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.

Wastewater treatment facilities are continually challenged to meet both environmental regulations and reduce running costs (particularly energy and staffing costs). Improving the efficiency of operational monitoring at wastewater treatment plants (WWTPs) requires the development and implementation of appropriate performance metrics; particularly those that are easily measured, strongly correlate to WWTP performance, and can be easily automated, with a minimal amount of maintenance or intervention by human operators. Turbidity is the measure of the relative clarity of a fluid. It is an expression of the optical property that causes light to be scattered and absorbed by fine particles in suspension (rather than transmitted with no change in direction or flux level through a fluid sample). In wastewater treatment, turbidity is often used as an indicator of effluent quality, rather than an absolute performance metric, although correlations have been found between turbidity and suspended solids. Existing laboratory-based methods to measure turbidity for WWTPs, while relatively simple, require human intervention and are labour intensive. Automated systems for on-site measuring of wastewater effluent turbidity are not commonly used, while those present are largely based on submerged sensors that require regular cleaning and calibration due to fouling from particulate matter in fluids. This paper presents a novel, automated system for estimating fluid turbidity. Effluent samples are imaged such that the light absorption characteristic is highlighted as a function of fluid depth, and computer vision processing techniques are used to quantify this characteristic. Results from the proposed system were compared with results from established laboratory-based methods and were found to be comparable. Tests were conducted using both synthetic dairy wastewater and effluent from multiple WWTPs, both municipal and industrial. This system has an advantage over current methods as it provides a multipoint analysis that can be easily repeated for large volumes of wastewater effluent. Although the system was specifically designed and tested for wastewater treatment applications, it could have applications such as in drinking water treatment, and in other areas where fluid turbidity is an important measurement.

Determining driver situational awareness is one of the most difficult tasks in driver performance profiling. Perception, being the first level of driver situational awareness, is perhaps the only objectively quantifiable factor for driver inattention detection. In the context of this paper, driver perception is defined as their ability to understand their operating environment and peripheral contextual information. One of the most reliable means of determining driver perception is through eye tracking and gaze concentration. In this study, a set of carefully chosen in-vehicle gaze targets are used to determine driver gaze concentration on/off the road. The time drivers spend looking at these predefined gaze targets has been used to characterise driver visual behaviour at roundabouts during normal driving. In total, 24 drivers completed a 22 km route, including two 5-exit two-lane roundabouts in urban and suburban traffic. This study shows that on the approach to roundabouts, driver gaze direction and gaze concentration was often diverted away from the direction of travel of the vehicle and for some drivers it took a significant amount of time to return their gaze towards the direction of travel of the vehicle. The study is based on the premise that a driver not looking at key regions in a scene cannot perceive the contents of those regions with sufficient detail for safe driving. It is not possible to determine a driver’s level of understanding of a scene, unless the parameters of the scene as well as the driver’s response is determined in real time.

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.

In advanced driver assistance systems (ADAS) or autonomous vehicle research, acquiring semantic information about the surrounding environment generally relies heavily on camera-based object detection. Image signal processors (ISPs) in cameras are generally tuned for human perception. In most cases, ISP parameters are selected subjectively and the resulting image differs depending on the individual who tuned it. While the installation of cameras on cars started as a means of providing a view of the vehicle’s environment to the driver, cameras are increasingly becoming part of safety-critical object detection systems for ADAS. Deep learning-based object detection has become prominent, but the effect of varying the ISP parameters has an unknown performance impact. In this study, we analyze the performance of 14 popular object detection models in the context of changes in the ISP parameters. We consider eight ISP blocks: demosaicing, gamma, denoising, edge enhancement, local tone mapping, saturation, contrast, and hue angle. We investigate two raw datasets, PASCALRAW and a custom raw dataset collected from an advanced driver assistance system (ADAS) perspective. We found that varying from a default ISP degrades the object detection performance and that the models differ in sensitivity to varying ISP parameters. Finally, we propose a novel methodology that increases object detection model robustness via ISP variation data augmentation.