Autonomous vehicles and Intelligent Transport Systems (ITS) have started to become a reality in recent years. However, shortcomings of these early intelligent vehicles demonstrate a need to increase an intelligent vehicle’s perceptual bubble beyond the vehicle’s onboard sensors. Vehicle-to-Everything (V2X) communications is a technology intended to enable intelligent vehicles to increase their perceptive range via communication with the outside world i.e. other vehicles, road users, and infrastructure. One of the critical layers of V2X communications is that of the underlying wireless access. There remains an open debate on which technology (or technologies) will ultimately be adopted as the de facto wireless access technology for V2X communications. Presently, a myriad of unconnected threads of work are being undertaken to this end, resulting in an unclear picture of the current state of wireless access for V2X communications. In this tutorial review, we collate current research on wireless access for V2X communications to showcase recent developments and the challenges facing candidate technologies. To allow the reader to engage with the debate, we present an overview of the field of V2X communications, its history, and standardisation. Following this, we provide an overview of the key candidate wireless access technologies that may enable V2X communications. Through this work, we aim to provide a comprehensive first step into the discipline of wireless access for V2X communications.

Surround-view fisheye cameras are commonly used for near-field sensing in automated driving. Four fisheye cameras on four sides of the vehicle are sufficient to cover 360° around the vehicle capturing the entire near-field region. Some primary use cases are automated parking, traffic jam assist, and urban driving. There are limited datasets and very little work on near-field perception tasks as the focus in automotive perception is on far-field perception. In contrast to far-field, surround-view perception poses additional challenges due to high precision object detection requirements of 10cm and partial visibility of objects. Due to the large radial distortion of fisheye cameras, standard algorithms cannot be extended easily to the surround-view use case. Thus, we are motivated to provide a self-contained reference for automotive fisheye camera perception for researchers and practitioners. Firstly, we provide a unified and taxonomic treatment of commonly used fisheye camera models. Secondly, we discuss various perception tasks and existing literature. Finally, we discuss the challenges and future direction.

This paper reviews the Internet of Things (IoT) in healthcare, also known as Healthcare-IoT (H-IoT), and its security and privacy challenges. Specifically, smartwatches, blood pressure sensors, and temperature sensors are discussed as IoT devices that pose vulnerabilities. Several risks may arise, such as data breaches, unauthorized access, and potential patient harm. This review explores data security challenges associated with machine learning and H-IoT. Additionally, it emphasizes the importance of monitoring healthcare IoT layers such as perception, network, cloud, and application layers. Detecting and responding to anomalies involve a wide range of cyber threats and attacks, as well as protocols such asWi-Fi 6, Narrowband Internet of Things (NB-IoT), Bluetooth, ZigBee, LoRa, and 5G New Radio (5G NR). Hence, to protect and mitigate H-IoT devices from the increasing cyber threat landscape, a robust authentication mechanism based on machine learning and deep learning techniques is required. Overall, this paper reviews the security and privacy challenges in healthcare IoT and discusses risk mitigation strategies for building cyber resilience in H-IoT.

Cameras are the primary sensor in automated driving systems. They provide high information density and are optimal for detecting road infrastructure cues laid out for human vision. Surround-view camera systems typically comprise of four fisheye cameras with 190°+ field of view covering the entire 360° around the vehicle focused on near-field sensing. They are the principal sensors for low-speed, high accuracy, and close-range sensing applications, such as automated parking, traffic jam assistance, and low-speed emergency braking. In this work, we provide a detailed survey of such vision systems, setting up the survey in the context of an architecture that can be decomposed into four modular components namely Recognition, Reconstruction, Relocalization, and Reorganization. We jointly call this the 4R Architecture. We discuss how each component accomplishes a specific aspect and provide a positional argument that they can be synergized to form a complete perception system for low-speed automation. We support this argument by presenting results from previous works and by presenting architecture proposals for such a system. Qualitative results are presented in the video at https://youtu.be/ae8bCOF77uY.

Automated vehicles rely heavily on image data from visible spectrum cameras to perform a wide range of tasks from object detection, classification, and avoidance to path planning. The availability and reliability of these sensors in adverse weather is therefore of critical importance to the safe and continuous operation of an automated vehicle. This review paper presents a data communication-inspired Image Formation Framework that characterizes the data flow from object through channel to sensor, and subsequent processing of the data. This framework is used to explore the degree to which adverse weather conditions impact the cameras used on automated vehicles for sensing and perception. The effect of rain on each element of the model is reviewed. Furthermore, the prevalence of the appearance of these rain-induced changes in publicly available, open-source datasets is reviewed. The degree to which synthetic rain generation techniques can accurately capture these changes is also examined. Finally, the paper offers some suggestions on how future adverse weather automotive datasets should be collected.

In this paper, we provide a survey on automotive surround-view fisheye optics, with an emphasis on the impact of optical artifacts on computer vision tasks in autonomous driving and ADAS. The automotive industry has advanced in applying state-of-the-art computer vision to enhance road safety and provide automated driving functionality. When using camera systems on vehicles, there is a particular need for a wide field of view to capture the entire vehicle’s surroundings, in areas such as low-speed maneuvering, automated parking, and cocoon sensing. However, one crucial challenge in surround-view cameras is the strong optical aberrations of the fisheye camera, which is an area that has received little attention in the literature. Additionally, a comprehensive dataset is needed for testing safety-critical scenarios in vehicle automation. The industry has turned to simulation as a cost-effective strategy for creating synthetic datasets with surround-view camera imagery. We examine different simulation methods (such as model-driven and data-driven simulations) and discuss the simulators’ ability (or lack thereof) to model real-world optical performance. Overall, this paper highlights the optical aberrations in automotive fisheye datasets, and the limitations of optical reality in simulated fisheye datasets, with a focus on computer vision in surround-view optical systems.