Development Of Autonomous Vehicles Research Articles

Pedestrian detection method based on improved YOLOv5

With the development of autonomous vehicles and intelligent transportation, more accurate detection of pedestrians. However, pedestrian detection suffers from occlusion and small target. First, the HorNet to improve the higher-order spatial interaction capability of the model, expand the effective sensory field, and enhance the feature extraction of pedestrians. Then, ODConv to gain the variability of each dimension and capture rich information. Finally, a layer to increase the accuracy of detecting pedestrians at small scales. We optimize the regression prediction of the anchor using the Efficient IOU Loss (EIOU) function. Experimental data show that the mean average precision (mAP) of the HOD-YOLOv5 model achieves 83.5%, compared to YOLOv5, which is 4.4% higher than original YOLOv5, and the recognition speed of the HOD-YOLOV5 reaches 106.7 frames per second (FPS). This demonstrates that the proposed model could realize real-time pedestrian detection at a relatively small cost, which satisfies the requirements of uncrewed and intelligent transportation.

Review of Electrical and Electronic Architectures for Autonomous Vehicles: Topologies, Networking and Simulators

With the rapid development of autonomous vehicles, more and more functions and computing requirements have led to the continuous centralization in the topology of electrical and electronic (E/E) architectures. While certain Tier1 suppliers, such as BOSCH, have previously proposed a serial roadmap for E/E architecture development, implemented since 2015 with significant contributions to the automotive industry, lingering misconceptions and queries persist in actual engineering processes. Notably, there are concerns regarding the perspective of zone-oriented E/E architectures, characterized by zonal concentration, as successors to domain-oriented E/E architectures, known for functional concentration. Addressing these misconceptions and queries, this study introduces a novel parallel roadmap for E/E architecture development, concurrently evaluating domain-oriented and zone-oriented schemes. Furthermore, the study explores hybrid E/E architectures, amalgamating features from both paradigms. To align with the evolution of E/E architectures, networking technologies must adapt correspondingly. The networking mechanisms pivotal in E/E architecture design are comprehensively discussed. Additionally, the study delves into modeling and verification tools pertinent to E/E architecture topologies. In conclusion, the paper outlines existing challenges and unresolved queries in this domain.

Problems of information support of the logistics process

The article discusses modern logistics processes and related problems. The main emphasis is placed on the ongoing changes associated with computerization and informatization penetrating into all spheres of our life. Having identified information not only as a resource of economic activity, but also as a potentially problematic system, nevertheless, the need to attract information resources to management within the framework of the logistics process has been proven. Problems of information support for the transportation process are considered in the aspects of information about the condition of the cargo and its location; tracking and monitoring of vehicles in the format of basic programs for tracking the movement of goods, namely: warehouse management systems; transport tracking and monitoring systems; programs for managing transport logistics processes. The article also examines the current domestic experience and practice of using information technologies, indicates the obstacles to their implementation, and develops directions for computerization in the field of logistics. In conclusion, the main promising directions for the development of information support for the transport process are the use of the capabilities of artificial intelligence and machine learning; the development of digital platforms that will allow logistics companies to improve the interaction of all participants in the transport market and the development of autonomous vehicles.

AI to V2X Privacy and Security Issues in Autonomous Vehicles: Survey

Artificial Intelligence (AI) is transforming all of the technologies we use every day. More than ever, we are very near to the objective of vehicle autonomy, which has long been desired. Large automakers are also spending billions of dollars on the development of autonomous vehicles (AVs). Among the advantages of this new technology are the possibility for increased passenger safety, less congested roads, reduced traffic, optimized traffic, reduced fuel consumption, less pollution, and improved travel experiences. However, there are also new security and privacy problems associated with this paradigm change. Previously simple mechanical devices, vehicles are today computerized, networked, and intelligent. They gather vast amounts of data, which must be shielded from intrusions. In this paper, we examine privacy issues and security hurdles in AVs. We investigate several attacks using a layer-by-layer methodology. It summarizes the contributions of these research works and categorizes them based on application domains. It also identifies open problems and research challenges that need to be addressed to fully realize the potential of AI in advancing V2X systems. Our intention is to provide insights into the unresolved research issues surrounding AVs and to suggest future lines of inquiry.

Artificial Intelligence in the 21st Century: Opportunities, Risks and Ethical Imperatives

Artificial Intelligence in the 21st Century: Opportunities, Risks and Ethical Imperatives

Object Detection for Self-Driving Car in Complex Traffic Scenarios

The application of convolutional neural networks (CNNs) in particular has greatly enhanced the object detection capabilities of self-driving cars, because of recent advancements in artificial intelligence (AI). However, striking a balance in vehicular settings between high precision and fast processing continues to be a persistent challenge. Developing nations such as India, possessing the second-largest global population, introduce unique intricacies to road scenarios. Numerous challenges arise on Indian roads, such as unique vehicle kinds and a variety of traffic patterns, such as auto-rickshaws, which are only seen in India. This study presents the outcomes of evaluating the YOLOv8 models, which have demonstrated superior performance in Indian traffic conditions when compared to other existing YOLO models. The examination utilized the dataset, compiled from data collected in the cities of Bangalore and Hyderabad, as well as their surrounding areas. The investigation's findings demonstrate how well the YOLOv8 models work to address the unique problems that Indian road conditions present. This study advances the development of autonomous vehicles designed for intricate traffic situations such as those found on Indian Roads.

Dual-Cockpit Human and Hardware-In-the-Loop Test Bench for Autonomous Vehicle Development

Dual-Cockpit Human and Hardware-In-the-Loop Test Bench for Autonomous Vehicle Development

Navigating Nonlinear Analysis and Artificial Intelligence Frontiers for Revolutionary Technology Solutions

The intersection of nonlinear analysis and artificial intelligence (AI) presents a promising frontier in the rapidly changing field of technology, with the potential to yield ground-breaking solutions in a multitude of fields. In order to investigate how different disciplines may work together to transform technological progress, this study explores the synthesis of these fields. Deeper comprehension of complicated patterns and behaviours is made possible by the intersection of AI and nonlinear analysis, which can both model complex systems and events. This combination has the potential to go beyond conventional linear thinking and open doors to solve complex problems. Through the utilisation of AI's learning skills to augment nonlinear models, a novel approach to problem-solving is revealed. The purpose of the paper is to examine the applications of this synergy in various disciplines. The combination of nonlinear analysis and AI holds the potential to transform a number of industries, from banking and predictive analytics to biological systems research and autonomous vehicle development. This investigation also includes the societal and ethical ramifications of using such cutting-edge technologies. It aims to provide a thorough understanding of the factors necessary for the ethical integration of these advances into our lives by delving into concerns of privacy, prejudice, and responsible use. This study tries to give academics, engineers, and innovators a full road map by looking at recent advances and possible future possibilities. It seeks to stimulate novel research directions, interdisciplinary teamwork, and the advancement of ground-breaking technical innovations that have the potential to fundamentally alter our understanding of and interactions with the environment around us.

MetaLiDAR: Automated metamorphic testing of LiDAR‐based autonomous driving systems

AbstractRecent advances in artificial intelligence technology and perception components have promoted the rapid development of autonomous vehicles. However, as safety‐critical software, autonomous driving systems often make wrong judgments, seriously threatening human and property safety. LiDAR is one of the most critical sensors in autonomous vehicles, capable of accurately perceiving the three‐dimensional information of the environment. Nevertheless, the high cost of manually collecting and labeling point cloud data leads to a dearth of testing methods for LiDAR‐based perception modules. To bridge the critical gap, we introduce MetaLiDAR, a novel automated metamorphic testing methodology for LiDAR‐based autonomous driving systems. First, we propose three object‐level metamorphic relations for the domain characteristics of autonomous driving systems. Next, we design three transformation modules so that MetaLiDAR can generate natural‐looking follow‐up point clouds. Finally, we define corresponding evaluation metrics based on metamorphic relations. MetaLiDAR automatically determines whether source and follow‐up test cases meet the metamorphic relations based on the evaluation metrics. Our empirical research on five state‐of‐the‐art LiDAR‐based object detection models shows that MetaLiDAR can not only generate natural‐looking test point clouds to detect 181,547 inconsistent behaviors of different models but also significantly enhance the robustness of models by retraining with synthetic point clouds.

Artificial Neural Network-Powered, Driverless Vehicle Concept Development

Autonomous cars are now possible due to significant advances in robotics and intelligent control systems. Before these vehicles can safely operate in traffic and other hostile environments, there are many navigation, vision, and control issues. We want techniques that are both cost-effective and efficient, so that the field of research and academia may fully embrace self-driving cars. Within this scenario, we need something that can convert people to autonomous automobiles and include existing vehicles so that academics and explorers can access them. This study proposes a flexible mechanical layout that can be assembled in a short time and installed in most modern automobiles; it can also be used as a stepping stone in the development of autonomous vehicles. Using various actuators, conventional automobiles can be converted into autonomous vehicles. In the context of motor vehicle automation, motors are often used as actuators. In addition to motors, a pneumatic system was developed to automate the predetermined steps. An autonomous vehicle's mechanical arrangement is crucial, and it must be regularly updated and built to be robust in the face of dynamic conditions. We re-implemented two additional convolutional neural networks in an effort to conduct an objective test of their proposed network and compare our system's structure, technical complexity, and performance test during autonomous driving with theirs. This predicted network is around 250 times larger than the Alex Net network and four times larger than Pilot Net after training. Although the complexity and measurement of the publication's system are lower than other models that contribute lower latency and greater speed throughout inference, the operation was claimed by our system, which achieved autonomous driving with an equivalent efficacy as that achieved with two other models. The projected deep neural system reduces the need to infer ultra-fast computational hardware. This is important for cost efficiency, scale, and cost.

EVALUATING NAVIGATION PERFORMANCE OF ELASTICALLY CONSTRUCTED HD MAP WITH MULTI-SENSOR FUSION ENGINE SYSTEM

Abstract. In response to the rapid development of autonomous vehicles and the increasing demand for HD maps, the conventional mapping processes following HD maps guidelines require significant manpower and time resources. Therefore, we propose flexible procedures and methods for HD maps creation, aiming to reduce cost expenditure by employing diverse source of ground control point, sensor data collection, and mapping algorithm. This approach accelerates the production speed and capability of HD maps. In this study, we select Taiwan's National Highway No. 8 as the trial field for the elastic HD map construction method, and equipped with autonomous vehicle-grade GNSS, IMU, and LiDAR systems.We align the constructed-map data to the global coordinate system, in order to realize the concept of control point cloud map. To assess the assistance and correction capabilities of HD maps in autonomous vehicle navigation systems, we conduct accuracy evaluation through both direct and indirect methods, and analyse the strengths and weaknesses of each approach. The analysis result demonstrates that the elastic method-built HD maps not only meet the mapping accuracy requirements specified in the HD maps verification and validation guidelines, but also assist autonomous vehicles in realizing positioning, navigation, and timing with “where in lane” level (0.5 meter) accuracy.

Adaptive Truck Platooning with Drones: A Decentralized Approach for Highway Monitoring

The increasing demand for efficient and safe transportation systems has led to the development of autonomous vehicles and vehicle platooning. Truck platooning, in particular, offers numerous benefits, such as reduced fuel consumption, enhanced traffic flow, and increased safety. In this paper, we present a drone-based decentralized framework for truck platooning in highway monitoring scenarios. Our approach employs multiple drones, which communicate with the trucks and make real-time decisions on whether to form a platoon or not, leveraging Model Predictive Control (MPC) and Unscented Kalman Filter (UKF) for drone formation control. The proposed framework integrates a simple truck model in the existing drone-based simulation, addressing the truck dynamics and constraints for practical applicability. Simulation results demonstrate the effectiveness of our approach in maintaining the desired platoon formations while ensuring collision avoidance and adhering to the vehicle constraints. This innovative drone-based truck platooning system has the potential to significantly improve highway monitoring efficiency, traffic management, and safety. Our drone-based truck platooning system is primarily designed for implementation in highway monitoring and management scenarios, where its enhanced communication and real-time decision-making capabilities can significantly contribute to traffic efficiency and safety. Future work may focus on field trials to validate the system in real-world conditions and further refine the algorithms based on practical feedback and evolving vehicular technologies.

From simulation to reality: development of a small-scale autonomous electric vehicle for educational and research purposes

Robotics is a constantly evolving field, making it possible to perform various tasks that help people every day. Among these tasks, robot safe navigation through different environments is one of the challenges of mobile robotics. This work presents the development of a small autonomous vehicle designed for navigation using predefined coordinates. The chosen navigation strategy uses virtual vector fields that exploit an interaction of virtual attractive and repulsive forces. The proposed strategy has been validated through simulations and in real scenarios. Furthermore, this work presents the development of the physical prototype of the robot, emphasizing the practical and educational aspects of using autonomous robots, providing valuable insights and practical learning experiences in robotics and navigation.

Social Interaction‐Aware Dynamical Models and Decision‐Making for Autonomous Vehicles

Interaction‐aware autonomous driving (IAAD) is a rapidly growing field of research that focuses on the development of autonomous vehicles (AVs) that are capable of interacting safely and efficiently with human road users. This is a challenging task, as it requires the AV to be able to understand and predict the behaviour of human road users. In this literature review, the current state of IAAD research is surveyed. Commencing with an examination of terminology, attention is drawn to challenges and existing models employed for modeling the behaviour of drivers and pedestrians. Next, a comprehensive review is conducted on various techniques proposed for interaction modeling, encompassing cognitive methods, machine‐learning approaches, and game‐theoretic methods. The conclusion is reached through a discussion of potential advantages and risks associated with IAAD, along with the illumination of pivotal research inquiries necessitating future exploration.

Psychological factors shaping public acceptance of the adoption of autonomous vehicles in Indonesia

IntroductionThe rapid development of autonomous vehicles (AVs) has the potential to revolutionize transportation systems and address societal challenges. However, understanding the psychological factors influencing public acceptance and readiness for AV adoption is crucial for successful implementation. This study provides a comprehensive analysis of these psychological factors, specifically focusing on the context of health issues in Indonesia. MethodologyA mixed-methods approach analyzed quantitative data from 500 respondents to a questionnaire addressing key perceptions of AVs using regression analysis, coupled with 30 qualitative interviews. ResultsThe results revealed that trust in AV technology mediates the relationship between control perception, perceived safety, human-machine interaction, educational status, cultural factors, and public acceptance of AV adoption. Cultural factors were also identified as moderators influencing this relationship. Qualitative analysis of interview data further highlighted the significance of trust, reliability, and safety concerns in shaping public acceptance. Participants emphasized the need for effective human-machine interaction and educational awareness to enhance understanding and acceptance. DiscussionThis research contributes to the existing literature by explicitly examining psychological factors within the context of health issues in Indonesia, providing valuable insights for policymakers aiming to promote AV adoption. Ultimately, understanding and addressing these psychological factors will pave the way for a successful and sustainable AV future.

Autonomous Vehicles for Healthcare Applications: A Review on Mobile Robotic Systems and Drones in Hospital and Clinical Environments

The development of autonomous vehicles, both ground and airborne, for hospitals and clinical settings is an extremely interesting topic that has developed rapidly in recent years. Given their significant potential to improve operational efficiency and safety protocols, these devices are gradually gaining an important place in the healthcare industry. This is true for both in-hospital and out-of-hospital functions. The integration of autonomous vehicles into these environments will greatly increase operational efficiency and enrich the experience for both medical staff and patients. This document provides an updated and comprehensive overview of the current state-of-the-art in the development of autonomous vehicles for the healthcare sector. Particular emphasis is placed on design, functionality, and level of autonomy. The review is organized on two levels: a prospective review highlights the main trends in the design and application of autonomous vehicles, and an analytical review performs an in-depth analysis of the main aspects of the technical solutions developed and implemented in the scientific research reviewed. The results are presented in a schematic approach.

Driving Sustainability: Carbon Footprint, 3D Printing, and Legislation concerning Electric and Autonomous Vehicles.

In recent years, there has been a remarkable development in the technology and legislation related to electric and autonomous vehicles (i.e., EVs/AVs). This technological advancement requires the deployment of the most up-to-date supporting infrastructure to achieve safe operation. Further infrastructure is needed for Level 5 vehicles, namely the introduction of super-fast wireless 5G technology. To achieve harmony between the rapid technological advancement of EVs/AVs and environmental preservation, enacting legislation related to their sustainable use is vital. Thus, this manuscript provides a review of the technological development of EVs/AVs, with a special focus on carbon footprints and the implementation of additive manufacturing using recycled materials. While EVs have a 12.13% increased carbon footprint compared to conventional vehicles, AVs with basic and advanced intelligence features have an increased carbon footprint of 41.43% and 99.65%, respectively. This article emphasizes that the integration of 3D-printed components has the potential to offset this impact with a substantial 60% reduction. As a result, custom-made solutions involving 3D printing are explored, leading to greater speed, customization, and cost-effectiveness for EVs/AVs. This article also lists the advantages and disadvantages of the existing legislation in Spain, the United Kingdom, and the western Balkans, demonstrating various approaches to promoting electric mobility and the development of autonomous vehicles. In Spain, initiatives like the MOVES program incentivize EV adoption, while the UK focuses on expanding the EV market and addressing concerns about EVs' quiet operation. In the western Balkans, the adoption of legislation lags behind, with limited incentives and infrastructure for EVs. To boost sales, legal mechanisms are necessary to reduce costs and improve accessibility, in addition to offering subsidies for the purchase of EVs. To this end, an analysis of the incentive measures proposed for the development and use of renewable power sources for the supply of energy for EVs/AVs is presented.

Scalable laser-based underwater wireless optical communication solution between autonomous underwater vehicle fleets.

The development of multiple autonomous underwater vehicles (AUVs) has revolutionized the traditional reliance on a single, costly AUV for conducting underwater surveys. This shift has garnered increasing interest among marine researchers. Communication between AUV fleets is an urgent concern due to the data rate limitation of underwater acoustic communication. Laser-based underwater wireless optical communication (UWOC) is a potential solution once the link-establishing requirement between AUVs can be met. Due to the limited coverage area of the laser beam, the previous pointing, acquisition, and tracking (PAT) method is to quickly adjust the beam direction and search for the target according to the set scanning path. In response to these challenges, we propose a scalable laser-based link establishment method that combines the maneuvering of the AUV, the acoustic positioning, and the control of the optical system. Our proposed approach has consistently outperformed the existing PAT method in simulated environments, effectively establishing laser links. Importantly, we have successfully implemented our approach in machine experiments, confirming its practical applicability.

Video Oyunu Ortamında Otonom Sürüş İçin Şerit Tespiti

To ensure comfortable and safe driving, the automotive industry has accelerated the development of autonomous vehicles in recent years. In the design of autonomous vehicles, challenging problems such as lane detection need to be solved. Convolutional neural networks, which show superior performance in many fields, have also been used in the lane detection problem. The datasets required to train CNN models are too large to be collected and labeled by manual effort. In this study, a method is proposed to automatically collect a labeled data set from the video game environment to be used in the detection of highway lanes. Different CNN models such as ResNet50, VGG16, Xception, and InceptionV3 networks are trained using the Transfer Learning method with 745,823 collected images. The images captured by the front vehicle camera are used as input, the coordinates of the points in the left and right lane and the center of the lane in the 2D plane in front of the vehicle and the angle of the vehicle are used as outputs. The performances of these models are tested and compared on the images collected from a road not used in the training set. According to the performance comparisons, ResNet50 performs best.

SLAV-Sim: A Framework for Self-Learning Autonomous Vehicle Simulation.

With the advent of autonomous vehicles, sensors and algorithm testing have become crucial parts of the autonomous vehicle development cycle. Having access to real-world sensors and vehicles is a dream for researchers and small-scale original equipment manufacturers (OEMs) due to the software and hardware development life-cycle duration and high costs. Therefore, simulator-based virtual testing has gained traction over the years as the preferred testing method due to its low cost, efficiency, and effectiveness in executing a wide range of testing scenarios. Companies like ANSYS and NVIDIA have come up with robust simulators, and open-source simulators such as CARLA have also populated the market. However, there is a lack of lightweight and simple simulators catering to specific test cases. In this paper, we introduce the SLAV-Sim, a lightweight simulator that specifically trains the behaviour of a self-learning autonomous vehicle. This simulator has been created using the Unity engine and provides an end-to-end virtual testing framework for different reinforcement learning (RL) algorithms in a variety of scenarios using camera sensors and raycasts.