Vehicle Motion Research Articles

Building A Smart Campus: Designing An Architectural Framework For Campus Security Gates And Vehicle Asset Management With GPS Tracking Technology

Modern technology must be integrated into educational institutions as they develop into "smart campuses" in order to guarantee the security and effectiveness of day-to-day operations. This study combines GPS tracking technology to present an architectural framework for the deployment of intelligent security gates and automotive asset management systems. The objective is to build a secure and effective environment that improves campus security and maximizes the use of available resources. The suggested architecture combines a centralized software platform with innovative hardware elements, including GPS trackers, RFID readers, and security cameras. RFID-enabled security gates improve access control by limiting entrance to the college grounds to only authorized people and vehicles. Surveillance cameras simultaneously offer real-time monitoring to guarantee adherence to security procedures.
 The vehicle asset management system utilizes GPS technology to monitor campus vehicles in real-time, increasing fleet management efficiency and enhancing general campus security. GPS tracking integration allows scheduling maintenance, optimizing routes, and notifying authorities of unwanted vehicle movements.

Ensemble Learning-based Algorithms for Traffic Flow Prediction in Smart Traffic Systems

Due to the tremendous growth of road traffic accidents, Intelligent Transportation Systems (ITSs) are becoming even more important. To prevent road traffic accidents in the long term, it is necessary to find new vehicle flow management techniques in order to optimize traffic flow. With the high growth of deep learning and machine learning, these methods are increasingly being used in ITSs. This research provides a novel conceptual ITS model that aims to predict vehicle movement through the collective learning usage to anticipate intersections. The proposed approach consists of three main stages: data collection through cameras and sensors, implementation of machine learning and deep learning algorithms, and result evaluation, utilizing the coefficient of determination (R-squared), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). To accomplish this, various machine learning and deep learning algorithms, such as Random Forest, LSTM, Linear Regression, and ensemble methods (bagging), were incorporated into the model. The findings revealed the enhancement due to the proposed method, which was observed through a significant performance improvement of 93.52%.

Motion Planning and Control of Autonomous Aggressive Vehicle Maneuvers

Aggressive vehicle maneuvers such as those performed by professional racing drivers achieve high agility motion at the edge of handling limits. These aggressive maneuvers can be used to design human-inspired active safety features for next-generation “accident-free” vehicles. We present a motion planning and control design for autonomous aggressive vehicle maneuvers. The motion planner takes advantages of the sparse stable trees and the enhanced rapidly exploring random tree (RRT*) algorithms. The use of the sparsity property helps to reduce the computational cost of the RRT* method by removing non-useful nodes in each iteration and therefore to rapidly converge to the optimal solution. The proposed motion control design allows the vehicle to operate outside the stability region to accomplish a safe, agile maneuver. A safety region is computed to augment the stability region and the motion control is built on a modified nonlinear model predictive control method. We implement the proposed planner and controller and demonstrate the autonomous aggressive maneuvers on a 1/7-scale racing vehicle platform. Comparison with human expert driver and other existing methods is also presented to demonstrate the performance and robustness. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Motion planning and control of human driver-inspired aggressive vehicle maneuvers is a challenging task because of high-agility, unstable fast vehicle motions. This paper is motivated by addressing this challenge in autonomous driving technologies. Instead of restricting vehicle motions within a stability region that is taken by existing methods, we augment the conservative stability region to a safety region with guaranteed performance. To improve the computational efficiency of sampling-based motion planners, we take advantage of sparsity and also integration of a nonlinear predictive control method to compute feasible vehicle motion in searching space. The stability of the vehicle motion controller and sub-optimality of the motion planner are analyzed and guaranteed. Using a scaled vehicle testbed, we validate and compare the proposed motion planning and control design with other existing methods and human expert driver. The experimental results demonstrate the superior performance than the other methods and comparable with human expert driving skills.

Designing optimal material selection for manufacturing car wheel rims

The selection of suitable material for manufacturing wheel rim is essential toaddress the issue of weight reduction while ensuring the load-bearing capacity of the wheelrims during vehicle movement on the road. In this study, the research team designed a 3Dmodel of the wheel rim using Solidworks software. Afterwards, a numerical simulationmodel using Ansys Workbench software was constructed to calculate the load-bearingcapacity of the wheel rim under two conditions: stationary and moving, using four differentmaterials including steel alloy, aluminum alloy, magnesium alloy, and titanium. From thecalculated results, the research team concluded that aluminum remains a suitable materialfor manufacturing wheel rims. This is due to its appropriate mechanical properties, ease offabrication, cost-effectiveness, and the significant weight reduction achieved when wheelrims are manufactured using aluminum material.

Finite-Time Fault-Tolerant Formation Control for Distributed Multi-Vehicle Networks With Bearing Measurements

This paper addresses a bearing-only formation tracking problem in robotic networks by considering exogenous disturbances and actuator faults. In contrast to traditional position-based coordination strategies, the bearing-only coordinated movements of the unmanned vehicles only rely on the neighboring bearing information. This feature can be utilized to reduce the sensing requirements in the hardware implementation. A gradient-descent protocol is first developed to achieve the desired coordination within a prespecified settling time, where the unknown disturbances are considered in the vehicle dynamics, then the bound of formation tracking error is guaranteed by the Lyapunov approach. In case of damage to the actuators (e.g., motors) in some of the vehicles during the task, fault-tolerant analysis of the proposed controller is provided to ensure the success of the task in extreme environments. Furthermore, the proposed bearing-based method is extended to deal with general linear systems, which can be applied to a wider range of robotic platforms. Finally, numerical simulations and lab-based experiments using unmanned ground vehicles are conducted to validate the effectiveness of the proposed strategy. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The aim of this paper is to develop and design a practical bearing-only formation control approach for multi-vehicle systems. Many real-world complex tasks can be solved by multiple unmanned aerial and ground vehicles being connected by a communication network. This paper has proposed a formation tracking scheme for networked multi-vehicle systems that only relies on the relative bearing information of the neighboring vehicles. Closed-loop stability of the scheme and finite-time convergence of the tracking error have been established using the Lyapunov stability approach. The proposed method ensures the robustness and fault-tolerance of the multi-vehicle system against hardware faults or exogenous disturbances. A systematic set of guidelines on how to apply the proposed strategy in practice is also provided for the control practitioners in the form of an algorithm. In order to demonstrate the feasibility and usefulness of the proposed coordination scheme, numerical simulations and lab-based hardware experiments were conducted. Potential applications of the proposed scheme include search and rescue, security surveillance and cooperative exploration.

Vehicle Motion Planning in Complex Environment via Decomposition and Convexification

Vehicle Motion Planning in Complex Environment via Decomposition and Convexification

CoTEV: Trustworthy and Cooperative Task Execution in Internet of Vehicles

Due to the increasing number of service requests from the vehicles, the load at the road side units (RSUs) increases, which affects the delay-sensitive vehicle services. In Internet of Vehicles (IoV), the vehicles can communicate directly with other vehicles and take help from the vehicles to cooperatively accomplish a task. However, it is very challenging to cooperatively execute a task in an IoV environment with high traffic and dynamic vehicle movements. Furthermore, it is difficult for a task vehicle to choose trustworthy and cooperative vehicles. In this paper, we propose algorithms for cooperative task execution by taking the help of trusted vehicles, when it is not possible to complete a deadline-specified task through the RSUs. We propose a hedonic coalition formation game-based approach to form distributed coalitions of cooperative vehicles. We consider the trust score of the vehicles along with their computational capabilities and journey routes. After each task execution, the service feedback is reflected in the trust score of each cooperative vehicle in the coalition. Our proposed algorithms allow the cooperative vehicles to autonomously choose the coalitions and select a vehicle task to maximize their payoffs. To satisfy the task deadlines in multiple coalitions, we design the merging of vehicle coalitions. We consider the simulation of urban mobility (SUMO) tool to generate the mobility traces of the vehicles in a real road network of Berlin city, which considers the traffic junctions and vehicle density on the roads. Through extensive simulations, we show that the proposed algorithms significantly increase the service rate of delay-sensitive task requests by at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$30.5 \%$</tex-math></inline-formula> and the trust score by at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$20.61 \%$</tex-math></inline-formula> , compared to the benchmark schemes.

Research on the Application of Computer Big Data Technology in Smart Travel

This paper mainly focuses on the algorithms related to local path planning and path tracking control of unmanned vehicles in the process of obstacle avoidance. By introducing the temporal dimension as a reference, the perceptual results are projected onto the 3D spatio-temporal navigation map by combining the multi-target behavior prediction and other means; by increasing the temporal dimension, the static obstacles and dynamic obstacles are unified into the same parameter space. Under this parameter space, the front-end A* path search initializes the unified B spline curve control points, designs the trajectory cost function and performs nonlinear optimization to generate a spatio-temporal trajectory that satisfies the safety collision-free and vehicle motion constraints (speed and acceleration limits), thus transforming the decision and planning problem under the two-dimensional fence dynamic physical space into a static scene decision and planning problem under the three-dimensional spatio-temporal space. Through simulation verification, the whole process of the proposed trajectory planning method takes 51.27ms on average, which meets the driving requirements of driverless cars. In addition, by adjusting the search conditions of the A* algorithm, its overall planning efficiency is improved by 27.86% compared with the search speed of the traditional algorithm. The actual feeling and data results from the real vehicle experiments show its good tracking effect, which verifies the effectiveness and practicality of the algorithm proposed in this paper.

ASSESSMENT OF THE TRAFFIC CONDITIONS OF FIXED-ROUTE VEHICLES IN THE AREA OF BUS STOPS

The conditions of movement of fixed-route vehicles in the area of bus stops are analyzed. The accident rate associated with public road transport in the Sverdlovsk region and Yekaterinburg for three years is considered. Bus stops in the central part of the city have been investigated, most of which are not equipped with a check-in "pocket". The width of the roadway opposite the investigated stopping points is determined. It was recorded that most of the stopping points are installed opposite the roadway, with a width of 11 to 20 m. 65% of the considered stopping points do not have an entrance "pocket", which leads to a stop of public transport in the lane. It has been established that in the presence of a check-in "pocket" buses create a large number of conflict situations with vehicles moving in the same direction, which increases the likelihood of road accidents. The high accident rate involving public transport leads to the need to analyze the movement of fixed-route vehicles in the area of stopping points. Purpose – determination of conditions affecting the safety of route vehicles in the area of stopping points. Methodology statistical methods of processing a full-scale experiment were used in the work. Results: the ranges of changes in the width of the carriageway opposite the stopping points, the impact of the presence of a check-in "pocket" on road safety have been determined. Practical implications: It is advisable to apply the results obtained in the development of measures to ensure road safety in the area of bus stops.

Study of the influence of lateral forces and velocity on the lateral dynamics of automobile

Stability of a car's trajectory of motion is the ability of a car to retain its required state of motion, under different conditions of motion. Depending on different states of motion, cars can lose direction of motion instability when there is a lateral force, due to vehicle speed, due to turning or braking the road with different grip coefficients on the sides of the wheel. In such complex moving conditions, cars need to keep a stable trajectory of movement under the driver's control. The paper examines the stability of the car's trajectory in linear motion conditions with the influence of lateral forces and the velocity of the vehicle, these two factors act to change the state of movement of the vehicle, which significantly affects the safety of the vehicle's movement. The authors looked at the effect of vehicle velocity and lateral forces on vehicle transverse dynamics: lateral forces caused by horizontal winds, inclination of the road surface, speed of movement of the vehicle acting on the entire body create centrifugal inertial forces; this force acts to cause the tires to deform. At this point the vehicle is shifted from its original trajectory. In the research content, the authors built a lateral Dynamics Model, in which the influencing factors are set as inputs to the model including vehicle velocity and transverse wind forces, output is the rotation angle of the body, transverse acceleration and horizontal displacement of the vehicle. The simulation results showed that the effect of vehicle speed was relatively large: lateral displacement increased by 228 % at a speed of 120 km/h. Therefore, when driving on the highway, there should always be driver control to ensure that the car does not deviate from the lane

Оценка безопасности движения большого автономного необитаемого подводного аппарата

The problems of ensuring the safe motion of a large autonomous underwater vehicle (AUV) when it passes through sections with different environmental conditions are considered by the example of the development of the Northern Sea Route. An analysis was made of the influence of various environmental conditions - open and coastal water, fast ice and ice conditions - on the safety of the operation of a large AUV. An approach is proposed to assess the vehicle safety in various environments using a dynamic Bayesian network and conditional probability tables. To test the algorithms for controlling the the vehicle motion and the vehicle itself, a digital test site was developed in the form of a stand for simulating the functioning of the AUV. The development of digital twins in the form of software simulators of the vehicle devices and mechanisms made it possible to test control algorithms on the simulation stand both during normal operation of its systems and in the event of malfunctions due to functioning in various environments. The results obtained on a simulation stand for a large AUV operating on the farthest and most complicated part of the Northern Sea Route - the Vilkitski Strait, helped us to assess the safety of the vehicle operation and outline the ways to improve the safety by upgrading the algorithms for controlling the AUV motion in difficult environmental conditions.

Investigation of Charging Technologies for Electric Vehicles

In today's world, internal combustion engine vehicles are widely used. These vehicles cause harmful emissions as they use fossil fuels for engine propulsion and vehicle movement. To address this issue, electric vehicles have been considered as a solution. In electric vehicles, electric motors are used instead of traditional internal combustion engines and the necessary electrical energy is supplied from battery packs. In this way, electric vehicles do not cause harmful emissions to the environment. If the energy required for electric vehicle charging stations is obtained from renewable energy systems, electric vehicles will be zero-emission vehicles. Battery packs, that provide energy for electric vehicles, typically consist of rechargeable Lithium-ion battery groups and they can be charged using fast charging technology. In this study, the future of transportation, electric vehicles and the standards and technologies used in electric vehicle charging stations have been examined. Additionally, an electric vehicle system has been designed using MATLAB/Simscape.

Position Checking-Based Sampling Approach Combined with Attraction Point Local Optimization for Safe Flight of UAVs.

Trading off the allocation of limited computational resources between front-end path generation and back-end trajectory optimization plays a key role in improving the efficiency of unmanned aerial vehicle (UAV) motion planning. In this paper, a sampling-based kinodynamic planning method that can reduce the computational cost as well as the risks of UAV flight is proposed. Firstly, an initial trajectory connecting the start and end points without considering obstacles is generated. Then, a spherical space is constructed around the topological vertices of the environment, based on the intersections of the trajectory with the obstacles. Next, some unnecessary sampling points, as well as node rewiring, are discarded by the designed position-checking strategy to minimize the computational cost and reduce the risks of UAV flight. Finally, in order to make the planning framework adaptable to complex scenarios, the strategies for selecting different attraction points according to the environment are designed, which further ensures the safe flight of the UAV while improving the success rate of the front-end trajectory. Simulations and real-world experiment comparisons are conducted on a vision-based platform to verify the performance of the proposed method.

Stability analysis and numerical simulation of a car-following model considering safety potential field and V2X communication: A focus on influence weight of multiple vehicles

In a V2X environment, the target vehicle is capable of acquiring motion information from multiple vehicles ahead, and this information plays a crucial role in predicting the target vehicle's motion behavior. To better understand how leading vehicles affect the car-following behavior of the target vehicle, we have developed an improved car-following model that considers the effects of multiple leading vehicles. This model constructs distance-based and field-based models to describe the influence weights of different vehicles on the target vehicle. The stability conditions of the model were obtained through linear stability analysis, and the mKdV equation, which describes the evolution characteristics of traffic density waves in congested areas, was determined through nonlinear analysis. Numerical simulations were conducted to discuss the multi-vehicle effect λ, different vehicle influence weights (distance-based and field-based models) βj, and the number of leading vehicles considered q. The study found that in congested areas, initial perturbations evolve in the form of kink-antikink waves, moving rearward, with the amplitude of the headway curve decreasing as the multi-vehicle effect coefficient value increases. For different vehicle influence weights, the field-based model outperforms the distance-based model. Moreover, as the number of leading vehicles considered q increases, the stability of traffic flow gradually improves. The numerical results are consistent with the theoretical findings, and it is noted that the model successfully enhances vehicular movement efficiency, reduces congestion, and improves road safety. To minimize collision incidents, the improved model can be implemented as an active safety technology.

Fusing crowdsourced data to an adaptive wireless traffic signal control system architecture

In this study, a novel wireless adaptive traffic signal control System architecture is proposed, in which crowdsourced data is integrated to detect the traffic state on each approach of an intersection. Under the wireless architecture, the green signal timing is calculated using a travel time-based Max Pressure control algorithm, which is performed on a single-board computer (SBC, RPi 4). The resulting green timings are sent from SBC to the master controller (ESP32) over serial UART and then to the slave (ESP32) wirelessly using ESP-NOW; slave ESP32 is placed on each approach of the intersection. It integrates a fail-safe mechanism to avoid any vehicular conflicts. The proposed system architecture also embeds a crowdsourced data-based pre-emption of intersections for efficient movement of emergency vehicles. The efficacy of the proposed system is compared using a simulation tool with real-world data. With respect to actuated and Max Pressure based on traffic flow, the proposed algorithm reduces the total delay by 54.8% and 22.96%, respectively. Further, the total queue dissipation time decreases by 8.32% and 5.45%, respectively. It outperforms the actuated signal control as well as Max Pressure based on traffic flows. The capability of transforming existing traffic signal controllers into wireless adaptive traffic signal control systems makes it a very affordable and scalable solution.

Improving safety in mixed traffic: A learning-based model predictive control for autonomous and human-driven vehicle platooning

As autonomous vehicles (AVs) become more common on public roads, their interaction with human-driven vehicles (HVs) in mixed traffic is inevitable. This requires new control strategies for AVs to handle the unpredictable nature of HVs. This study focused on safe control in mixed-vehicle platoons consisting of both AVs and HVs, particularly during longitudinal car-following scenarios. We introduce a novel model that combines a conventional first-principles model with a Gaussian process (GP) machine learning-based model to better predict HV behavior. Our results showed a significant improvement in predicting HV speed, with a 35.64% reduction in the root mean square error compared with the use of the first-principles model alone. We developed a new control strategy called GP-MPC, which uses the proposed HV model for safer distance management between vehicles in the mixed platoon. The GP-MPC strategy effectively utilizes the capacity of the GP model to assess uncertainties, thereby significantly enhancing safety in challenging traffic scenarios, such as emergency braking scenarios. In simulations, the GP-MPC strategy outperformed the baseline MPC method, offering better safety and more efficient vehicle movement in mixed traffic.

Separate legal aspects of movement of vehicles for personal use by natural persons across the customs border of Ukraine

The article is devoted to revealing the specifics of the practical and theoretical problems of moving vehicles for personal use by natural persons across the customs border of Ukraine, certain aspects of their customs control and customs clearance within the framework of customs regulation. Definitions of the categories "goods", "vehicles", "personal vehicles”, "commercial vehicles", "natural persons", "citizens", "personal use of a vehicle", "commercial" use of a vehicle" are defined. It was established that commercial vehicles are not goods according to the requirements of the Customs Code of Ukraine. The types of individual prohibitions when natural persons import vehicles for personal use into the customs territory of Ukraine have been clarified. It was established that customs clearance when citizens import vehicles for personal use into the customs territory of Ukraine is possible for the purpose of free circulation, and the first state registration of imported vehicles in Ukraine, new and those that were in use, or manufactured in Ukraine, is carried out on the condition that they comply with environmental standards not lower than the "EURO-2" level in accordance with technical regulations. The declaration form used by citizens to declare vehicles for free circulation has been clarified. It was revealed that in the case of citizens importing vehicles for personal use, which are imported into the customs territory of Ukraine and are subject to state registration, a customs declaration in the form of a single administrative document is provided for declaration. Features of the passage and movement of land vehicles for personal use across the customs border of Ukraine by natural persons who are not their owners are highlighted. The grounds and conditions for exemption from taxation with customs payments upon importation (forwarding) to the customs territory of Ukraine by natural persons of vehicles for personal use have been defined.

Key factors for developing freight and passenger integrated transportation systems in Brazil

The intensification of e-commerce increases freight movements in urban areas, which negatively contributes to promoting sustainable cities. Conversely, the spare capacity of the public transport system could reduce the externalities of urban freight transport and then promote sustainable cities. This article identifies and evaluates key factors for developing integrated freight and public transport systems. Key factors were identified with a literature review and then classified into benefits and barriers related to the urban environment, and challenges and strengths related to the transport operation. Subsequently, these factors were evaluated by experts and classified by estimating a Luce model. The results indicate that benefits are related to the fight against climate change by optimising urban space and reducing the movements of freight vehicles. Barriers are associated with the lack of a collaborative culture and the need to change consumer behaviour, which helps to promote collaboration and reduce just-in-time demand. For the transport operation, the integration of people and freight systems can reduce operational costs, increase the efficiency of the transport system, and improve the accessibility for people and goods, while promoting economic development by decentralising economic activity. To achieve these benefits, the main challenges concern the logistic activity and the cooperation of stakeholders.

Biosecurity level assessment in commercial poultry farms of central Ethiopia

Proper biosecurity practice is crucial in poultry farming to reduce the risk of diseases. A study was conducted in Adama, Ada’a, and Lume districts, central Ethiopia, to assess the biosecurity measures of commercial poultry farms. A total of 51 farms were randomly selected, and their geographical locations were recorded using GPS devices. The biosecurity level of each farm was assessed using an observation checklist and a biosecurity score. The collected data were analyzed using Stata version 16 for binary data, and QGIS version 3.24.1 was used to map the farms. The results of the study showed that the farms were located close together within 5 km square, 3 km square, and 6 km square areas in Ada’a, Lume, and Adama, respectively. The average distance among the farms was 933.8 meters. The majority of the farms were located within 100 meters of residential areas. Only 58.8% of the farms had adequate space between sheds; 60.8% did not have an adequate drainage system. Ninety-eight percent of the farms obtain chicken from certified breeding enterprises; 92.2% provide underground water for their chickens; 80.4% report frequent cleaning and disinfection of farms; and 82.4% disclose vehicle movement control. Nearly one-third (31.4%) of the farms practice isolation of sick birds but keep them within the same shed where healthy birds are kept. The assessment of biosecurity levels of the study farms revealed that 23.5% (n=12) of the farms had a good biosecurity score, while 76.5% (n=39) had a poor score. The biosecurity scores of the farms showed a statistically significant difference among the study districts (p=0.040), with the Lume district having higher biosecurity scores than Adama and Ada’a districts. The age of the farm owners was significantly associated with the biosecurity score (p=0.003); all farms (100%) owned by individuals aged 20 - 39 had good biosecurity scores, and 80% of those farms owned by individuals aged 40 - 59 had good biosecurity. Farms owned by individuals who have previous chicken-rearing experience had better biosecurity scores than those farms owned by non-experienced owners (p&lt;0.001). Among the farms having professional consultants, 57.7% of them had good biosecurity scores, whereas only 12.0% of farms lacking professional consultants had good biosecurity scores (p=0.015). In conclusion, the study revealed that biosecurity measures were not adequately implemented in poultry farms in central Ethiopia. Farm owners, veterinarians, and livestock authorities must collaborate to implement biosecurity measures to minimize the risk of losses and public health.

Modeling and control design for half electric vehicle with wheel BLDC actuator and Pacejka's tire

We are considering the problem of controlling the longitudinal motion of half-vehicle electric vehicle (EV) or hybrid electric vehicle (HEV). A substantial part of the problem lies in the modeling of the controlled system dynamics, including those of: (i) the half-vehicle chassis motion; (ii) the actuator constituted of a brushless direct-current (BLDC), as an in-wheel motor, along with the associated voltage inverter; (iii) the tire. The dynamics of the latter are presently captured by Pacejka's model which is known to be more most accurate as it accounts for the various phenomena generated at the contact tire/road. The whole system model also takes into account other nonlinear effects, e.g., including the road load, the rolling resistance and the aerodynamic effects. Compared to those used in existing comparable works, the system model developed in this study is much more accurate. But it is also more complex, because it is of higher dimension and stronger nonlinearity, increasing substantially the complexity of the control design problem. The control objectives include tight regulation of the vehicle's speed in the various driving modes and safety of the on-board passengers. These objectives are technically reformulated in terms of reference trajectories, to be matched by the vehicle speeds despite unpredictable variations in aerodynamic forces and road parameters. This challenging control problem is dealt by developing a cascade-structure adaptive controller. The cascade structure is resorted to make the compensation for speed dynamics and disturbances decoupled from those of torque dynamics. The adaptive nature is realized by using parameter estimators, providing the controller with the capacity of compensating the effect of parameter uncertainty. The various controller components are designed making use of several different theoretical design tools including the backstepping control method and Lyapunov stability method. In addition to theoretical analysis, the performances of the developed controller are highlighted by simulation results in the presence of varying aerodynamic forces, road slope and various road surfaces. Comparison with simpler controller is also performed.