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Abstract This article presents a two-stage Dynamic Programming (DP)-based approach to solving the problem of Hybrid Energy Storage System (HESS) component sizing, specifically, the lithium-ion (Li-ion) battery and ultracapacitor (UC) for a mild hybrid electric powertrain. In the first stage, optimal sizing of the battery for the powertrain without a UC is solved for a specified drive cycle, which is used in the reported literature. In the second stage, the battery is complemented with a UC cascaded through a direct current-to-direct current (DC/DC) converter in a semi-active configuration. A DP-based formulation is then constructed and solved for the hybrid energy storage subsystem.

Abstract A wind-tunnel study was undertaken to investigate the drag reduction potential of two-truck platooning, in the context of understanding some of the factors that may influence the potential fuel savings and greenhouse-gas reductions. Testing was undertaken in the National Research Council Canada 2 m × 3 m Wind Tunnel with two 1/15-scale models of modern aerodynamic tractors paired with dry-van trailers configured with and without combinations of side-skirts and boat-tails. Separation distances of 0.14, 0.28, 0.49, 0.70 and 1.04 vehicle lengths were tested (3 m, 6 m, 10.5 m, 15 m, and 22.5 m full scale). Additionally, within-lane lateral offsets up to 0.31 vehicle widths (0.8 m full scale) were evaluated, along with a full-lane offset of 1.42 vehicle widths (3.7 m full scale). This study has made use of a wind-averaged-drag coefficient as the primary metric for evaluating the effect of vehicle platooning.

Abstract Aiming to improve the lateral instability of adaptive cruise control (ACC) systems, both the front and rear vehicles are considered the centers of two control strategies. A vehicle control system is designed to enable the vehicle to automatically find the best following distance based on the displacement and speeds of the front and rear vehicles, hence enhancing driver assistance, traffic efficiency, and road utilization ratio. A practical model predictive control is designed to improve performance, responsiveness, and minor discomfort. A quadratic programming (QP) solver is used to construct an error preview-based mathematical model for the vehicle control, which is then applied to improve the control performance of the system to achieve relative intervehicle distance control. The time sampling of the parameters and the prediction horizon are obtained by numerical simulation, verifying the effectiveness of the ACC system proposed.

Abstract In this article, we use MPC algorithm to design an adaptive path tracking controller based on the vehicle coordinate system, which is effectively applicable to path tracking scenarios with different vehicle speeds and large path curvatures. To reduce the lateral position error and heading angle error, a fitting function learned through a large number of simulations is used to adaptively adjust the prediction horizon parameter and a compensation strategy of steering angle increment designed based on fuzzy control algorithm is used to reduce the influence of model mismatch and low modeling accuracy on the path tracking control effect, then the front wheel steering angle is calculated and output to the vehicle model for path tracking. In this article, multi-scenario simulations are conducted in Simulink and CarSim environments to verify the performance of the proposed controller.

Abstract Commercial vehicles contribute to the majority of freight transportation in the United States. They are also significant fuel consumers, with over 23% of fuel used in transportation in the United States. The gas price volatility and increasingly stringent regulation on greenhouse-gas emissions have driven manufacturers to adopt new fuel-efficient technologies. Among others, an advanced transmission control strategy, which can provide tangible improvement with low incremental cost. In the commercial sector, individual drivers have little or no interest in vehicle fuel economy, contrary to fleet owners. Aggressive driving behavior can greatly increase the real-world vehicle fuel consumption. However, the effectiveness of transmission calibration to match the shift strategy to the driving characteristics is still a challenge.

Abstract The advancement in vision sensors and embedded technology created the opportunity in autonomous vehicles to look ahead in the future to avoid potential obstacles and steep regions to reach the target location as soon as possible and yet maintain vehicle safety from rollover. The present work focuses on developing a nonlinear model predictive controller (NMPC) for a high-speed off-road autonomous vehicle, which avoids undesirable conditions including stationary obstacles, moving obstacles, and steep regions while maintaining the vehicle safety from rollover. The NMPC controller is developed using CasADi tools in the MATLAB environment. The CasADi tool provides a platform to formulate the NMPC problem using symbolic expressions, which is an easy and efficient way of solving the optimization problem. In the present work, the vehicle lateral dynamics are modeled using the Pacejka nonlinear tire model.

Abstract During the entry and exit of attraction viewing, the rapid generation of travel demand and converging traffic flows in a short period can easily pose safety hazards to people due to its complex terrain. This study aims to propose a path planning method that integrates a dynamic traffic model with the A* algorithm for the planning of scenic routes. The study first combines the cellular transport model (CTM) model with the Greenshield model as its dynamic traffic model and then improves the A* algorithm with the Morphin search tree algorithm (Morphin) as its scenic route planning. The results of the study show that the improved A* algorithm reaches the expected error of 10−4 after 21 ms using Matlab tests, and simulation tests are conducted in regular and complex sections of the scenic area.

Abstract With the introduction of autonomous vehicles, interest in platooning of Heavy Goods Vehicle (HGV) is gradually on the rise. Platooning of HGVs has several benefits such as an increase in fuel efficiency, reduction of congestion on roads, and lower costs incurred in operating a fleet. Therefore, several researchers are trying to address this problem by developing vehicle platooning algorithms that will allow HGVs to drive on highways in a tight platoon formation. This article proposes a Proportional Integral Derivative (PID) controller based on the combination of Constant Distance (CD) and Constant Headway Time (CHT) policies to operate an HGV platoon in the Cooperative Adaptive Cruise Control (CACC) mode. In addition to CACC, the controller is tested and verified to carry out splitting and merging maneuvers. An Appeal, Reply, and Implementation (ARI) protocol has been proposed as the communication paradigm for the execution of splitting and merging maneuvers.

Abstract This article investigates the headway and optimal velocity tracking of autonomous vehicles (AVs), considering their predictive driving for the stability and integrity of spatial vehicle formation in the platoon. First, the human-like anticipation car-following model is used for modeling the autonomous system. Second, an adaptive radial basis function neural network (ARBF-NN)-based sliding mode control (SMC) is proposed for the control purpose. The control objective is to regulate traffic perturbation during entire road operations. To enable the controller to experience less computational burden and adaptation complexity, a minimum parameter learning (MPL) has also been integrated with ARBF-NN-based SMC. Third, an illustrative simulation example has been performed for two scenarios, i.e., constant headway and time-varying headway of vehicles.

Abstract Path tracking is an essential stage for vehicle safety control. It is more newsworthy than ever in the automotive context and especially for autonomous vehicle. The study proposes an optimal control method for path tracking problem in inverse vehicle handling dynamics. The proposed method generates an expected trajectory which guarantees minimum clearance to the prescribed path by identifying the optimal steering torque input. Based on this purpose, the path tracking problem, which is treated as an optimal control problem, is then solved by local collocation method and mesh refinement. Finally, a real vehicle test is executed to verify the rationality of the proposed model and methodology. The results show that using control variables as a mesh refinement function can capture the dramatic changes in state variables, and the efficiency improvement is more significant as the number of the grid points increases.

Abstract The conception of Automated Driving Systems is expanding fast with the expectation of the whole society and with heavy investments toward research and development. However, the insertion of these vehicles in real scenarios worldwide is still a challenge for governments, once they require an important evolution of the legal and regulatory framework. Although there are several initiatives to accelerate the insertion process, each country has specificities when considering the traffic scenario. In order to contribute to this emerging problem, this article presents a perspective of how the insertion of these vehicles can be performed considering specificities of the Brazilian scenario, one of the world's biggest car markets. Thus, it is discussed the global scenario of autonomous vehicles, the Brazilian traffic system, and the certification and homologation process, focusing on a new test track proposal.

Abstract In order to reduce collision at a 90-degree intersection, an automatic emergency collision avoidance control method for intelligent vehicles based on vehicle-to-everything (V2X) technology is proposed. Most of the existing automatic emergency braking (AEB) control algorithms are designed for a single high-friction road with reference to the European New Car Assessment Programme (Euro NCAP) evaluation procedures, and they do not consider changes in road friction. Thus, it may be difficult to avoid collision successfully on a low-friction road. Although some studies have considered the variation of road friction, they are only applicable to straight-line rear-end collisions and cannot be directly applied to intersections. In addition, most studies regard the vehicle only as a particle, ignoring the actual dynamic characteristics of the vehicle.

Abstract Recovering solar energy during parking phases can significantly improve light-duty vehicles’ fuel economy (FE) and help in reducing greenhouse gas impact and harmful tailpipe emissions. Nevertheless, the contribution due to off-driving photovoltaic (PV) recharging shall be adequately accounted for when approaching the development and testing of energy-efficient and environment-friendly powertrain energy management. The aim of this work was thus to enhance the existing highly charge-depleting (HCD) control strategy of a 48V mild-hybrid electric vehicle (MHEV), implementing a state-of-charge-dependent soft charge-depleting (SCD) one. The performance of the implemented control strategy was analyzed on two different vehicle configurations, obtained by integrating a solar kit with the aforementioned mild-hybrid powertrain.

Abstract The latest developments in vehicle-to-infrastructure (V2I) and vehicle-to-anything (V2X) technologies enable all the entities in the transportation system to communicate and collaborate to optimize transportation safety, mobility, and equity at the system level. On the other hand, the community of researchers and developers is becoming aware of the critical role of roadway infrastructure in realizing automated driving. In particular, intelligent infrastructure systems, which leverage modern sensors, artificial intelligence, and communication capabilities, can provide critical information and control support to connected and/or automated vehicles to fulfill functions that are infeasible for automated vehicles alone due to technical or cost considerations. However, there is limited research on formulating and standardizing the intelligence levels of road infrastructure to facilitate the development, as the SAE automated driving levels have done for automated vehicles.

Abstract For safe and comfortable automated driving in the urban domain, especially in complex geometries as intersections, the prediction of surrounding traffic participants is fundamental. Several works in this field focus on predicting the behavior of vulnerable road users (VRU) at crossings. However, no approaches were found dealing with predicting the interaction between turning vehicles giving right of way or cooperating with VRU, which is substantial for the trajectory planning of following vehicles. Infrastructural sensor data from an intersection in Germany enables the development of a prediction concept for vehicles interacting with VRU. Our studies show that the original criteria for classifying an interaction between vehicles and VRU—the post-encroachment time (PET)—is not suitable as ground truth criteria for the aimed prediction. Instead, a clustering-based labelling approach with k-means shows promising results in trajectory pattern distinction.

Abstract Aiming at the high false alarm rate of vehicle collision avoidance algorithms at intersections controlled by traffic lights, a vehicle collision avoidance warning algorithm based on vehicle spatiotemporal position prediction (SPPWA) is proposed. The algorithm first obtains real-time data information such as the heading angle and global positioning system (GPS) coordinates of the two vehicles from the OnBoard Unit (OBU), and then the data is preprocessed by different filtering methods, and then excludes the data information that the two vehicles cannot collide. Finally, the filtered data is used to predict the spatiotemporal position of the vehicle before the two vehicles reach the collision point and determine whether the vehicle will collide. The algorithm is verified in three vehicle crash scenarios through PreScan and Matlab/Simulink co-simulation.

Abstract Previous researches about vehicle lateral dynamics mainly focus on one or several specific working conditions and make simulation analysis in the time domain to compare the quality of the steering control system, which cannot compare the performance of the controller as a whole. In this article, an innovative concept was proposed to compare the vehicle steering controller. Combined with the bifurcation theory, the performance of three different steering control systems (front-wheel steering system, four-wheel steering system, and direct yaw-moment control [DYC] system) can be compared intuitively from the phase plane. First of all, taking the front-wheel steering system as the research object, the state phase trajectory of the vehicle under certain speed and different front-wheel steering angle inputs is analyzed, based on the established two degrees-of-freedom (2-DOF) model of vehicle lateral dynamics.

Abstract Excessive brake heating of trucks on downgrades is a cause of continuing concern for the Wyoming Department of Transportation (WYDOT). Brake failure on downgrades characteristically takes a catastrophic toll on lives and property. The Grade Severity Rating System (GSRS) developed by the Federal Highway Administration (FHWA) recommends a maximum safe speed limit that has been identified as a feasible remedy for reducing the incidence of downgrade truck crashes. However, truck characteristics and roadway geometrics have changed over the years following the development of the GSRS. To deal with this development, a research project was initiated by the WYDOT in 2016 to update the GSRS model. The test truck used for the field tests in the prior research project was fitted with disc brakes on the front axle and drum brakes on the rear axle. However, disc brakes represent only about 20% of the brake market.

Abstract For Chinese low-cost airlines network, this article employed two indicators: network topology indexes, to evaluate the current status of the network, and economic performance indexes, to analyze the development potential of the network. From the topology indexes, each airline has its own different characteristics, advantages, and disadvantages, while from economic and socio-demographic indexes, Spring Airlines, West Air, and China United Airlines have obvious advantages and other airlines have distinct shortcomings. Then, the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method was used to comprehensively evaluate Chinese low-cost airlines. The results show that the ranking in terms of network is: Spring Airlines, Western Airlines, China United Airlines, Lucky Air, 9 Air, Chengdu Airlines, and also show Chinese low-cost airlines network is in initial and growth stage.

Abstract The outbreak of COVID-19 pandemic in the beginning of 2020 has made it necessary to review many practices in countless areas, changing our lifestyles drastically. Humanity has put health issues in priority to deal with the disease effectively. While health systems are having difficult times in terms of patient care, vaccination, and treatment protocols, existing designs in many areas have proven to be inefficient in preventing or decelerating the pandemic. As the disease is transmitted mainly by particle transfer through coughing, sneezing, and even with speaking, wearing face masks and keeping a distance of 2 m as well as hygiene (especially hand) are shown to be effective methods. However, long exposure to indoor air populated with people is still a major risk due to the possibility of high concentration of virus-contaminated air.