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The Intelligent Transport System (ITS) is very prominent due to its connection with the Internet of Things (IoT), which enhances passenger security and comfort. The Vehicular Ad-hoc Network (VANET) is a component of ITS. It manages the techniques used for routing and privacy in autonomous cars. The increasing number of autonomous cars has exceeded the capacity of current wireless networks for transmission. It is expected that the proposed 6G wireless network can meet VANET criteria. Very little research has investigated the privacy concerns of VANETs in 6G networking connections. This work presents a method for dealing with authentic and privacy concerns for automobiles in VANETs. Our solution strengthens the vehicle's connectivity system by detecting malicious attacks like replay attacks, DoS attacks, and impersonification attacks. The proposed system uses batch authentication to reduce traffic and workload on the network. The proposed system employs both ID-based authentication and deep learning methods. Where the role of ID-based authentication is to check for access in the network, deep learning takes on the role of identifying all the malicious packets in the system. Our result also shows that the proposed system can identify malicious packets with an accuracy of 98.25% and works successfully in 6G networking communication. [ABSTRACT FROM AUTHOR]

Ocean data exhibits great heterogeneity from variances in measuring methods, formats, and quality, making it extremely complicated and diverse due to a variety of data kinds, sources, and study elements. A few examples of data sources are satellites, buoys, ships, self-driving cars, and distant systems. The processing of data is made more challenging by the significant regional and temporal variations in oceanic characteristics including temperature, salinity, and currents. This work presents an interactive tool for multivariate ocean parameter visualisation, specifically overlays, based on Python. In ocean data visualisation, overlays are extra visual layers or data points that are layered to improve comprehension over a basic map. Based on the available data and the visualisation goals, these overlays are chosen and blended. Users can customise overlays with this tool, which also supports formatting, 2D and 3D visualisation, and data preparation. In order to reduce artefacts, it uses kriging interpolation for 3D visualisation and a modified version of the ray casting algorithm for representing octree data. By integrating overlays like as bathymetry, currents, temperature, and marine life, users can produce visually appealing and comprehensive depictions of ocean data. This method provides a thorough grasp of intricate marine processes by making it easier to see patterns, trends, and abnormalities in the data. [ABSTRACT FROM AUTHOR]

To validate active human body models for investigating occupant safety in autonomous cars, it is crucial to comprehend the responses of vehicle occupants during evasive maneuvers. This study sought to quantify the behavior of midsize male and small female passenger seat occupants in both upright and reclined postures during three types of vehicle maneuvers. Volunteer tests were conducted using a minivan, where vehicle kinematics were measured with a DGPS sensor and occupant kinematics were captured with a stereo-vision motion capture system. Seatbelt loads, belt pull-out, and footrest reaction forces were also documented. The interior of the vehicle was 3D-scanned for modeling purposes. Results indicated that seatback angles significantly affected occupant kinematics, with small female volunteers displaying reduced head and torso movements, except during emergency braking with a upright posture seatback. Lane-change maneuvers revealed that maximum lateral head excursions varied depending on the maneuver's direction. The study concluded that seatback angles were crucial in determining the extent of occupant movement, with notable variations in head and torso excursions observed. The collected data assist in understanding occupant behavior during evasive maneuvers and contribute to the validation of human body models, offering essential insights for enhancing safety systems in autonomous vehicles. [ABSTRACT FROM AUTHOR]

Estimating the state of a system by fusing sensor data is a major prerequisite in many applications. When the state is time-variant, derivatives of the Kalman filter are a popular choice for solving that task. Two variants are the square-root unscented Kalman filter (SRUKF) and the square-root cubature Kalman filter (SCKF). In contrast to the unscented Kalman filter (UKF) and the cubature Kalman filter (CKF), they do not operate on the covariance matrix but on its square root. In this work, we modify the SRUKF and the SCKF for use on manifolds. This is particularly relevant for many state estimation problems when, for example, an orientation is part of a state or a measurement. In contrast to other approaches, our solution is both generic and mathematically coherent. It has the same theoretical complexity as the UKF and CKF on manifolds, but we show that the practical implementation can be faster. Furthermore, it gains the improved numerical properties of the classical SRUKF and SCKF. We compare the SRUKF and the SCKF on manifolds to the UKF and the CKF on manifolds, using the example of odometry estimation for an autonomous car. It is demonstrated that all algorithms have the same localization performance, but our SRUKF and SCKF have lower computational demands. [ABSTRACT FROM AUTHOR]

Introduction: When it comes to interpreting visual input, intelligent systems make use of contextual scene learning, which significantly improves both resilience and context awareness. The management of enormous amounts of data is a driving force behind the growing interest in computational frameworks, particularly in the context of autonomous cars. Method: The purpose of this study is to introduce a novel approach known as Deep Fused Networks (DFN), which improves contextual scene comprehension by merging multi-object detection and semantic analysis. Results: To enhance accuracy and comprehension in complex situations, DFN makes use of a combination of deep learning and fusion techniques. With a minimum gain of 6.4% in accuracy for the SUN-RGB-D dataset and 3.6% for the NYU-Dv2 dataset. Discussion: Findings demonstrate considerable enhancements in object detection and semantic analysis when compared to the methodologies that are currently being utilized. [ABSTRACT FROM AUTHOR]

As autonomous vehicles grow more common, maintaining their cyber security becomes increasingly important. The CAN (Controller Area Network) bus, a critical communication network in self-driving cars, is susceptible to cyber-attacks that can jeopardize vehicle safety and performance. In this paper, we offer a novel hybrid approach, DeepXG, that combines XGBoost and deep learning (DL) approaches to detect intrusions in the CAN bus. Our model takes advantage of both algorithms' strengths to extract critical characteristics and learn complicated patterns for accurate and resilient intrusion detection. We conducted comprehensive studies to evaluate DeepXG's performance using a genuine CAN traffic dataset from a CAV's OBD-2 port. The proposed method outperformed many intrusion detection methods, achieving an amazing accuracy of 99.90%. The XGBoost feature relevance score enables effective feature selection while reducing computing complexity and boosting generalization. Our findings show that DeepXG helps improve cyber security in autonomous vehicles. The hybrid model's ability to effectively detect and classify network intrusions makes it a potential approach for safeguarding the CAN bus and ensuring autonomous vehicle safety. [ABSTRACT FROM AUTHOR]

Quadrupedal robots are confronted with the intricate challenge of navigating dynamic environments fraught with diverse and unpredictable scenarios. Effectively identifying and responding to obstacles is paramount for ensuring safe and reliable navigation. This paper introduces a pioneering method for 3D object detection, termed viewpoint feature histograms, which leverages the established paradigm of 2D detection in projection. By translating 2D bounding boxes into 3D object proposals, this approach not only enables the reuse of existing 2D detectors but also significantly increases the performance with less computation required, allowing for real-time detection. Our method is versatile, targeting both bird's eye view objects (e.g., cars) and frontal view objects (e.g., pedestrians), accommodating various types of 2D object detectors. We showcase the efficacy of our approach through the integration of YOLO3D, utilizing LiDAR point clouds on the KITTI dataset, to achieve real-time efficiency aligned with the demands of autonomous vehicle navigation. Our model selection process, tailored to the specific needs of quadrupedal robots, emphasizes considerations such as model complexity, inference speed, and customization flexibility, achieving an accuracy of up to 99.93%. This research represents a significant advancement in enabling quadrupedal robots to navigate complex and dynamic environments with heightened precision and safety. [ABSTRACT FROM AUTHOR]

Self-driving cars are one of the main areas of research today, but it has to be acknowledged that the information from the sensors (the perceptron) is a huge amount of data, which is now unmanageable even when projected onto a single traffic junction. In the case of self-driving, the nodes have to be sequenced and organized according to the planned route. A self-driving car in Hungary would have to be able to interpret more than 70,000 traffic junctions to be able to drive all over the country. Besides the huge amount of data, another problem is the issue of validation and verification. For self-driving cars, this implies a level of complexity using traditional methods that calls into question the economics of the already existing system. Fuzzy Petri nets provide an alternative solution to both problems. They allow us to obtain a model that accurately describes the reliability of a node through its dynamics, which is essential in perception since the more reliable a node is, the smaller the deep learning mesh required. In this paper, we outline the analysis of a traffic node's safety using Petri nets and fuzzy analysis to gain information on the reliability of the node, which is essential for the modeling of self-driving cars, due to the deep learning model of perception. The reliability of the dynamics of the node is determined by using the modified fuzzy Petri net procedure. The need for a fuzzy extension of the Petri net was developed by knowledge of real traffic databases. [ABSTRACT FROM AUTHOR]

This study investigates the impact of human-like machines (HLMs) on the user experience (UX) of young adults during voice interactions between drivers and autonomous vehicles. A mixed-method approach was employed to evaluate three voice agents with varying levels of anthropomorphism: a machine voice without humanized speech strategies (Agent A), a human voice without humanized speech strategies (Agent B), and a human voice with humanized speech strategies (Agent C). A total of 30 participants were invited to interact with the agents in a simulated driving scenario. Quantitative measures were employed to assess intimacy, trust, intention to use, perceived safety, and perceived anthropomorphism based on a 7-point Likert scale, while qualitative interviews were conducted to gain deeper insights. The results demonstrate that increased anthropomorphism enhances perceived anthropomorphism (from 2.77 for Agent A to 5.01 for Agent C) and intimacy (from 2.47 for Agent A to 4.52 for Agent C) but does not significantly affect trust or perceived safety. The intention to use was higher for Agents A and C (4.56 and 4.43, respectively) in comparison to Agent B (3.88). This suggests that there is a complex relationship between voice characteristics and UX dimensions. The findings of this study highlight the importance of balancing emotional engagement and functional efficiency in the design of voice agents for autonomous vehicles. [ABSTRACT FROM AUTHOR]

Car detection on the road through computer vision is crucial for improving safety, as it plays an essential role in spotting nearby vehicles and preventing fatal accidents. Additionally, car detection significantly contributes to the advancement of autonomous vehicles. Previous explorations of car detection using YOLOv5 have revealed weaknesses regarding its resulting mean average precision (mAP). This scenario led to the development of a more advanced version of you only look once (YOLO), namely YOLOv8. Consequently, this study aimed to adopt YOLOv8 for automatic car detection on the road. YOLOv8 is proven to perform better than the previous version. A dataset comprising video frame images was captured on the highway in Semarang, Indonesia. The experiment results indicated that the proposed approach achieved impressive precision, recall, and mAP values, reaching 94.1%, 98.2%, and 98.8%, respectively. The proposed approach enhanced mAP and training time when compared with YOLOv5. Therefore, it was concluded that the proposed method was better suited for real-time car detection. [ABSTRACT FROM AUTHOR]

It is crucial to understand the mechanism of how people perceive and accept Shared Autonomous Vehicles (SAVs) to realize the benefits that they will bring to transport networks, including increased safety on the roads. Experience with semi-automated cars and Autonomous Vehicle (AV) demonstration projects has been shown to positively affect acceptance of AVs, however automated transport technologies have been adopted in practice for years such as automated railways and aircraft autopilot; whether there are any connections between exposure to automated railways and aircraft autopilot and acceptance of SAVs is not well known. We explored the connections between trust in safety, knowledge of, and experience with automated railways, aircraft autopilot, and ADAS and intention to use a SAV. We surveyed individuals from Korea National University of Transportation (KNUT) in Chungju, Korea (n = 226), who will soon be able to use a SAV service, and adopted a model based on the Technology Acceptance Model (TAM). We found there to indeed be a connection between exposure to and trust in the safety of existing automated transport modes and acceptance of a SAV. Constructs with the strongest effects were found to be trust in safety of automated transport, hedonic motivation, perceived ease-of-use, perceived usefulness, and knowledge of automated transport. Experience with automated railways, but not semi-automated cars, was found to negatively moderate the relationship between hedonic motivation and intention to use a SAV. We discuss the policy implications of these results. [ABSTRACT FROM AUTHOR]

Since autonomous vehicles (AV) are in the testing process, it is an open question of how pedestrians will communicate with self-driving cars. Nowadays, explicit communication pattern is the main way of pedestrian-driver interaction, however, AV may use implicit communication when making crossing decisions. This study aims to analyze pedestrians' gaze behavior when crossing the road using an eye camera and find the most applicable location for the LED interface on AVs. 10 pedestrian crossings in Gyor, Hungary were analyzed using the synchronized eye-tracking (ET) technology and regular video cameras for combined data processing. The data were analyzed using digital image processing techniques and statistical methods to identify where pedestrians looked and whether a pedestrian-driver interaction was captured during the crossing maneuver. [ABSTRACT FROM AUTHOR]

The commercialization and the subsequent widespread adoption of self-driving electric cars have allowed environmentally friendly vehicles to have more interior space than traditional gas-powered vehicles, which has, in turn, led to a growing demand for space utilization of vehicle interiors. A monopost seat is an innovative, lightweight seat designed to maximize space utilization under the seat, with the insertion of a monopost columnar structure between the monopost seat guide rail and the floorboard. However, compared to standard seats, a monopost seat leaves room for improvement when it comes to safety considerations. In our previous study, dynamic simulations of a monopost seat were performed using LS-DYNA. The results revealed that the upper slide rail was the most vulnerable component. In this study, optimal designs were generated to make the upper sliding rail more stable and lighter. To this end, PIAnO, a comprehensive optimal design program, was employed to combine the orthogonal array design of experiments with metamodel-based optimal design methods to deliver the best possible model. A series of simulations confirmed the safety of the new model, which was a significantly improved version of the existing design in terms of stability and weight. [ABSTRACT FROM AUTHOR]

This document discusses the importance of Positioning, Navigation, and Timing (PNT) solutions in various industries. In smart cities, PNT enhances infrastructure and IoT integration, leading to more efficient urban management. In the automotive industry, advanced PNT solutions are essential for the safe and efficient operation of autonomous vehicles. In the maritime and aviation industries, advanced PNT technologies enable precise navigation and ensure compliance with regulations. In defense and security, resilient PNT systems provide critical support in GNSS-denied environments. The document also highlights challenges and opportunities in PNT research, such as improving precision, managing large-scale deployments, ensuring safety, and addressing data quality and privacy concerns. The authors suggest future directions for PNT research, including the integration of artificial intelligence, multi-sensor fusion, quantum technologies, edge computing, security and resilience, advanced applications, standardization and interoperability, and privacy considerations. [Extracted from the article]

Artificial intelligence (AI) systems of autonomous systems such as drones, robots and self-driving cars may consume up to 50% of the total power available onboard, thereby limiting the vehicle's range of functions and considerably reducing the distance the vehicle can travel on a single charge. Next-generation onboard AI systems need an even higher power since they collect and process even larger amounts of data in real time. This problem cannot be solved using traditional computing devices since they become more and more power-consuming. In this review article, we discuss the perspectives on the development of onboard neuromorphic computers that mimic the operation of a biological brain using the nonlinear–dynamical properties of natural physical environments surrounding autonomous vehicles. Previous research also demonstrated that quantum neuromorphic processors (QNPs) can conduct computations with the efficiency of a standard computer while consuming less than 1% of the onboard battery power. Since QNPs are a semi-classical technology, their technical simplicity and low cost compared to quantum computers make them ideally suited for applications in autonomous AI systems. Providing a perspective on the future progress in unconventional physical reservoir computing and surveying the outcomes of more than 200 interdisciplinary research works, this article will be of interest to a broad readership, including both students and experts in the fields of physics, engineering, quantum technologies and computing. [ABSTRACT FROM AUTHOR]

Over the past few years, neural networks have started penetrating safety critical systems to make decisions as, for example, in robots, rockets, and autonomous driving cars. Neural networks based on floating-point arithmetic are very time and memory consuming, which are not compatible with embedded systems known to have limited resources. They are also very sensitive to the precision in which they have been trained, so changing this precision generally degrades the quality of their answers. To deal with that, we introduce a new technique to generate a fixed-point code for a trained neural network. This technique is based on fixed-point arithmetic with mixed-precision. This arithmetic is based on integer operations only, which are compatible with small memory devices. The obtained neural network has the same behavior as the initial one (based on the floating-point arithmetic) up to an error threshold defined by the user. The experimental results show the efficiency of our tool SyFix in terms of memory saved and the accuracy of the computations. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to explore and ranks the factors that might determine attitudes and intentions toward using autonomous vehicles (AVs). Design/methodology/approach: The "technology acceptance model" (TAM) was extended by assessing the moderating influences of personal-related factors. Data were collected from 378 Vietnamese and analysed using a combination of "partial least squares" and the "adaptive neuro-fuzzy inference system" (ANFIS) technique. Findings: The findings demonstrated the power of TAM in explaining the attitude and intention to use AVs. ANFIS enables ranking the importance of determinants and predicting the outcomes. Perceived ease of use and attitude were the most crucial drivers of attitude and intention to use AVs, respectively. Personal innovativeness negatively moderates the influence of perceived ease of use on attitude. Data privacy concerns moderate positively the impact of perceived usefulness on attitude. The moderating effect of price sensitivity was not supported. Practical implications: These findings provide insights for policymakers and automobile companies' managers, designers and marketers on driving factors in making decisions to adopt AVs. Originality/value: The study extends the AVs literature by illustrating the importance of personal-related factors, ranking the determinants of attitude and intention, illustrating the inter-relationships among AVs adoption factors and predicting individuals' attitudes and behaviours towards using AVs. [ABSTRACT FROM AUTHOR]

Autonomous driving is a recently developed area in which technology seems to be ahead of its understanding within society. That causes some fears concerning the reliability of autonomous vehicles and controversies over liability in case of accidents. Specifying levels of driving autonomy within the SAE-J3016 standard is widely recognized as a significant step towards comprehending the essence of the achievements. However, the standard provides even more valuable insights into the process of driving automation. In the paper, we develop the ideas using the methods of formal ontology that allow us to make the conceptual system more precise and formalize it. To increase inseparability, we ground our system on a top-level BFO ontology. We present a formal account of several areas covered by the SAE-J3016 standard, including motor vehicles and their systems, driving tasks and subtasks, roles of persons in road communication, and autonomy levels. [ABSTRACT FROM AUTHOR]

Silicon photonics is one of the most dynamic fields within photonics, and it has seen huge progress in the last 20 years, addressing applications in data centers, autonomous cars, and sensing. It is mostly focused on the telecommunications wavelength range (1.3 and 1.55 µm), where silicon becomes transparent. In this range, there are excellent light sources and photodetectors, as well as optical fibers operating with extremely low losses and dispersion. It is a technology that hugely benefits from the availability of complementary metal–oxide–semiconductor (CMOS) fabrication infrastructure and techniques used for microelectronics. Silicon and germanium, as another CMOS compatible group IV material, are transparent beyond the wavelength of 2 µm. The mid-IR wavelength range (2–20 µm) is of particular importance as it contains strong absorption signatures of many molecules. Therefore, Si- and Ge-based platforms open up the possibility of small and cost-effective sensing in the fingerprint region for medical and environmental monitoring. In this paper, we discuss the current mid-IR silicon photonics landscape, future directions, and potential applications of the field. [ABSTRACT FROM AUTHOR]

In today's big data era, with the development of the Internet of Things (IoT) technology and the trend of autonomous driving prevailing, visual information has shown a blowout increase, but most image matching algorithms have problems such as low accuracy and low inlier rates, resulting in insufficient information. In order to solve the problem of low image matching accuracy and low inlier rate in the field of autonomous driving, this research innovatively applies spectral clustering (SC) in the field of data analysis to image matching in the field of autonomous driving, and a new image matching algorithm "SC-RANSAC" based on SC and Random Sample Consensus (RANSAC) is proposed. The datasets in this research are collected based on the monocular cameras of autonomous driving cars. We use RANSAC to obtain the initial inlier set and the SC algorithm to filter RANSAC's outliers and then use the filtered inliers as the final inlier set. In order to verify the effectiveness of the algorithm, it shows the matching effect from three angles: camera translation, rotation, and rotation and translation. SC-RANSAC is also compared with RANSAC, graph-cut RANSAC, and marginalizing sample consensus by using two different types of datasets. Finally, we select three representative pictures to test the robustness of the SC-RANSAC algorithm. The experimental results show that SC-RANSAC can effectively and reliably eliminate mismatches in the initial matching results; has a high inlier rate, real-time performance, and robustness; and can be effectively applied in the environment of autonomous driving. [ABSTRACT FROM AUTHOR]

In the past few years, the lane detection technique has become a key factor for autonomous driving systems and self-driving cars on the road. Among the various vehicle subsystems, the lane detection module is one of the essential parts of the Advanced Driver Assistance System (ADAS). Conventional lane detection approaches use machine vision algorithms to find straight lines in road scene images. However, it is challenging to identify straight or curved lane markings in complex environments. To deal with this problem, this paper presents a lane detection technique based on deep learning. It is combined with a 3D convolutional network, so the temporal information is added to the network architecture. Using the front camera images, the system can immediately detect the lane marking information ahead. Moreover, we propose an approach for improvement by adding the time axis to the network architecture. In addition to using 3D-ResNet50, the temporal convolution and spatial convolution are separated for processing. The accuracy is 91.34% improved after adding time and space split convolution with LeakyReLU. The experiments carried out using real scene images have demonstrated the feasibility of the proposed technique for applications to various complex scenes. [ABSTRACT FROM AUTHOR]

Lane detection is an essential task for autonomous vehicles. Deep learning-based lane detection methods are leading development in this sector. This paper proposes an algorithm named Deep Learning-based Lane Detection (DLbLD), a Convolutional Neural Network (CNN)-based lane detection algorithm. The presented paradigm deploys CNN to detect line features in the image block, predict a point on the lane line part, and project all the detected points for each frame into one-dimensional form before applying K-mean clustering to assign points to related lane lines. Extensive tests on different benchmarks were done to evaluate the performance of the proposed algorithm. The results demonstrate that the introduced DLbLD scheme achieves state-of-the-art performance, where F1 scores of 97.19 and 79.02 have been recorded for TuSimple and CU-Lane benchmarks, respectively. Nevertheless, results indicate the high accuracy of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Intelligent Transportation Systems (ITSs) have grown rapidly to accommodate the increasing need for safer, more efficient, and environmentally friendly transportation options. These systems cover a wide range of applications, from transportation control and management to self-driving vehicles to improve mobility while tackling urbanization concerns. This research looks closely at the important infrastructure parts of vehicle-to-vehicle (V2V) communication systems. It focuses on the different types of communication architectures that are out there, including decentralized mesh networks, cloud-integrated hubs, edge computing-based architectures, blockchain-enabled networks, hybrid cellular networks, ad-hoc networks, and AI-driven dynamic networks. This review aims to critically analyze and compare the key components of these architectures with their contributions and limitations. Finally, it outlines open research challenges and future technological advancements, encouraging the development of robust and interconnected V2V communication systems in ITSs. [ABSTRACT FROM AUTHOR]

Installing multi-camera systems and inertial measurement units (IMUs) in self-driving cars, micro aerial vehicles, and robots is becoming increasingly common. An IMU provides the vertical direction, allowing coordinate frames to be aligned in a common direction. The degrees of freedom (DOFs) of the rotation matrix are reduced from 3 to 1. In this paper, we propose a globally optimal solver to calculate the relative poses and scale of generalized cameras with a known vertical direction. First, the cost function is established to minimize algebraic error in the least-squares sense. Then, the cost function is transformed into two polynomials with only two unknowns. Finally, the eigenvalue method is used to solve the relative rotation angle. The performance of the proposed method is verified on both simulated and KITTI datasets. Experiments show that our method is more accurate than the existing state-of-the-art solver in estimating the relative pose and scale. Compared to the best method among the comparison methods, the method proposed in this paper reduces the rotation matrix error, translation vector error, and scale error by 53%, 67%, and 90%, respectively. [ABSTRACT FROM AUTHOR]

Autonomous navigation is the leading technology in current era, in this intelligent traffic light, sign detection, ADAS and obstacle detections were playing major role. Image segmentation is the process of dividing an image into different regions, or semantic classes. This is a challenging problem in autonomous vehicle technology because it requires the vehicle to be able to understand its surroundings to safely navigate. The major challenges in this platform are the accuracy and efficiency of model performance. The proposed method in the abstract uses a convolutional neural network (CNN) to perform image segmentation. CNNs are a type of deep learning model that is well-suited for image processing tasks. The CNN in this paper was trained on a local city dataset, and it was able to achieve a mean intersection over union (IoU) of 73%. IoU is a measure of how well the segmentation results match the ground truth labels. A score of 100% indicates that the segmentation is perfect, while a score of 0% indicates that the segmentation is completely wrong. This means that the method can segment images at a very fast rate, which is important for autonomous vehicles that need to make real-time decisions. Overall, the proposed method is a promising approach for image segmentation in autonomous vehicles. It can achieve high accuracy and speed, and it is easy to implement using Python. The proposed method attains an accuracy of 98.34 %, a Sensitivity of 97.26 % and a sensitivity of 96.37 % had been attained. The method could be used to improve the safety and efficiency of autonomous vehicles by enabling them to better understand their surroundings. [ABSTRACT FROM AUTHOR]

The artificial intelligence required to create self-directed automobiles relies heavily on the capability of precisely perceiving the environment around oneself. Most self-driving automobiles include several detectors, which work together to form a multi-source perception of the surroundings. Extended use of a system that drives autonomously will introduce a variety of worldwide and local failure indications due to the extreme sensitivity of the instruments involved to ambient or environmental situations. These failure indications pose significant risks to the technique's security. The paper presents a real-time information synthesis system incorporating techniques for identifying flaws and accepting faults. The compact connection can be recognized if the qualities mentioned above are provided, and the input information properties may be retrieved in real-time. One way to use the newly introduced method for assessing device reliability is to compute the detectors' worldwide and local degrees of trustworthiness. In order to ensure the precision and dependability of information combination, problem data is filtered out, and monitor duplication is used to assess both the worldwide and local assurance levels of data from sensors at the moment. The chronological and geographic association of data from sensors allows for this. Experimental findings show that the network's algorithms can outperform current techniques in terms of both rapidity and precision and can pinpoint the object's location even when specific sensors are blurry or broken. This research established that the proposed hybrid structure benefits autonomous vehicles' real-time reliability and speed. [ABSTRACT FROM AUTHOR]

The article explores the use of simulation testing for automated vehicles (AVs) and the different approaches taken by AV companies. Imagry and Helm.ai use neural networks and unsupervised learning, respectively, to train their AV software. Real-world testing and data collection are seen as crucial by both companies. DeepScenario, based in Germany, has developed software that creates detailed virtual testbed environments for automakers to run their algorithms against. rFpro, in collaboration with dRISK.ai, is involved in UK government-funded projects to improve simulation accuracy for AV systems and identify gaps in training. The projects aim to determine when simulations are reliable enough for AV testing. [Extracted from the article]

Recent 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. [ABSTRACT FROM AUTHOR]

Few prior works study self-driving cars by deep learning with IoT collaboration. SDC-Net, which is an end-to-end multitask self-driving car camera cocoon IoT-based system, is one of the research areas that tackles this direction. However, by design, SDC-Net is not able to identify the accident locations; it only classifies whether a scene is a crash scene or not. In this work, we introduce an enhanced design for the SDC-Net system by (1) replacing the classification network with a detection one, (2) adapting our benchmark dataset labels built on the CARLA simulator to include the vehicles' bounding boxes while keeping the same training, validation, and testing samples, and (3) modifying the shared information via IoT to include the accident location. We keep the same path planning and automatic emergency braking network, the digital automation platform, and the input representations to formulate the comparative study. The SDC-Net++ system is proposed to (1) output the relevant control actions, especially in case of accidents: accelerate, decelerate, maneuver, and brake, and (2) share the most critical information to the connected vehicles via IoT, especially the accident locations. A comparative study is also conducted between SDC-Net and SDC-Net++ with the same input representations: front camera only, panorama and bird's eye views, and with single-task networks, crash avoidance only, and multitask networks. The multitask network with a BEV input representation outperforms the nearest representation in precision, recall, f1-score, and accuracy by more than 15.134%, 12.046%, 13.593%, and 5%, respectively. The SDC-Net++ multitask network with BEV outperforms SDC-Net multitask with BEV in precision, recall, f1-score, accuracy, and average MSE by more than 2.201%, 2.8%, 2.505%, 2%, and 18.677%, respectively. [ABSTRACT FROM AUTHOR]

This paper explores the potential of a German-language chatbot to engage users in argumentative dialogues on ethically sensitive topics. Utilizing an argumentative knowledge graph, the chatbot is equipped to engage in discussions on the ethical implications of autonomous AI systems in hypothetical future scenarios in the fields of medicine, law, and self-driving cars. In a study with 178 student participants, we investigated the chatbot's argumentation effect—its ability to offer new perspectives, gain user acceptance, and broaden users' viewpoints on complex issues. The results indicated a substantial argumentation effect, with 13–21% of participants shifting their opinions to more moderate stances after interacting with the chatbot. This shift demonstrates the system's effectiveness in fostering informed discourse and increasing users' understanding of AI ethics. While the chatbot was well-received, with users acknowledging the quality of its arguments, we identified opportunities for improvement in its argument recognition capabilities. Despite this, our results indicate the chatbot's potential as an educational tool in engaging users with the ethical dimensions of AI technology and promoting informed discourse. [ABSTRACT FROM AUTHOR]

With the development of autonomous cars, the incidence of motion sickness has increased. Current studies on the characteristics of motion sickness have primarily focused on its long-term characteristics while ignoring its short-term characteristics, especially for the reporting time of the real-time evaluation method. This study explores the long and short-term characteristics of motion sickness, and uses self-reported motion sickness ratings at the end of car motion (the high point) and 3 s after the end of motion (the low point). Motion sickness ratings increased with task vs. no-task conditions. There is no significant correction between gender and maximum motion sickness in this study. Moreover, participants with high motion sickness susceptibility experienced increased motion sickness. The difference between the low point and the high point is found to decrease as the motion sickness ratings increase. [ABSTRACT FROM AUTHOR]

Autonomous systems are becoming increasingly relevant in our everyday life. The transportation field is no exception and the smart cities concept raises new tasks and challenges for the development of autonomous systems development which has been progressively researched in literature. One of the main challenges is communication between different traffic objects. For instance, a mobile robot system can work as a standalone autonomous system reacting to a static environment and avoiding obstacles to reach a target. Nevertheless, more intensive communication and decision making is needed when additional dynamic objects and other autonomous systems are present in the same working environment. Traffic is a complicated environment consisting of vehicles, pedestrians, and various infrastructure elements. To apply autonomous systems in this kind of environment it is important to integrate object localization and to guarantee functional and trustworthy communication between each element. To achieve this, various sensors, communication standards, and equipment are integrated via the application of sensor fusion and AI machine learning methods. In this work review of vehicular communication systems is presented. The main focus is the researched sensors, communication standards, devices, machine learning methods, and vehicular-related data to find existing gaps for future vehicular communication system development. In the end, discussion and conclusions are presented. [ABSTRACT FROM AUTHOR]

This study endeavors to examine the factors influencing consumers' intentions to purchase self‐driving cars. The data collected from 174 respondents underwent analysis using the partial least squares technique. The findings reveal that relative advantage, compatibility, trialability, and observability significantly impact consumer attitudes. Additionally, consumer attitude, subjective norm, and perceived behavioral control were identified as significant influencers of the intention to purchase a self‐driving car. Furthermore, achievement vanity was observed to moderate the effect of observability on consumer attitude. These results offer critical theoretical insights and practical implications for the adoption of self‐driving cars. [ABSTRACT FROM AUTHOR]

Human decision-making is increasingly supported by artificial intelligence (AI) systems. From medical imaging analysis to self-driving vehicles, AI systems are becoming organically embedded in a host of different technologies. However, incorporating such advice into decision-making entails a human rationalization of AI outputs for supporting beneficial outcomes. Recent research suggests intermediate judgments in the first stage of a decision process can interfere with decisions in subsequent stages. For this reason, we extend this research to AI-supported decision-making to investigate how intermediate judgments on AI-provided advice may influence subsequent decisions. In an online experiment (N = 192), we found a consistent bolstering effect in trust for those who made intermediate judgments and over those who did not. Furthermore, violations of total probability were observed at all timing intervals throughout the study. We further analyzed the results by demonstrating how quantum probability theory can model these types of behaviors in human–AI decision-making and ameliorate the understanding of the interaction dynamics at the confluence of human factors and information features. [ABSTRACT FROM AUTHOR]

There is a growing body of scholarship on the ethics of autonomous vehicles. Yet the ethical discourse has mostly been focusing on the behavior of the vehicle in accident scenarios. This paper offers a different ethical prism: the implications of the autonomous vehicle for human well-being. As such, it contributes to the growing discourse on the wider societal and moral implications of the autonomous vehicle. The paper is premised on the neo-Aristotelian approach which holds that as human beings, our well-being depends on developing and exercising our innate human capacities: to know, understand, love, be sociable, imagine, create and use our bodies and use our willpower. To develop and exercise these capacities, our environments need to provide a range of opportunities which will trigger the development and exercise of the capacities. The main argument advanced in the paper is that one plausible future of the autonomous vehicle—a future of single-rider autonomous vehicles—may effectively reduce the opportunities to develop and exercise our capacities to know, be sociable and use our willpower. It will therefore be bad for human well-being, and this provides us with a moral reason to resist this plausible future and search for alternative ones. [ABSTRACT FROM AUTHOR]

Deep learning (DL), a variant of the neural network algorithms originally proposed in the 1980s (Rumelhart et al., 1986), has made surprising progress in artificial intelligence (AI), ranging from language translation, protein folding (Jumper et al., 2021), autonomous cars, and, more recently, human-like language models (chatbots). All that seemed intractable until very recently. Despite the growing use of DL networks, little is understood about the learning mechanisms and representations that make these networks effective across such a diverse range of applications. Part of the answer must be the huge scale of the architecture and, of course, the large scale of the data, since not much has changed since 1986. But the nature of deep learned representations remains largely unknown. Unfortunately, training sets with millions or billions of tokens have unknown combinatorics, and networks with millions or billions of hidden units can't easily be visualized and their mechanisms can't be easily revealed. In this letter, we explore these challenges with a large (1.24 million weights VGG) DL in a novel high-density sample task (five unique tokens with more than 500 exemplars per token), which allows us to more carefully follow the emergence of category structure and feature construction. We use various visualization methods for following the emergence of the classification and the development of the coupling of feature detectors and structures that provide a type of graphical bootstrapping. From these results, we harvest some basic observations of the learning dynamics of DL and propose a new theory of complex feature construction based on our results. [ABSTRACT FROM AUTHOR]

Automobile-robot (self-driving automobile) is being researched and developed vigorously. When the automobile-robot is moving on the road surface, the low frequency vibration excitation not only influences the ride comfort of the automobile-robot but also strongly affects the durability of the vehicle's structures. To research the automobile-robot's vibration in the low frequency region, a dynamic model of the vehicle is established to calculate the vibration equations in the time region. Based on the theory of the Laplace transfer function, the automobilerobot's vibration equations in the time region are transformed and converted to the vibration equations in the frequency region. Then, the effect of the design parameters and operation parameters on the characteristic of the automobile-robot's acceleration-frequency is simulated and analyzed to evaluate the ride comfort as well as the durability of the automobile-robot's structures in the frequency region. The research results show that the design parameters of the stiffness, mass, and road wavelength remarkably affect the characteristic of the automobile-robot's acceleration-frequency. To reduce the resonant amplitude of the acceleration-frequency in the vertical and pitching direction of the automobile-robot, the stiffness parameters of the automobilerobot's and tires should be reduced while the mass of the automobile-robot's body should be increased. Additionally, the road's roughness also needs to be decreased or the road's quality needs to be enhanced to reduce the resonant amplitude of the automobile-robot's accelerationfrequency. [ABSTRACT FROM AUTHOR]

Connected Autonomous vehicles (CAVs) are expected to improve the safety and efficiency of traffic by automating driving tasks. Amongst those, lane changing is particularly challenging, as it requires the vehicle to be aware of its highly-dynamic surrounding environment, make decisions, and enact them within very short time windows. As CAVs need to optimise their actions based on a large set of data collected from the environment, Reinforcement Learning (RL) has been widely used to develop CAV motion controllers. These controllers learn to make efficient and safe lane changing decisions using on-board sensors and inter-vehicle communication. This paper, first presents four overlapping fields that are key to the future of safe self-driving cars: CAVs, motion control, RL, and safe control. It then defines the requirements for a safe CAV controller. These are used firstly to compare applications of Multi-Agent Reinforcement Learning (MARL) to CAV lane change controllers. The requirements are then used to evaluate state-of-the-art safety methods used for RL-based motion controllers. The final section summarises research gaps and possible opportunities for the future development of safe MARL-based CAV motion controllers. In particular, it highlights the requirement to design MARL controllers with continuous control for lane changing. Moreover, as RL algorithms by themselves do not guarantee the level of safety required for such safety-critical applications, it offers insights and challenges to integrate safe RL methods with MARL-based CAV motion controllers. [ABSTRACT FROM AUTHOR]

In driverless formula car racing, cone detection faces two significant challenges: one is recognizing cones at long distances accurately, and the other is being prone to leakage under bright light conditions. These challenges directly affect the detection accuracy and response speed. In order to cope with these problems, the thesis is based on YOLOv8s to improve the cone bucket detection algorithm. Firstly, a P2 detection layer for detecting tiny objects is added on top of YOLOv8s to detect small targets with 160 × 160 pixels, which improves the detection of small conical buckets in the distant view. At the same time, to reduce the network's complexity to achieve lightweightness, the original 20 × 20 pixel detection header is deleted. Second, the head of the original YOLOv8 is replaced with a multi-scale fusion Dynamic Head, designed to improve the head's ability in scale, space, and task perception to enhance the detection performance of the model in complex scenes. Again, a novel loss function, MPDIoU, is introduced, which has advantages in simplifying the bounding box similarity comparison, and it can adapt to the overlapping or non-overlapping situation of the bounding box more effectively. It reduces the phenomenon of missed detection caused by overlapping conical buckets. Finally, the LAMP pruning method is used to trim the model to make the model lightweight. By adding and modifying the above modules, the improved algorithm improves the detection accuracy from 92.2% to 95.2%, the recall rate from 84.2% to 91.8%, and the average accuracy from 91.3% to 96%, while the number of parameters is reduced from 28.7 M to 26.6 M. The detection speed still meets the real-time requirement in real-vehicle testing compared to the original algorithm. In the real car test, compared with the original algorithm, the improved algorithm shows apparent advantages in reducing the missed detection of cones and barrels, which meets the demand for high accuracy of cones and barrel detection in the complex race environment and also meets the conditions for deployment on small devices with limited resources. [ABSTRACT FROM AUTHOR]