Autonomous Electric Vehicles Research Articles

A heterogeneous continuum traffic flow model considering mixed connected and autonomous vehicles

Under the background of mixed scenarios of Connected and Autonomous Vehicles and Connected and Autonomous Electric Vehicles, a novel mixed car-following model considering the information of multiple vehicles and the difference of safety spacing in different driving scenarios is proposed. Then, the corresponding heterogeneous continuum traffic flow model is derived. Secondly, through the linear and nonlinear analysis approaches, the linear stability condition and the KdV-Burgers equation are yielded to describe the characteristics of traffic flow evolution. Finally, numerical simulations are performed to explore the density evolution of traffic flow under different initial density and permeability conditions. The conclusions can be summarized as follows: (1) The derived model can well simulate spatiotemporal phase changes and traffic flow phenomena such as stop-and-go waves and local cluster effects. (2) With the increase of the permeability of electric vehicles and speed difference sensitivity coefficient, the road density decreases from 0.15veh/m to 0.06veh/m, the traffic congestion area decreases, and the traffic congestion is effectively suppressed, which also verifies the rationality of the above theory.

Energy saving and speed control in autonomous electric vehicle using enhanced manta ray foraging algorithm optimized intelligent systems

The integration of autonomous vehicles (AVs) with hybrid electric vehicles (HEVs) aims to address the problem of optimizing energy economy while improving reliability and safety. By using intelligent driver support technologies and sophisticated control algorithms, this method aims to lower emissions and fuel consumption. Therefore, the HEVs develop an intelligent driver aid system that integrates an energy management system (EMS) with adaptive cruise control (ACC). To maximize energy use, the system integrates HEVs with autonomous vehicle technologies. In order to maintain safe distances from the lead vehicle, identify the desired acceleration, and switch between speed and distance control, the ACC uses a deep learning system in conjunction with a tilt integral derivative (TID) controller. This ensures stability. Based on ACC commands, the EMS controls energy usage. An enhanced manta ray foraging optimization (EMRFO) technique is used to significantly enhance speed control and energy economy. Simulink/MATLAB analysis of the suggested model shows notable gains in energy consumption control, particularly with the ACC with deep neural network (DNN) system, and considerable reductions in driving risks. In addition, the system precisely controls engine torque to maintain a 72.75 % State of Charge (SoC) and an average engine energy efficiency of 32.36 %. To validate the performance, the proposed approach is put into practice in real-time configuration. The configuration for the experiment produced an ideal efficiency of 31.99 % with minimal error using the proposed method.

Road user opinions and needs regarding small modular autonomous electric vehicles: Differences between elderly and non‐elderly in Norway

AbstractThis study examines road user opinions regarding small modular autonomous electric vehicles, focusing on the differences between the elderly and non‐elderly populations in Norway. The data allowed for a comparison between 193 respondents under 65 years old and 208 respondents over 65 years old. The results highlighted significant differences between the two groups about the vehicles, their usability, and the likeliness of using them as public transport if implemented in the future. Traffic safety and personal security were found to be decisive aspects, for respondents over 65 years old being more worried about safety and security than their counterparts. Trust that the authorities will ensure the safe implementation of such vehicles in the current transportation system was also significantly different between the two groups, with the younger generations having more trust in the authorities than the older group. The results shed light on road user opinions about a small modular transport mode, particularly on those over 65 years old, indicating a need for research efforts to better identify how this new form of public transport should be implemented in the future to improve the mobility of all travellers and meet the needs of the seniors.

Search for The Best Route on A GPS-Based Autonomous Electric Vehicle Using The A-Star Algorithm

Search for The Best Route on A GPS-Based Autonomous Electric Vehicle Using The A-Star Algorithm

Design of intelligent cruise control system using fuzzy-PID control on autonomous electric vehicles prototypes

Electric vehicles provide a solution for using alternative fuels, namely, electricity. Electric vehicles are used for short distances and intercity travel over long distances, increasing the risk of accidents. Cruise Control is a technology embedded in vehicles to maintain stable speeds; this system will automatically adjust the vehicle's speed when motion changes cause changes in vehicle speed. This study aims to apply lidar sensors to detect distance in the Intelligent Cruise Control (ICC) system using the Fuzzy-PID control method. Testing results were obtained at safe distance inputs of 5, 6, and 7 meters with various object distances. All the tests were carried out; the response systems were obtained with an average settling time of 5 seconds and an average overshoot of 1.53%. Therefore, the proposed Fuzzy-PID method works well for controlling Intelligent Cruise Control systems in autonomous electric vehicle prototypes.

Enhancement of energy utilization efficiency and speed control of autonomous electric vehicles (AEVs): A hybrid approach

Enhancement of energy utilization efficiency and speed control of autonomous electric vehicles (AEVs): A hybrid approach

A Bi-objective location-routing model for the healthcare waste management in the era of logistics 4.0 under uncertainty

The purpose of this study is to apply Industry 4.0-based technologies to improve the management of infectious healthcare waste considering location-routing problem and population risk under uncertainty. To achieve this, a decision support system is developed and implemented utilizing a bi-objective mixed-integer linear programming (MILP) model. The bi-objective MILP model improves the performance of the healthcare waste management by applying Industry 4.0 technologies, including electric autonomous vehicles, information sharing system, internet of things (IoT), Global Navigation Satellite System (GNSS), and RFID-tagged waste bags. We develop a multi-objective solution approach by integrating the lexicographic and TH methods. The validity of the model has been established through its implementation in seven hospitals in the city of Karaj, Iran. The results denoted significant improvements in waste collection efficiency, route optimization, and the reduction of contamination risks.

Charging infrastructure assessment for shared autonomous electric vehicles in 374 small and medium-sized urban areas: An agent-based simulation approach

This research examines the use of Shared Autonomous Electric Vehicles (SAEVs) in 374 U.S. small and medium-sized urban areas, focusing on fleet and infrastructure needs through agent-based simulations. It assesses metrics such as fleet size, trips per vehicle, and charging station requirements, considering two charger types: Level 2 and Level 3. The findings show significant spatial differences in SAEV operations and infrastructure across these cities. Statistical analysis links these variations to regional road networks and travel patterns. The study finds Level 3 chargers more efficient, requiring fewer stations and enabling more trips per vehicle compared to Level 2 chargers. Furthermore, Level 3 chargers exhibit a greater number of trips per SAEV and a higher ratio of vehicles to charging stations. These findings highlight the significance of considering charging infrastructure characteristics to optimize SAEV fleet performance and promote sustainable transportation systems in urban areas. This study significantly contributes by identifying the spatial variation and correlates of the SAEVs' operational and charging infrastructural performance. Policymakers, urban planners, and transportation service providers can leverage these insights to design and implement effective charging infrastructure for SAEV fleets, thereby advancing the transition to cleaner and more efficient mobility solutions.

Optimal management of coupled shared autonomous electric vehicles and power grids: Potential of renewable energy integration

Shared Autonomous Electric Vehicles (SAEVs) are pivotal for future transportation, offering both promise and challenges upon integration with the power grid. This symbiosis augments power system flexibility, stability and reliability through Vehicle-to-Grid (V2G) services, and optimize transportation efficiency. However, it amplifies the demand for robust charging infrastructure and electricity power during peak periods. This paper proposes a framework employing a sequential receding horizon optimization approach to manage SAEV mobility and charging dynamics. Focused on maximizing transportation service quality while ensuring power grid stability, the model accommodates dynamic trip requests and electricity generation, utilizing a rolling horizon algorithm. Notably, the study explores the potential of SAEVs in fortifying the integration of renewable energy resources (RES) into the power grid. Our research strives to equip policymakers and system planners with a robust tool for crafting efficient and sustainable future urban transportation and energy systems.

A Branch-and-Price algorithm for the electric autonomous Dial-A-Ride Problem

The Electric Autonomous Dial-A-Ride Problem (E-ADARP) consists in scheduling a fleet of electric autonomous vehicles to provide ride-sharing services for customers that specify their origins and destinations. The E-ADARP considers the following perspectives: (i) a weighted-sum objective that minimizes both total travel time and total excess user ride time; (ii) the employment of electric autonomous vehicles and a partial recharging policy. This paper presents the first labeling algorithm for a path-based formulation of the DARP/E-ADARP, where the main ingredient includes: (1) fragment-based representation of paths, (2) a novel approach that abstracts fragments to arcs while ensuring excess-user-ride-time optimality, (3) construction of a sparser new graph with the abstracted arcs, which is proven to preserve all feasible routes of the original graph, and (4) strong dominance rules and constant-time feasibility checks to compute the shortest paths efficiently. This labeling algorithm is then integrated into Branch-and-Price (B&P) algorithms to solve the E-ADARP. In the computational experiments, the B&P algorithm achieves optimality in 71 out of 84 instances. Remarkably, among these instances, 50 were solved optimally at the root node without branching. We identify 26 new best solutions, improve 30 previously reported lower bounds, and provide 17 new lower bounds for large-scale instances with up to 8 vehicles and 96 requests. In total 42 new best solutions are generated on previously solved and unsolved instances. In addition, we analyze the impact of incorporating the total excess user ride time within the objectives and allowing unlimited visits to recharging stations. The following managerial insights are provided: (1) solving a weighted-sum objective function can significantly enhance the service quality, while still maintaining operational costs at nearly optimal levels, (2) the relaxation on charging visits allows us to solve all instances feasibly and further reduces the average solution cost.

Data and Energy Impacts of Intelligent Transportation—A Review

The deployment of intelligent transportation is still in its early stages and there are many challenges that need to be addressed before it can be widely adopted. Autonomous vehicles are a class of intelligent transportation that is rapidly developing, and they are being deployed in selected cities. A combination of advanced sensors, machine learning algorithms, and artificial intelligence are being used in these vehicles to perceive their environment, navigate, and make the right decisions. These vehicles leverage extensive data sourced from various sensors and computers integrated into the vehicle. Hence, massive computational power is required to process the information from various built-in sensors in milliseconds to make the right decision. The power required by the sensors and the use of additional computational power increases the energy consumption, and, hence, could reduce the range of the autonomous electric vehicle relative to a standard electric car and lead to additional emissions. A number of review papers have highlighted the environmental benefits of autonomous vehicles, focusing on aspects like optimized driving, improved route selection, fewer stops, and platooning. However, these reviews often overlook the significant energy demands of the hardware systems—such as sensors, computers, and cameras—necessary for full autonomy, which can decrease the driving range of electric autonomous vehicles. Additionally, previous studies have not thoroughly examined the data processing requirements in these vehicles. This paper provides a more detailed review of the volume of data and energy usage by various sensors and computers integral to autonomous features in electric vehicles. It also discusses the effects of these factors on vehicle range and emissions. Furthermore, the paper explores advanced technologies currently being developed by various industries to enhance processing speeds and reduce energy consumption in autonomous vehicles.

Integrated optimization of charging infrastructure, fleet size and vehicle operation in shared autonomous electric vehicle system considering vehicle-to-grid

Shared autonomous electric vehicles (SAEVs) are predicted to become a significant solution to reduce global emissions and energy consumption resulting from urban transportation. The centralized operation of SAEVs not only allows large-scale travel demand response but also can provide essential ancillary services to the smart grid through the concept of vehicle-to-grid (V2G). With V2G technology, unused electric vehicles can work as a distributed energy storage facility for the electricity grid to smoothen the intermittent demand. Designing and operating a V2G-enabled SAEV system is challenging. This problem involves complicated planning and operational decisions, as well as time-varying electric tariffs. In this work, a flow-based Integer Linear Programming (ILP) model is formulated for determining the optimal configurations (charging infrastructure and fleet size) and daily operation strategies (serving passengers, relocation, charging/discharging). The developed mathematical model allows for maximizing the total profit, comprising investment cost, revenue from serving passengers, and V2G profit through charging/discharging schedules. A two-stage Benders decomposition-based algorithm is proposed to address the sophisticated ILP problem. Via testing instances in the Manhattan network based on real-world and synthetic data, we have demonstrated the feasibility of our approaches and studied the benefits of integrating V2G in the SAEV system.

Adaptive Drift Control of Autonomous Electric Vehicles After Brake System Failures

Adaptive Drift Control of Autonomous Electric Vehicles After Brake System Failures

Optimizing routing and scheduling of shared autonomous electric taxis considering capacity constrained parking facilities

This paper focuses on routing and scheduling of autonomous electric vehicles to provide reservation-based shared ride services, while a set of parking facilities with limited capacity are used for vehicle intermittent charging. A mixed-integer linear program model is formulated in the form of a vehicle routing problem with satellite facilities (VRPSF), subject to a series of additional time and capacity-related constraints. The objective of the model is to minimize the total operating costs of the system, including those related to vehicle miles traveled and the deployed vehicle fleet size. The number of vehicles inside each parking facility is tracked so as to ensure that the capacity is never exceeded throughout the service horizon. A customized solution method based on an adaptive large neighborhood search algorithm with an explicit treatment of parking facility choices is developed. A series of numerical experiments, consisting of both hypothetical examples and a real-world case study in Hangzhou, China, have been conducted to evaluate the effectiveness and applicability of the proposed model and algorithm. The results demonstrate that ride-sharing services and parking facilities have the potential to significantly reduce the total vehicle energy consumption and operating costs for a shared autonomous electric taxi (SAET) operator in practical scenarios.

Performance Improvement of an Electric Vehicle Charging Station Using Brain Emotional Learning-Based Intelligent Control

Electric vehicle (EV) charging facilities are essential to their development and deployment. These days, autonomous microgrids that use renewable energy resources to energize charging stations for electric vehicles alleviate pressure on the public electricity grid. Nevertheless, controlling and managing such charging stations’ energy is difficult due to the nonlinearity and irregular character of renewable energy sources. The current research recommends using a Brain Emotional Learning Intelligent Control (BELBIC) controller to enhance an autonomous EV charging station’s performance and power management. The charging station uses a battery to store energy and is primarily powered by photovoltaic (PV) solar energy. The principles of BELBIC are dependent on emotional cues and sensory inputs, and they are based on an emotion processing system in the brain. Noise and parameter variations do not affect this kind of controller. In this study, the performance of a conventional proportional–integral (PI) controller and the suggested BELBIC controller is evaluated for variations in solar insolation. The various parts of an EV charging station are simulated and modelled by the MATLAB/Simulink framework. The findings show that, in comparison to the conventional PI controller, the suggested BELBIC controller greatly enhances the transient responsiveness of the EV charging station’s performance. The EV keeps charging while the storage battery perfectly saves and keeps steady variations in PV power, even in the face of any PV insolation disturbances. The suggested system’s simulation results are provided and scrutinized to confirm the concept’s suitability. The findings validate the robustness of the suggested BELBIC control versus parameter variations.

Performance Evaluation of YOLOv5-based Custom Object Detection Model for Campus-Specific Scenario

This study evaluates the performance of a custom object detection model based on the YOLOv5 architecture, specifically tailored for autonomous electric vehicles. The model undergoes pre-processing using the Roboflow computer vision platform, which offers a wide range of tools for data pre-processing and model training. The experiments were conducted on a diverse dataset comprising various objects encountered in campus-specific driving scenarios, such as pedestrians, vehicles, buildings, and obstacles. The performance of the custom object detection model is assessed using standard metrics, including precision, recall, mean average precision (mAP), and intersection-over-union (IoU) at different thresholds. The training process was conducted in a controlled environment, resulting in a Precision of 0.851, a Recall of 0.831, and a mAP of 0.843. These metrics were analyzed to evaluate the YOLOv5-based custom object detection model's ability to detect and categorize objects accurately, its precision in predicting bounding boxes, and its capability to handle various object categories. We also examined the effects of different hyperparameters and data augmentation techniques on the model's performance, including variations in learning rate, batch size, and optimizer algorithms to determine their impact on accuracy and convergence. This analysis provided valuable insights into the model's strengths and weaknesses, highlighting areas for improvement and optimization. These findings are instrumental in developing and deploying advanced object detection systems to enhance the safety and reliability of autonomous electric vehicles.

A new adaptive Kriging-based optimization (AKBO) framework for constrained optimization problems: A case study on shared autonomous electric vehicle system design

Adaptive Kriging-based optimization (AKBO) efficiently performs optimization by employing adaptive sampling methods that sequentially update Kriging model. To solve constrained optimization problems, existing adaptive sampling methods add samples based on probability of feasibility (PoF) representing the probability that all constraints are satisfied. However, these methods have the limitation that samples are added inefficiently when the PoF is inaccurate. Therefore, this study proposes a new AKBO framework that is robust to instability of PoF in adaptive sampling. To this end, a near constraint boundary region that is expected to be close to the constraint boundary is defined based on a probability measure. Then, a near constraint boundary search (NCBS) algorithm, which explores the sample point that minimizes the objective function within the near constraint boundary region, is developed for global search, whereas a near optimum search (NOS) algorithm, which searches for better solutions that may exist near the current optimum candidate, is developed for local search. To alleviate the distortions of the Kriging model that may occur due to the highly nonlinear response, a truncated constraint function (TCF) method that limits the extremely violated constraint values used in the design of experiment (DoE) of the Kriging model is also proposed. This study verifies the performance of the proposed AKBO framework through the shared autonomous electric vehicle (SAEV) system design optimization problem. Comparison of optimum designs derived from various optimization methods shows that the proposed AKBO framework derives the feasible global optimum with high accuracy and efficiency.

Simulation-Based Investigation of On-Demand Vehicle Deployment for Night Bus Routes Using the Monte Carlo Method

Public transportation systems, including trams and buses, play a crucial role in urban traffic. However, these traditional modes of transport have some well-known drawbacks, such as long distances between stops, lengthy waiting times, and a lack of privacy. In response to these challenges, an innovative mobility concept called “FLAIT-train” offers potential solutions. The FLAIT-train operates on regular roads and aims to provide DOOR-2-DOOR transport, addressing the issues associated with fixed stops and offering increased accessibility and convenience. In its initial phase, the FLAIT-train operates on exclusive lanes, but it is designed to integrate with other traffic eventually. The vehicle technology of FLAIT-trains closely resembles that of battery electric autonomous vehicles. To assess whether FLAIT-trains can be used as a suitable alternative to conventional public transportation systems, this paper employs traffic simulations that consider key performance indicators, including the average waiting time per passenger, maximum waiting time of a single passenger, average in-vehicle time per passenger, and average occupancy rate of the vehicles. Using SUMO software (“Simulation of Urban Mobility”, version 1.12.0), a night bus service scenario is meticulously designed and generated. Within this scenario, both FLAIT-trains and conventional buses are simulated under identical conditions and based on statistical data.

Research and construction of an adaptive drive with increased efficiency based on a balancing friction clutch

Presently, there is significant emphasis on researching the automation of processes, systems, and devices across scientific and technical fields. This emphasis extends notably to sectors like aerospace, robotics, and electric transportation technology. One effective approach to tackling the technical and design challenges of automating various technological tools and processes is through the implementation of adaptive systems. Specifically, achieving efficient and reliable power transmission in electric autonomous and mobile vehicles can be realized by employing an adaptive mechanical drive with two degrees of freedom. The adaptability of this two-mobile system is enabled by introducing an innovative continuous multi-speed drive design featuring an additional friction coupling. The primary attribute ensuring the reliability of this self-adjusting mechanical drive lies in its ability to autonomously adapt to external loads. This is made possible by a balancing friction coupling that establishes a connection between the frictional torque and the relative angular velocity.

Machine learning-based identification of cybersecurity threats affecting autonomous vehicle systems

With the advancement of humanity, transportation and trade activities have increased, leading to the development process of basic land vehicles as more than physical power became necessary. Hand tools were developed with the invention of the wheel, followed by animal-powered vehicles, and then steam engine technology. After the advancement of electromechanical technologies, today's modern vehicles have been developed. Those who used these vehicles thought of transferring control from the human to autonomous driving systems to solve their safety and comfort problems. Today, instead of fully autonomous systems targeted for the future, autonomous driving support systems have been developed. Although these systems aim to increase the safety and comfort of passengers, they can become an easy target for malicious people due to network technologies and remote connection features. The most effective method of protection from these attackers is to conduct vulnerability analysis against newly emerging threats for the systems we use and to rectify identified vulnerabilities. In this research paper, the weaknesses of wireless communication towards remote connection usage of the mini electric autonomous vehicle were investigated, which we developed and produced its mechanics, electronics, and software. In this context, a test environment was created, and the problems and threats in autonomous driving technology were revealed through attacks (Deauth Attack, DoS, DDoS and MitM) made on the test environment. Following the exposed vulnerabilities, studies were conducted for the detection of these attacks using Artificial Intelligence. In the study, different algorithms were used to detect the attacks, and random forest algorithm successfully detected 96.1% of attacks. The main contribution to the field of cybersecurity in autonomous vehicles by providing effective solutions for threat identification and defense.