that seeks to optimally sort connected and automated vehicles (CAVs) in a multi-lane platoon to reach an ideally organized platoon. In the proposed method, a CAV platoon is firstly discretized into a grid system, where a CAV moves from one cell to another in discrete time-space domain. Then, the cooperative sorting problem is modeled as a path-finding problem in the graphic domain. The problem is solved by the deterministic A* algorithm with a stepwise strategy, where only one vehicle can move within a movement step. The resultant shortest path is further optimized with an integer linear programming algorithm to minimize the sorting time by allowing multiple movements within a step. To improve the algorithm running time and address multiple shortest paths, a distributed stochastic A* algorithm (DSA*) is developed by introducing random disturbances to the edge costs to break uniform paths (with equal path cost). Numerical experiments are conducted to demonstrate the effectiveness of the proposed DSA* method. The results report shorter sorting time and significantly improved algorithm running time due to the use of DSA*. In addition, we find that the optimization performance can be further improved by increasing the number of processes in the distributed computing system.
The transparent, flexible, and open-source Python library carculator_truck is introduced to perform the life cycle assessment of a series of medium- and heavy-duty trucks across different powertrain ...types, size classes, fuel pathways, and years in a European context. Unsurprisingly, greenhouse gas emissions per ton-km reduce as size and load factor increase. By 2040, battery and fuel cell electric trucks appear to be promising options to reduce greenhouse gas emissions per ton-km on long distance segments, even where the required range autonomy is high. This requires that various conditions are met, such as improvements at the energy storage level and a drastic reduction of the greenhouse gas intensity of the electricity used for battery charging and hydrogen production. Meanwhile, these options may be considered for urban and regional applications, where they have a competitive advantage thanks to their superior engine efficiency. Finally, these alternative options will have to compete against more mature combustion-based technologies which, despite lower drivetrain efficiencies, are expected to reduce their exhaust emissions via engine improvements, hybridization of their powertrain, as well as the use of biomass-based and synthetic fuels.
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