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.

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.

Vehicle-to-Everything (V2X) communication technology aims to enhance intelligent vehicles’ coordination and perception by enabling interactions with other vehicles, road users, and infrastructure. Although 4th and 5th generation cellular technologies (4G LTE and 5G NR) can support non-safety V2X communication, their design, rooted in multimedia and telephony, doesn’t align well with safety-critical applications. Despite the advancements of 5G NR over 4G LTE, ensuring reliable Quality-of-Service (QoS) metrics remains a notable concern. The process of handover, a core cellular network function, significantly contributes to the challenges in cellular communication reliability. To assess handover’s impact on latency in V2X communication scenarios, a driving-focused measurement campaign was executed on an early 5G NR-enabled commercial cellular network. Findings reveal that, in terms of its effect on end-user latency, the more mature 4G LTE network displays on average 63% (intra-freq.) and 36% (inter-freq.) lower latency costs when performing handovers compared to early 5G NR deployments. Notably, inter-generation handovers (4G to 5G and vice versa) exhibit 24% (intra-freq.) and 10% (inter-freq.) higher average latency costs with respect to other handover types. Furthermore, discernible variations in network reliability emerge between distinct V2X scenarios such as urban and rural environments. The prevailing reliability concerns tied to present handover algorithms hold substantial weight, as they undermine intelligent vehicles’ trust in network-received information. Consequently, these outcomes underscore the urgency for designing and implementing handover strategies better suited to the dynamic mobility characteristics of intelligent vehicles across diverse V2X scenarios.

Despite advancements in perception technology, achieving full autonomy in vehicles remains challenging partly due to limited situational awareness. Even with their sophisticated sensor arrays, autonomous vehicles often struggle to comprehend complex real-world environments due to the challenges associated with occlusion. A possible solution for addressing this limitation lies in the concept of vehicle-to-infrastructure cooperative driving, which enables vehicles to interact with various sensors implemented in the surrounding infrastructure. The infrastructure can share real-time data, such as traffic conditions, road hazards, and weather updates, facilitating safer and more efficient navigation. Within this framework, cooperative sensing is a crucial component, augmenting the onboard sensing capabilities of autonomous vehicles. Cooperative sensing surpasses traditional onboard sensors by leveraging a shared sensor network among vehicles and infrastructure. This approach mitigates challenges posed by occlusion, where objects are obscured from a vehicle's direct view. By pooling information from multiple sources, autonomous vehicles can gain a more comprehensive understanding of their surroundings, leading to enhanced safety and performance on the road. This study addresses a literature gap regarding information flow from real-world scenes to environmental models for cooperative V2I systems. It explores three core concepts essential for understanding the environment: sensing, perception, and mapping. This paper identifies the specific information required from infrastructure nodes, proposes an optimized sensor suite, discusses data processing algorithms, and investigates effective spatial model representations for cooperative sensing. This research informs the reader about the different challenges and opportunities associated with a V2I cooperative sensing system.

Interacting with other roads users is a challenge for an autonomous vehicle, particularly in urban areas. Existing vehicle systems behave in a reactive manner, warning the driver or applying the brakes when the pedestrian is already in front of the vehicle. The ability to anticipate a pedestrian’s crossing intention ahead of time will result in safer roads and smoother vehicle maneuvers. The problem of crossing intent forecasting at intersections is formulated in this paper as a classification task. A model that predicts pedestrian crossing behaviour at different locations around an urban intersection is proposed. The model not only provides a classification label (e.g., crossing, not-crossing), but a quantitative confidence level (i.e., probability). The training and evaluation are carried out using naturalistic trajectories provided by a publicly available dataset recorded from a drone. Results show that the model is able to predict crossing intention within a 3-s time window.