Advanced Vehicle Systems Research Articles

Vehicle And Driver Recognition for Access Control

The following paper proposes an advanced vehicle and driver recognition system to enhance security at vehicular entry-exit points in response to the growing needs of more efficient and secure access control systems at these checkpoints. The proposed system will integrate license plate recognition and facial recognition technologies using state of-the-art machine-learning models from Ultralytics: YOLOv8 and EasyOCR, respectively. It works in three stages: license plate detection, character alpha-numeric identification, and then driver identity with facial recognition. Hence, the two-tier authentication process based on license plate detection and facial identification ensures further prevention from unauthorized entry. Preliminary evaluation of the system also produced very high values for precision, recall, and the mean average precision, indicating that the VDR system offers substantial advances in effectiveness and security when compared to conventional methodologies for access control.

Multilayer Perceptron-Based Error Compensation for Automatic On-the-Fly Camera Orientation Estimation Using a Single Vanishing Point from Road Lane

This study introduces a multilayer perceptron (MLP) error compensation method for real-time camera orientation estimation, leveraging a single vanishing point and road lane lines within a steady-state framework. The research emphasizes cameras with a roll angle of 0°, predominant in autonomous vehicle contexts. The methodology estimates pitch and yaw angles using a single image and integrates two Kalman filter models with inputs from image points (u, v) and derived angles (pitch, yaw). Performance metrics, including avgE, minE, maxE, ssE, and Stdev, were utilized, testing the system in both simulator and real-vehicle environments. The outcomes indicate that our method notably enhances the accuracy of camera orientation estimations, consistently outpacing competing techniques across varied scenarios. This potency of the method is evident in its adaptability and precision, holding promise for advanced vehicle systems and real-world applications.

Advance Electric vehicle System for Minimization of wiring Harness Using IoV

The rapid growth of the electric vehicle (EV) market demands innovative solutions to optimize vehicle design, improve efficiency, and enhance user experience. Traditional vehicles heavily rely on extensive wiring harnesses to interconnect various components and systems, leading to increased weight, complexity, and production costs. This abstract presents an advanced electric vehicle system that leverages the power of the Internet of Vehicles (IoV) to minimize wiring harnesses, utilizing components such as Wi-Fi, Thinkspeak cloud, Buzzer, solar plat, charging storing in Battery, LED indicators, voltage and current sensor and other smart electronic devices. The proposed system employs Wi-Fi connectivity as a robust wireless communication technology within the vehicle. By utilizing Wi-Fi, the need for physical wiring is significantly reduced, enabling seamless data transfer between vehicle components and the central control unit using ESP-32 now wireless Communications. This approach streamlines the vehicle's internal architecture, leading to a cleaner and lighter design, ultimately improving energy efficiency and reducing manufacturing complexity. Furthermore, the integration of the Thinkspeak cloud serves as a centralized data storage and analysis platform. Thinkspeak cloud enables real-time monitoring and remote access to critical vehicle parameters and performance metrics, facilitating efficient fleet management and predictive maintenance. The cloud-based solution enhances the vehicle's connectivity and allows for over-the-air updates, enabling continuous improvements and adaptability to emerging technologies.

A Novel Control Strategy of Crosswind Disturbance Compensation for Rack-Type Motor Driven Power Steering (R-MDPS) System

The wind suddenly blown to the side of the car (i.e, cross-wind effect) is considered as one of the major lateral disturbances, which causes the unstable motion of the vehicle and the persistent driving fatigue for the driver who tracks the desired travel path. In particular, a commercial vehicle having a large side area is greatly affected by this effect. Therefore, many related automotive/car manufacturers still wish to equip their Advanced Driving Assistant System (ADAS) with the crosswind disturbance compensation control system. Meanwhile, in recent advanced vehicle systems, a rack-type motor-driven power steering (R-MDPS) system is more widely used than a column-type MDPS (C-MDPS) due to the structure advantage and the effective steering assistance. Recognizing those two issues, this paper investigates a novel anti-control and estimation strategy of crosswind disturbance for the R-MDPS system of vehicles with non-negligible side surfaces. Specifically, an adaptive disturbance observer (D.O.B) has been proposed to estimate the crosswind effect. Furthermore, using optimal control theory, the compensation control system is designed to assist the driver in two possible situations. One is for when the driver continues to steer the vehicle under the effect of the crosswind, and the other is for when the driver temporarily loses steering control due to the effect. In addition, the control mode selection conditions between two controls are clearly presented to maximize the efficiency and performance of the proposed control system. Finally, the effectiveness of the proposed control system has been evaluated based on the Simulink/Carsim Co-simulation and HILS environments.

In-Vehicle Test Results for Advanced Propulsion and Vehicle System Controls Using Connected and Automated Vehicle Information

<div class="section abstract"><div class="htmlview paragraph">A key enabler to maximizing the benefits from advanced powertrain technologies is to adopt a systems integration approach and develop optimized controls that consider the propulsion system and vehicle as a whole. This approach becomes essential when incorporating Advanced Driver Assistance Systems (ADAS) and communication technologies, which can provide information on future driving conditions. This may enable the powertrain control system to further improve the vehicle performance and energy efficiency, shifting from an instantaneous optimization of energy consumption to a predictive and “look-ahead” optimization. Benefits from this approach can be realized at all levels of electrification, from conventional combustion engines to hybrid propulsion systems and full electric vehicles, and at all levels of vehicle automation.</div><div class="htmlview paragraph">This paper documents an extensive simulation and experimental campaign that provides a systematic quantification of fuel economy and mobility benefits resulting from leveraging Vehicle-to-Everything (V2X) connectivity, longitudinal (Level 1+) automation, and advanced cylinder deactivation to improve the propulsion system and vehicle efficiency. A Vehicle Dynamics and Powertrain (VD&PT) optimization framework, developed jointly by The Ohio State University and BorgWarner, utilizes advanced route information available from the navigation system and GPS, and Vehicle-to-Infrastructure (V2I) communication when available to minimize the cumulative fuel consumption over a driver-selected route.</div><div class="htmlview paragraph">To assess the benefits of these technologies in real-world conditions, a Monte Carlo simulation framework was developed to quantify the impact of Signal Phase and Timing (SPaT), variability in traffic conditions, and behavior of different drivers on the fuel consumption and vehicle travel time. Further, verification through in-vehicle testing was conducted by reconstructing a subset of these scenarios on a proving ground.</div></div>

Image Distortion and Rectification Calibration Algorithms and Validation Technique for a Stereo Camera

This paper focuses on the calibration problem using stereo camera images. Currently, advanced vehicle systems such as smart cars and mobile robots require accurate and reliable vision in order to detect obstacles and special marks around. Such modern vehicles can be equipped with sensors and cameras together or separately. In this study, we propose new methodologies of stereo camera calibration based on the correction of distortion and image rectification. Once the calibration is complete, the validation of the corrections is presented followed by an evaluation of the calibration process. Usually, the validation section is not jointly considered with the calibration in other studies. However, the mass production of cameras widely uses the validation techniques in calibrations owned by manufacturing businesses. Here, we aim to present a single process for the calibration and validation of stereo cameras. The experiment results showed the disparity maps in comparison with another study and proved that the proposed calibration methods can be efficient.

Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey

Autonomous vehicles are becoming a reality in places with advanced infrastructure to support their operations. In crowded places, harsh environments, missions that require these vehicles to be aware of the context in which they are operating, and situations requiring continuous coordination with humans such as in disaster relief, advanced-vehicle systems (AVSs) need to be better contextually aware. The vast literature referring to “context-aware systems” is still sparse, focusing on very limited forms of contextual awareness. It requires a structured approach to bring it together to truly realize contextual awareness in AVSs. This paper uses a human-AVSs (HAVSs) lens to polarize the literature in a coherent form suitable for designing distributed HAVSs. We group the relevant literature into two categories: contextual-awareness related to the vehicle infrastructure itself that enables AVSs to operate, and contextual-awareness related to HAVSs. The former category focuses on the communication backbone for AVSs including ad-hoc networks, services, wireless communication, radio systems, and the cyber security and privacy challenges that arise in these contexts. The latter category covers recommender systems, which are used to coordinate the actions that sit at the interface of the human and AVSs, human–machine interaction issues, and the activity recognition systems as the enabling technology for recommender systems to operate autonomously. The structured analysis of the literature has identified a number of open research questions and opportunities for further research in this area.

Novel Interference Cancellation of Channel Estimation Scheme Based on LTE in V2V Communications

Transportation infrastructures integrate advanced Information Technology to enable the operations and management of transportation systems. Advanced vehicle and road systems of a comprehensive concept that improve the efficiency and safety of road traffic are being built and will be commercially available through Intelligent Transport Systems (ITS) technology and services. To support ITS technology, Vehicle-To-Everything (V2X) is needed, and in the 3rd Generation Partnership Project, many studies have focused on Long Term Evolution (LTE)-based vehicle communications. In order to realize reliable and optimized communications performance in vehicle communications, which move in propagation environments at high speed, in this study, we propose a novel channel estimation scheme suited for LTE sidelink-based Vehicle-To-Vehicle systems. Conventional channel estimation schemes can be categorized as Decision-Directed Channel Estimation, spectral temporal averaging, and smoothing methods. In this study, unlike conventional channel estimation schemes, we propose a Novel Interference Cancellation of Channel Estimation (NICCE) using Quadratic Smoothing of the pilot symbols, which estimates a channel with greater accuracy, and a novel interference cancellation of channel estimation in data symbols. In simulation results, the proposed NICCE scheme shows improved overall performance in terms of the Normalized Mean Square Error and uncoded Bit Error Rate relative to conventional schemes.

Differing Perceptions of Advanced Driver Assistance Systems (ADAS)

The extent to which drivers’ attitudes toward the use of autonomous vehicle systems can be influenced by various driver factors (e.g., driver age, experience with the systems, etc.) has yet to be firmly established. To investigate driver perceptions and acceptance of advanced vehicle systems, the current research examined initial and repeated exposure to systems while driving under various commonly encountered on-road situations (e.g., emergency braking). Somewhat surprisingly, driver perceptions of safety when driving vehicles with assistive technologies diminished following repeated exposure to the technologies. However, when drivers were afforded more extensive experience, they reported a heightened appreciation of the systems—especially drivers who might benefit most from this assistance (e.g., distracted or older drivers).

Bionic Lane Driving of Autonomous Vehicles in Complex Urban Environments: Decision-Making Analysis

A tactical-level lane-driving and decision-making model that considers multisource information in a complex and dynamic urban environment is critical for the development of autonomous vehicles. A key challenge in operating an autonomous vehicle robustly in the real world is to deal with dynamic and uncertain information. In the real world, drivers are capable of making accurate and timely decisions that should also be required of autonomous vehicles. In this work, the authors describe the development of an algorithm for autonomous lane change functions in which information was extracted from the decision-making process of human drivers to support the decision making of autonomous vehicles. First, a virtual urban traffic environment was built with PreScan, which is a simulation environment for the development of advanced driver assistant systems and intelligent vehicle systems. The vehicle dynamics were simulated by a dynamic model, with 6 degrees of freedom, based on Simulink, and driver decision rules were extracted through the concept of rough set theory. After that, an algorithm was presented for lane-driving decision making at the tactical level when velocity control operation was desirable and feasible. The development of the algorithm was based on driver experience, safety thresholds, and acceptable gap theory. The algorithm was proved to provide satisfactory velocity control actions as well as to safely decide whether to change lanes in a real urban environment. Finally, the reliability and effectiveness of the model was validated by both simulations and real road experiments. The findings from this study can provide a theoretical basis for the in-depth study of driving decision making in complex and uncertain environments.

Modeling and Acceleration Response Analysis for Vehicle Drivability

Vehicle drivability is now one of the keystones of product quality, and is refined aggressively to achieve vehicle brand differentiation and market position. This paper starts with an illustrating of drivabilitys definition, and then the objective measure metrics and model-based approach for vehicle drivability development during advanced vehicle and powertrain system development process are analyzed. The accelerator pedal aggressing and pedal-acceleration response characteristic as drivability critical factors are investigated emphatically at last.

Computational methods for the optimisation and design of electromechanical vehicle systems

The development of advanced electromechanical vehicle systems requires the consideration of multiple physical processes including electromagnetics, fluid flow, heat transfer, and structural mechanics. Accordingly, this paper presents a suite of simulation, optimisation, and design strategies that may be applied to the research and development of modern electric and hybrid-electric vehicles. These computational methods are illustrated through three examples that involve electric motors and electronic systems. Two and three-dimensional topology optimisation studies, parametric geometry sweeps, and multi-scale models are shown to inform unique designs prior to the fabrication and testing of advanced prototypes. It is expected that such computational techniques will have a significant impact on the development of highly efficient future ground transportation systems.

Block-DCT를 이용한 속도 제한 표지판 실시간 인식 알고리듬의 설계

본 논문에서 지능형 안전 자동차 시스템을 위해 연산량를 줄인 속도 제한 표지판 실시간 인식 방법을 제안한다. 제안된 방법은 관심영역의 전체 픽셀 정보를 특징으로 사용한 기존 방법의 큰 연산량을 줄이기 위해 적은 수의 DCT 계수를 선택하고, 격자구조로 분할된 영상에 대해 Block-DCT를 이용하여 산술 연산을 효과적으로 줄였다. 제안된 알고리듬은 연산량을 줄이기 위해 제안된 상관계수와 분산을 이용한 판별식에 따라 DCT 계수를 선택하고 이를 선형 판별법과 Mahalanobis Distance를 이용하여 속도 제한 표지판을 인식한다. 인식 성능을 높이기 위해 연속 프레임의 누적 분류 결과를 사용한다. 실험 결과 연속된 프레임에 대하여 100.0 %의 인식률을 보이며 기존 방식 대비 곱셈 연산량은 69.3 %, 덧셈은 67.9 % 감소를 확인할 수 있었다. This paper proposes a real-time algorithm for speed limit sign recognition for advanced safety vehicle system. The proposed algorithm uses Block-DCT in extracting features from a given ROI(Region Of Interest) instead of using entire pixel values as in previous works. The proposed algorithm chooses parts of the DCT coefficients according to the proposed discriminant factor, uses correlation coefficients and variances among ROIs from training samples to reduce amount of arithmetic operations without performance degradation in classification process. The algorithm recognizes the speed limit signs using the information obtained during training process by calculating LDA and Mahalanobis Distance. To increase the hit rate of recognition, it uses accumulated classification results computed for a sequence of frames. Experimental results show that the hit rate of recognition for sequential frames reaches up to 100 %. When compared with previous works, numbers of multiply and add operations are reduced by 69.3 % and 67.9 %, respectively. Start after striking space key 2 times.

Innovations in collaboration in the design of a low mass vehicle

The Institute for Advanced Vehicle Systems at the University of Michigan-Dearborn conducts research on advanced vehicle systems. Its driving project was the design a low mass vehicle (LMV) 30% lighter than the Toyota Echo. Over 35 individual projects were conducted. Technical results and break-through technologies are summarised. The LMV project involved innovative collaboration among faculty researchers, students from three institutions and industry partners. One innovation was researchers working on a common driving project, assuring that individual projects complement each other. Another was utilising the experience and expertise of industry partners. A third was the automotive design studio, teaming engineering and design students with industry professionals to gain hands-on experience in automotive design. One outcome of LMV research was students gaining what the Accreditation Board for Engineering and Technology calls 'professional skills' – abilities to communicate effectively and be effective team players. Partner companies say they value graduates with these skills.

Autonomous Intelligent Fuzzy Logic Guidance, and Flight Control System for the EFIGENIA EJ-1B MOZART Unmanned Aerial Vehicle UAV

The EFIGENIA EJ-1 is an advanced aerodynamics design vehicle and avionics autopilot system design in which a digital neural network combined with a fuzzy logic system performs the best flight guidance, navigation and flight control condition operation. This paper describes the EFIGENIA EJ-1 Short or Vertical take-off and Landing (S/VTOL) autonomous intelligent Unmanned Aerial Vehicle (UAV) embedded multiprocessor fuzzy logic flight guidance and control system scheme autopilot system designed.

Automotive Materials: Technology Trends and Challenges in the 21st Century

AbstractThe following article is an edited transcript based on the plenary talk given by Alan I. Taub of General Motors Corp. on November 28, 2005, at the Materials Research Society Fall Meeting in Boston.Fuel economy requirements, emissions regulations, and the push for energy independence are key factors driving the auto industry to increase vehicle efficiency. The main avenues to efficiency improvement are powertrain enhancements and mass reduction. This presentation details how General Motors is developing advanced propulsion systems and using lightweight materials to achieve greater vehicle efficiency. Taub, who is executive director of General Motors Research and Development, outlines GM's strategy for advancing propulsion technology, from improvements in the internalcombustion engine to hybridization to full vehicle electrification. He then describes the company's efforts to use lightweight materials such as aluminum and magnesium alloys, high-strength steels, and composites to reduce vehicle weight. Also highlighted is GM's success in employing novel materials in the development of advanced vehicle and powertrain systems to achieve additional efficiencies. One example is the application of smart materials, which enable new features and functions by way of mechamatronic solutions (the integration of smart materials with mechanical systems and electronics). Key technical hurdles that must be overcome to increase the use of these materials by the automotive industry are also discussed.

NASA hypersonic flight demonstrators—overview, status, and future plans

NASA's Next Generation Launch Technology (NGLT) program is developing and maturing advanced propulsion and vehicle systems technologies and flight vehicle concepts to enable future development of safer and more economical launch systems. Within NGLT, NASA is developing advanced air breathing propulsion systems and demonstrating these systems in hypersonic flight vehicles. The flight demonstrations are necessary to fully validate these technologies for application to future space launch vehicles and other flight systems. NASA's Hyper-X Program (X-43A) began the effort to flight demonstrate hypersonic air breathing propulsion systems to provide technologies that will enable development of safer and more economic space access vehicles in the future. Following X-43A, NASA, in collaboration with the United States (US) Department of Defense (DoD), is developing additional, progressively more complex hypersonic X-vehicles that will demonstrate new air breathing propulsion systems, propulsion-airframe integration, and other vehicle systems technologies required for high speed flight up to Mach 15. These technologies will contribute to safer, more reliable and more economic future launch systems and hypersonic aircraft/missiles. This paper describes NASA's current hypersonic flight demonstration projects, status of the efforts, and plans for future vehicles.

The EFIGENIA EJ-1 An S/VTOL Autonomous Unmanned Aerial Vehicle Autocontrolled with an Adaptive Digital Reconfigurable Guidance, Navigation and Flight Control System

This paper describes the EFIGENIA EJ-1 Short or Vertical take-off and Landing (S/VTOL) autonomous unmanned aerial vehicle (UAV). The EFIGENIA EJ-1 is an advanced aerodynamics design vehicle and avionics system which I developed as a personal research project that I designed, builded and actually conducting a serie of flight tests. A Digital neural network combined with a Fuzzy Logic Expert System perform the best flight guidance, navigation and flight control condition operation.

Designing human-centered automation: trade-offs in collision avoidance system design

Human-centered automation problems have multiple attributes: an attribute reflecting human goals and capabilities, and an attribute reflecting automation goals and capabilities. In the absence of a general theory of human interaction with complex systems, it is difficult to define and find a unique optimal multiattribute resolution to these competing design requirements. We develop a systematic approach to such problems using a multiattribute decomposition of human and automation goals. This paradigm uses both the satisficing decision principle which is unique to two-attribute problems, and the domination principle which is a common manifestation of the optimality principle in multiattribute domains. As applied to human-centered automation in advanced vehicle systems, the decision method identifies performance evaluations and compares the safety benefit of a system intervention against the cost to the human operator. We illustrate the method by analyzing an automated system to prevent lane departures.

Metallographic preparation of soft materials: Lead alloys

Metallographic preparation of lead and its alloys is notoriously difficult due to the low hardness of the material and its tendency to pick up and embed abrasive particles readily. Working with component parts for lead acid batteries at Lucas Advanced Vehicle Systems Development, the odd shapes and sizes, such as connector posts and intercell welds, do not lend themselves to microtoming or electropolishing. Vibratory polishing had been used in the past but this method could take up to 6 days to produce an acceptable microsection. Using semiautomatic polishing equipment had proved more successful, but again long periods of time were taken to produce satisfatory results with all the problems of lubricant and abrasive dosing. Tests on newer, commercially available pads and abrasives provided by various manufacturers have been carried out, but it was found that a combination of machining, polishing with diamond, and final polishing with alumina gave the best results. The use of automatic polishing equipment made the preparation easier with both contrarotation and complementary rotation directions. Lubricants and abrasives recommended by the metallographic consumable suppliers for soft materials were not suitable for the easily stained and oxidized lead alloys. This report lays out the steps taken to produce the stated optimized procedure and shows some unusual structures that have been revealed in components taken from batteries undergoing testing, one of which had suffered explosive damage.