Development Of Autonomous Vehicles Research Articles

Cooperative Vehicle Infrastructure System or Autonomous Driving System? From the Perspective of Evolutionary Game Theory

In this paper, we explore the trade-offs between public and private investment in autonomous driving technologies. Utilizing an evolutionary game model, we delve into the complex interaction mechanisms between governments and auto manufacturers, focusing on how strategic decisions impact overall outcomes. Specifically, we predict that governments may opt for strategies such as constructing and maintaining infrastructure for Roadside Infrastructure-based Vehicles (RIVs) or subsidizing high-level Autonomous Driving Vehicles (ADVs) without additional road infrastructure. Manufacturers’ choices involve deciding whether to invest in RIVs or ADVs, depending on governmental policies and market conditions. Our simulation results, based on scenarios derived from existing economic data and forecasts on technology development costs, suggest that government subsidy policies need to dynamically adjust in response to manufacturers’ shifting strategies and market behavior. This dynamic adjustment is crucial as it addresses the evolving economic environment and technological advancements, ensuring that subsidies effectively incentivize the desired outcomes in autonomous vehicle development. The findings of this paper could serve as valuable decision-making tools for governments and auto manufacturers, guiding investment strategies that align with the dynamic landscape of autonomous driving technology.

Development, Simulation and Visualization of Autonomous Farm Vehicles

Abstract: A Research in Autonomous Agricultural Vehicle Technology is presented. With the coming of the Smart Farming Revolution the development of more complicated machines has begun. Agricultural machines are made with the capability of driving themselves, using GPS maps and electronic sensors, an approach that will lead to the development of futuristic Precise Autonomous Farming Systems. The basic issues to be addressed are autonomous path planning, obstacle detection and avoidance for which various sensors will be employed such as GPS, laser-based sensors, ultrasonic sensors, IMU sensors and Camera sensors whose data has to be constantly monitored using Dead Reckoning algorithm and constant Pose Estimation. In these sensors, Camera, Ultrasonic, Laser-Based Sensors are used as Detection sensors while the IMU and GPS sensors are used as Localization sensors. Global Avoidance Subsystem which examines the whole environment of an autonomous vehicle mission-level path planner that pre-plans paths around all known obstacles. While Local Avoidance Subsystem/Reactive planner consists of an obstacle filter and an obstacle avoidance algorithm and re-plans a short way for the vehicle to navigate. With the ROS framework and all sensors data, a vehicle can guide itself through an unknown environment. This will help the farmer to undercut the labour shortage and improve the precision in farming which in turn improves the output to meet the growing demands

Cybersecurity of autonomous vehicles – threats and mitigation

The aim of this article is to draw attention to the growing problem of cybersecurity in the field of autonomous vehicles. A notable aspect is the use of autonomous vehicles to enhance the quality of decision-making processes as well as flexibility and efficiency. The implementation of new solutions will lead to improvements not just in transportation and delivery, but also in warehouse management. The growing demand for autonomous solutions, both in the industry and in the daily life of an average consumer, necessitates efforts to ensure their safe operation and use. The present literature review synthetically describes the history of the development of autonomous vehicles and machines. The standards and norms that should be met by products allowed for use as well as threats to cybersecurity, along with examples, are presented herein. The analysis of the collected materials leads to the conclusion that with the development of new technologies and the growth in the importance of autonomous solutions, the number of threats and the importance of systems securing the functioning of devices in cyberspace are increasing. Research on the problem also leads to the conclusion that legal systems do not fully keep up with technological developments, resulting in a lack of normative acts regulating this matter.

Resolving uncertainty on the fly: modeling adaptive driving behavior as active inference.

Understanding adaptive human driving behavior, in particular how drivers manage uncertainty, is of key importance for developing simulated human driver models that can be used in the evaluation and development of autonomous vehicles. However, existing traffic psychology models of adaptive driving behavior either lack computational rigor or only address specific scenarios and/or behavioral phenomena. While models developed in the fields of machine learning and robotics can effectively learn adaptive driving behavior from data, due to their black box nature, they offer little or no explanation of the mechanisms underlying the adaptive behavior. Thus, generalizable, interpretable, computational models of adaptive human driving behavior are still rare. This paper proposes such a model based on active inference, a behavioral modeling framework originating in computational neuroscience. The model offers a principled solution to how humans trade progress against caution through policy selection based on the single mandate to minimize expected free energy. This casts goal-seeking and information-seeking (uncertainty-resolving) behavior under a single objective function, allowing the model to seamlessly resolve uncertainty as a means to obtain its goals. We apply the model in two apparently disparate driving scenarios that require managing uncertainty, (1) driving past an occluding object and (2) visual time-sharing between driving and a secondary task, and show how human-like adaptive driving behavior emerges from the single principle of expected free energy minimization.

Pedestrian Collision Avoidance in Autonomous Vehicles: A Review

Pedestrian collision avoidance is a crucial task in the development and democratization of autonomous vehicles. The aim of this review is to provide an accessible overview of the pedestrian collision avoidance systems in autonomous vehicles that have been proposed by the scientific community over the last ten years. For this purpose, we propose a classification of studies in the literature in terms of the following: (i) pedestrian detection methods, (ii) collision avoidance approaches, (iii) actions, (iv) computing methods, and (v) test methods.

Self-Powered Intelligent Water Droplet Monitoring Sensor Based on Solid-Liquid Triboelectric Nanogenerator.

Real-time monitoring of rainwater is a critical issue in the development of autonomous vehicles and smart homes, while the corresponding sensors play a pivotal role in ensuring their sensitivity. Here, we study a self-powered intelligent water droplet monitoring sensor based on a solid-liquid triboelectric nanogenerator (SL-TENG). The sensor comprises a SL-TENG, a signal acquisition module, a central processing unit (CPU), and a wireless transmission module, facilitating the real-time monitoring of water droplet signals. It is worth noting that the SL-TENG has self-powering characteristics and can convert the kinetic energy of water droplets into electrical energy. The excellent output performance, with open-circuit voltage of 9 V and short-circuit current of 2 μA without any treatment of the SL-TENG, can provide an effective solution to the problem that traditional sensor need battery replacement. In addition, the SL-TENG can generate stable amplitude electrical signals through water droplets, exemplified by the absence of decay in a short-circuit current within 7 days. More importantly, the sensor is equipped with intelligent analytical capabilities, allowing it to assess rainfall based on variables such as amplitude and frequency. Due to its excellent stability and intelligent analysis, this sensor can be used for roof rainwater monitoring, intravenous administration monitoring, and especially in automobile automatic wipers and other fields.

Scalable evaluation methods for autonomous vehicles

Effective intelligent driving test and evaluation methods can improve the development and deployment process of autonomous vehicles (AVs). However, due to the extreme complexity and high dimensionality of driving behavior, how to objectively and efficiently evaluate the multi-dimensional performance of AVs in simulation and real-world environments is a long-standing problem. This paper proposes an objective multi-dimensional comprehensive evaluation (OMDCE) method that divides the intelligent driving test into four modules: test scenarios, scenario complexity model, simulation test platform, and automated evaluation system. The scenario complexity model is proposed to bridge the gap between the test scenarios and the automated evaluation system, thus enabling adaptive scaling of the evaluation scale for test scenarios with different difficulty levels. Besides, based on the existing four evaluation metrics for ego performance, altruism performance evaluation is first proposed to comprehensively portray AVs’ intelligence degree. The OMDCE method was evaluated by testing two different intelligent driving algorithms and conducting real vehicle testing. The experimental results demonstrated that the OMDCE method can effectively evaluate AVs in various scenarios and quantitatively measure their multi-dimensional performance. The proposed method reduces the subjectivity of manual evaluation and speeds up the evaluation. Moreover, it improves the universality and scalability of the evaluation metrics system.

FPGA-Based Real-Time Synchronous Parallel System for Underwater Acoustic Positioning and Navigation

With the development of autonomous underwater vehicles, the research of underwater acoustic positioning and navigation technology is becoming increasingly important. However, most underwater positioning systems use the traditional acoustic response synchronization method, which leads to insufficient positioning real-time performance and refresh rate. To solve this problem, this paper proposes a real-time global synchronization mechanism with high-precision atomic clocks, which can realize one positioning through one-way propagation, so that the positioning efficiency is at least doubled. To further improve the positioning accuracy, an M-sequence-based phase offset modulation method is proposed to facilitate beacon identification. Consequently, a normalized signal matching capture algorithm against Doppler effect is designed, and the positioning solution is fused with the real-time sound ray bending compensation mechanism. Based on the above content, we developed an FPGA-based real-time synchronous parallel underwater positioning and navigation (RSPUPN) system, which uses FPGA+MCU architecture to accelerate signal processing operations in a low-power and parallel way. The test results in the South China Sea show that the operating range of the system can reach 8 km, the positioning refresh rate can reach 0.2 Hz, and the relative average position error is 0.25%, confirming the effectiveness of the designed system in industrial applications.

Multi-camera multi-object tracking on the move via single-stage global association approach

The development of autonomous vehicles generates a tremendous demand for a low-cost solution based on a complete set of camera sensors to perceive the environment around the car. Towards this solution, it is essential for object detection and tracking to address new challenges in multi-camera settings. To address these challenges, this work introduces novel Single-Stage Global Association Tracking approaches to associate one or more detections from multi-cameras with tracked objects. These approaches aim to solve fragment-tracking issues caused by inconsistent 3D object detection. Moreover, our models also improve the detection accuracy of the standard vision-based 3D object detectors in the nuScenes detection challenge. The extensive experimental results on the nuScenes dataset demonstrate the benefits of the proposed method, which outperforms prior vision-based tracking methods in multi-camera settings.

Identifying Key Issues in Integration of Autonomous Ships in Container Ports: A Machine-Learning-Based Systematic Literature Review

Background: Autonomous ships have the potential to increase operational efficiency and reduce carbon footprints through technology and innovation. However, there is no comprehensive literature review of all the different types of papers related to autonomous ships, especially with regard to their integration with ports. This paper takes a systematic review approach to extract and summarize the main topics related to autonomous ships in the fields of container shipping and port management. Methods: A machine learning method is used to extract the main topics from more than 2000 journal publications indexed in WoS and Scopus. Results: The research findings highlight key issues related to technology, cybersecurity, data governance, regulations, and legal frameworks, providing a different perspective compared to human manual reviews of papers. Conclusions: Our search results confirm several recommendations. First, from a technological perspective, it is advised to increase support for the research and development of autonomous underwater vehicles and unmanned aerial vehicles, establish safety standards, mandate testing of wave model evaluation systems, and promote international standardization. Second, from a cyber–physical systems perspective, efforts should be made to strengthen logistics and supply chains for autonomous ships, establish data governance protocols, enforce strict control over IoT device data, and strengthen cybersecurity measures. Third, from an environmental perspective, measures should be implemented to address the environmental impact of autonomous ships. This can be achieved by promoting international agreements from a global societal standpoint and clarifying the legal framework regarding liability in the event of accidents.

Autonomous Marine Vehicle Operations

The world has witnessed the rapid development of autonomous marine vehicles,such as surface vehicles and underwater vehicles, which have created fruitful innovative approaches to previously unsolvable problems in marine and ocean engineering [...]

Delving into the Progress and Implications of Artificial Intelligence

Artificial Intelligence (AI) and Machine Learning (ML) represent burgeoning fields with the potential to transform numerous facets of society and industry. AI encompasses computer systems and algorithms capable of executing tasks typically necessitating human intelligence, such as learning, problem-solving, and decision-making. Conversely, ML entails the creation of algorithms facilitating computers to glean insights from data and refine their performance over time, sans explicit programming. This research delves into the fundamental principles and practical applications of AI and ML, encompassing domains like natural language processing, image and speech recognition, and the development of autonomous vehicles. Furthermore, we scrutinize the potential advantages and apprehensions linked with these technologies, including the prospect of job displacement and the susceptibility to misuse. Finally, we underscore the significance of ethical considerations and conscientious development practices to ensure the realization of AI and ML benefits while mitigating adverse repercussions.

Exploring the Advancements and Ramifications of Artificial Intelligence

Artificial Intelligence (AI) and Machine Learning (ML) represent burgeoning fields with the potential to transform numerous facets of society and industry. AI encompasses computer systems and algorithms capable of executing tasks typically necessitating human intelligence, such as learning, problem-solving, and decision-making. Conversely, ML entails the creation of algorithms facilitating computers to glean insights from data and refine their performance over time, sans explicit programming. This research delves into the fundamental principles and practical applications of AI and ML, encompassing domains like natural language processing, image and speech recognition, and the development of autonomous vehicles. Furthermore, we scrutinize the potential advantages and apprehensions linked with these technologies, including the prospect of job displacement and the susceptibility to misuse. Finally, we underscore the significance of ethical considerations and conscientious development practices to ensure the realization of AI and ML benefits while mitigating adverse repercussions.

A study on the key technologies and existing challenges in the development of autonomous vehicles

As an emerging means of transportation, autonomous driving has great development potential and application prospects. This paper investigates relevant issues of autonomous vehicles, aiming to explore the technical principle and implementation method of autonomous driving and evaluate its performance and effect in road traffic. By analyzing the existing related research results and practical experience, this paper conducts an in-depth study on key technologies of autonomous vehicles, including sensor and sensing systems as well as decision-making and control systems. Besides, the paper also summarizes the ethical and safety problems in the course of autonomous driving development and puts forward a deep learning-based control strategy for autonomous driving. This strategy has high accuracy and stability in different scenarios and can effectively improve the safety and performance of autonomous vehicles. To sum up, this paper carries out in-depth research on autonomous vehicles and tries to propose innovative solutions in key technical fields. This study provides reliable theoretical and technical support for the development and application of autonomous vehicles and promotes progress and innovation in the field of transportation.

Analysis of dynamic determinants of vehicles involved in crash affecting severity based on in-depth crash data

Objective The dynamic characteristics of vehicles involved in crashes may be an important factor affecting the crash severity. This study investigates the relationship between the dynamic characteristics of vehicles involved in crashes in the five seconds before the occurrence and the crash severity. The findings aim to offer insights for preventing severe crashes and advancing autonomous vehicle technology. Methods This study aims to investigate the impact of dynamic features, such as speed, acceleration, and relative distance of vehicles involved in the crash in the five seconds before the crash, on the crash severity. Five hundred ninety-six crash samples from the China In-depth Mobility Safety Study-Traffic Accident database were selected for crash reconstruction. A random parameters logit model was used to extract and analyze the effect of dynamic features of the vehicles involved in the crash on the crash severity. Results The random parameters logit model demonstrated a satisfactory fit. Analysis of the parameter estimation results of the model showed that the variables of speed, acceleration, and relative distance between vehicles involved in the crash at some time points during the five seconds before the crash significantly affected the crash severity. Notably, the coefficient of variation of relative distance over 5 s emerged as the most influential positive determinant of the crash severity. Conclusions Certain dynamic characteristics of vehicles involved in a crash in the five seconds before a crash significantly impact the crash severity. The study’s findings can serve as a reference for preventing severe crashes and advancing the development of autonomous vehicles.

Image Filtering Techniques for Object Recognition in Autonomous Vehicles

The deployment of autonomous vehicles has the potential to significantly lessen the variety of current harmful externalities, (such as accidents, traffic congestion, security, and environmental degradation), making autonomous vehicles an emerging topic of research. In this paper, a literature review of autonomous vehicle development has been conducted with a notable finding that autonomous vehicles will inevitably become an indispensable future greener solution. Subsequently, 5 different deep learning models, YOLOv5s, EfficientNet-B7, Xception, MobilenetV3, and InceptionV4, have been built and analyzed for 2-D object recognition in the navigation system. While testing on the BDD100K dataset, YOLOv5s and EfficientNet-B7 appear to be the two best models. Finally, this study has proposed Hessian, Laplacian, and Hessian-based Ridge Detection filtering techniques to optimize the performance of those 2 models. The results demonstrate that these filters could increase the mean average precision by up to 11.81%, and reduce detection time by up to 43.98% when applied to YOLOv5s and EfficientNet-B7 models. Overall, all the experiment results are promising and could be extended to other domains for semantic understanding of the environment. Additionally, various filtering algorithms for multiple object detection and classification could be applied to other areas. Different recommendations and future work have been clearly defined in this study.

A Bayesian Inference-based approach for extracting driving data with implicit intention

PurposeThe growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs include data on vehicles with and without intended driving behavior changes, they do not explicitly demonstrate a type of data on vehicles that intend to change their driving behavior but do not execute the behaviors because of safety, efficiency, or other factors. This missing data is essential for autonomous driving decisions. This study aims to extract the driving data with implicit intentions to support the development of decision-making models.Design/methodology/approachAccording to Bayesian inference, drivers who have the same intended changes likely share similar influencing factors and states. Building on this principle, this study proposes an approach to extract data on vehicles that intended to execute specific behaviors but failed to do so. This is achieved by computing driving similarities between the candidate vehicles and benchmark vehicles with incorporation of the standard similarity metrics, which takes into account information on the surrounding vehicles' location topology and individual vehicle motion states. By doing so, the method enables a more comprehensive analysis of driving behavior and intention.FindingsThe proposed method is verified on the Next Generation SIMulation dataset (NGSim), which confirms its ability to reveal similarities between vehicles executing similar behaviors during the decision-making process in nature. The approach is also validated using simulated data, achieving an accuracy of 96.3 per cent in recognizing vehicles with specific driving behavior intentions that are not executed.Originality/valueThis study provides an innovative approach to extract driving data with implicit intentions and offers strong support to develop data-driven decision-making models for autonomous driving. With the support of this approach, the development of autonomous vehicles can capture more real driving experience from human drivers moving towards a safer and more efficient future.

Analyzing Performance of YOLOx for Detecting Vehicles in Bad Weather Conditions.

Recent advancements in computer vision technology, developments in sensors and sensor-collecting approaches, and the use of deep and transfer learning approaches have excelled in the development of autonomous vehicles. On-road vehicle detection has become a task of significant importance, especially due to exponentially increasing research on autonomous vehicles during the past few years. With high-end computing resources, a large number of deep learning models have been trained and tested for on-road vehicle detection recently. Vehicle detection may become a challenging process especially due to varying light and weather conditions like night, snow, sand, rain, foggy conditions, etc. In addition, vehicle detection should be fast enough to work in real time. This study investigates the use of the recent YOLO version, YOLOx, to detect vehicles in bad weather conditions including rain, fog, snow, and sandstorms. The model is tested on the publicly available benchmark dataset DAWN containing images containing four bad weather conditions, different illuminations, background, and number of vehicles in a frame. The efficacy of the model is evaluated in terms of precision, recall, and mAP. The results exhibit the better performance of YOLOx-s over YOLOx-m and YOLOx-l variants. YOLOx-s has 0.8983 and 0.8656 mAP for snow and sandstorms, respectively, while its mAP for rain and fog is 0.9509 and 0.9524, respectively. The performance of models is better for snow and foggy weather than rainy weather sandstorms. Further experiments indicate that enhancing image quality using multiscale retinex improves YOLOx performance.

CMSV: a New Cloud Multi-Agents for Self-Driving Vehicles as a Services

The development of autonomous vehicles has changed the way of transmitting goods to users, with the potential to improve road safety and efficiency. One of the effective issues of self-driving vehicles is path planning issue. In this paper, a new system is proposed for addressing the potential of using multi-agent systems (MAS) to address path planning problems in autonomous vehicles. The system is called Cloud Multi-Agents for Self-Driving Vehicles as A Services (CMSV). It is used to solve the dependent delivery problem, where the vehicle transmits the goods/deliveries, that are dependent on each other, to specific locations in ordering or with specific directions. The new system consists of three phases: (1) converting the problem into TSP sub problems according to deliveries dependency, (2) applying GDC to select the good solutions (3) applying multi-agent system to select the optimal solution. The proposed system is compared with systems that use a genetic algorithm, Ant Colony algorithm, Particle Swarm Optimization and The Harris hawk optimization (HHO) algorithm. From the findings, we find that the proposed system is more efficient than the other systems.

Fast and Modular Autonomy Software for Autonomous Racing Vehicles

Autonomous motorsports aim to replicate the human race-car driver with software and sensors. As in traditional motorsports, Autonomous Racing Vehicles (ARVs) are pushed to their handling limits in multiagent scenarios at extremely high ( 150 mph) speeds. This Operational Design Domain (ODD) presents unique challenges across the autonomy stack. The Indy Autonomous Challenge (IAC) is an international competition aiming to advance autonomous vehicle development through ARV competitions. While far from challenging what a human race-car driver can do, the IAC is pushing the state of the art by facilitating full-sized ARV competitions. This paper details the MIT-Pitt-RW Team’s approach to autonomous racing in the IAC. In this work, we present our modular and fast approach to agent detection, motion planning and controls to create an autonomy stack. We also provide analysis of the performance of the software stack in single and multiagent scenarios for rapid deployment in a fast-paced competition environment. We also cover what did and did not work when deployed on a physical system (the Dallara AV-21 platform) and potential improvements to address these shortcomings. Finally, we convey lessons learned and discuss limitations and future directions for improvement.