Remote Operation is touted as being key to the rapid deployment of automated vehicles. Streaming imagery to control connected vehicles remotely currently requires a reliable, high throughput network connection, which can be limited in real-world remote operation deployments relying on public network infrastructure. This paper investigates how the application of computer vision assisted semantic communication can be used to circumvent data loss and corruption associated with traditional image compression techniques. By encoding the segmentations of detected road users into colour coded highlights within low resolution greyscale imagery, the required data rate can be reduced by 50 % compared with conventional techniques, while maintaining visual clarity. This enables a median glass-to-glass latency of below 200ms even when the network data rate is below 500kbit/s, while clearly outlining salient road users to enhance situational awareness of the remote operator. The approach is demonstrated in an area of variable 4G mobile connectivity using an automated last-mile delivery vehicle. With this technique, the results indicate that large-scale deployment of remotely operated automated vehicles could be possible even on the often constrained public 4G/5G mobile network, providing the potential to expedite the nationwide roll-out of automated vehicles.

Updating the road infrastructure requires the potential mass adoption of the road studs currently used in car detection, speed monitoring, and path marking. Road studs commonly include RF transceivers connecting the buried sensors to an offsite base station for centralized data management. Since traffic monitoring experiments through buried sensors are resource expensive and difficult, the literature detailing it is insufficient and inaccessible due to various strategic reasons. Moreover, as the main RF frequencies adopted for stud communication are either 868/915 MHz or 2.4 GHz, the radio coverage differs, and it is not readily predictable due to the low-power communication in the near proximity of the ground. This work delivers a reference study on low-power RF communication ranging for the two above frequencies up to 60 m. The experimental setup employs successive measurements and repositioning of a base station at three different heights of 0.5, 1 and 1.5 m, and is accompanied by an extensive theoretical analysis of propagation, including line of sight, diffraction, and wall reflection. Enhancing the tutorial value of this work, a correlation analysis using Pearson's coefficient and root mean square error is performed between the field test and simulation results.

Connected and Autonomous Vehicles (CAVs) rely on Vehicular Adhoc Networks with wireless communication between vehicles and roadside infrastructure to support safe operation. However, cybersecurity attacks pose a threat to VANETs and the safe operation of CAVs. This study proposes the use of simulation for modelling typical communication scenarios which may be subject to malicious attacks. The Eclipse MOSAIC simulation framework is used to model two typical road scenarios, including messaging between the vehicles and infrastructure - and both replay and bogus information cybersecurity attacks are introduced. The model demonstrates the impact of these attacks, and provides an open dataset to inform the development of machine learning algorithms to provide anomaly detection and mitigation solutions for enhancing secure communications and safe deployment of CAVs on the road.

The rising popularity of autonomous vehicles has led to the development of driverless racing cars, where the competitive nature of motorsport has the potential to drive innovations in autonomous vehicle technology. The challenge of racing requires the sensors, object detection and vehicle control systems to work together at the highest possible speed and computational efficiency. This paper describes an autonomous driving system for a self-driving racing vehicle application using a modest sensor suite coupled with accessible processing hardware, with an object detection system capable of a frame rate of 25fps, and a mean average precision of 92%. A modelling tool is developed in open-source software for real-time dynamic simulation of the autonomous vehicle and associated sensors, which is fully interchangeable with the real vehicle. The simulator provides performance metrics, which enables accelerated and enhanced quantitative analysis, tuning and optimisation of the autonomous control system algorithms. A design study demonstrates the ability of the simulation to assist in control system parameter tuning - resulting in a 12% reduction in lap time, and an average velocity of 25 km/h - indicating the value of using simulation for the optimisation of multiple parameters in the autonomous control system.

Intersection control has an important role in the management of urban traffic to ensure safety, high traffic flow and to prevent congestion. Recently, a growing body of literature has been reported on the theme of non-signalised intersection control in which traffic lights are replaced with intelligent road side units. Data from several studies suggest that non-signalised control could reduce vehicle delays and fuel consumption significantly whilst ensuring safety. However, there is little published data on the impact of the mixed driving behaviour with human-driven vehicles and autonomous vehicles. This paper investigates the emerging role of connectivity and vehicle autonomy in the context of traffic control under the mixed driving behaviour scenario. The concepts of vehicle-to-infrastructure (V2I) communications and multi-agent systems are central to achieving a robust and reliable traffic-light-free intersection control. Comprehensive computer simulation results on a four-way intersection indicate over 96% reduced average vehicle delay and 37% less fuel consumption with the non-signalised control solution compared to the traffic light control. The outcome of this study offers some important insights into enabling cooperation between vehicles and traffic infrastructure via V2I communications, in order to make more efficient real-time decisions about traffic conditions, whilst ensuring a higher degree of safety.

Heterogeneous wireless networks will play a significant role in providing multiservice connectivity in ITS, and in particular vehicular networks. This paper describes a smart scheduling approach that allows end user nodes to direct packets over the best available wireless access technologies and set priorities for selected services. The performance of this smart scheduler has been simulated in a non-cooperative multi user environment and the results show that, for the prioritised services, the scheduler can provide a lower average packet delay and a higher average packet delivery ratio for all users than a wireless system that selects on signal strength alone.

As the number of vehicles continues to grow, parking spaces are at a premium in city streets. In addition, due to the lack of knowledge about street parking spaces, heuristic circling in the streets not only costs drivers’ time and fuel, but also increases city congestion. In the wake of the recent trend to build convenient, green, and energy-efficient smart cities, common techniques adopted by high-profile smart parking systems are reviewed, and the performance of the various approaches are compared. A mobile sensing unit has been developed as an alternative to the fixed sensing approach. It is mounted on the passenger side of a car to measure the distance from the vehicle to the nearest roadside obstacle. By extracting parked vehicles’ features from the collected trace, a supervised learning algorithm has been developed to estimate roadside parking occupancy. Multiple road tests were conducted around Wheatley (Oxfordshire) and Guildford (Surrey) in the U.K. In the case of accurate GPS readings, enhanced by a map matching technique, the accuracy of the system is above 90%. A quantity estimation model is derived to calculate the density of sensing units required to cover urban streets. The estimation is quantitatively compared with a fixed sensing solution. The results show that the mobile sensing approach can perform at the same level as fixed sensing solutions when accurate location information is available but substantially fewer sensors are needed compared with the fixed sensing system.

Intersection control has an important role in the management of urban traffic to ensure safety, high traffic flow and prevent congestion. Signalised traffic control provides uniform information to all users and this is one of the main reasons for traffic signal’s success to date. These systems generally use fixed position road sensors such as loop detectors, radars and cameras to determine the current traffic state. Therefore, the efficiency of the signalised control mainly depends on the number of sensors deployed. Evidence from research studies indicates that non-signalised intersection control without traffic lights that utilises the advanced features of Connected and Autonomous Vehicles (CAV) could reduce intersection delay and fuel consumption significantly whilst ensuring safety [1]. The proportion of fatalities in road accidents that occurred at intersections was between 34-38% throughout the years 2005-2014 in the UK [2]. The aim of an intersection control system with Connected Vehicles (CV) is to achieve a high degree of cooperation between its users which will lead to a mutually beneficial outcome that reduces the number of accidents and provides lower vehicle delay [3]. This paper examines the emerging role of connectivity in the context of traffic control and proposes a Vehicle-to-Infrastructure (V2I) communications system based on Wireless Access in Vehicular Environments (WAVE) architecture [4] for multi-agent intersection control with CVs. In this system, the vehicle scheduling problem at intersections is considered as a shared resource allocation in which space and time are discretised and allocated to vehicles. The intention is that all approaching vehicle agents plan their intersection crossing trajectory. This is a complex stochastic process that depends not only on vehicular position and kinematic state, but also on the traffic status. These spatial characteristics are reduced to a temporal model to determine intersection crossing time window. The vehicle agents request for intersection crossing as shown in Figure 1, and the intersection manager agent controls vehicles crossing in a conflict-free way. The main objective of this paper is to identify the roles and responsibilities of intersection control agents and vehicle agents, and the communication requirements between these agents within the multi-agent system. The proposed intersection control system was validated through computer simulations on a four-way intersection using the PTV VISSIM traffic simulation tool as shown in Figure 2. The initial results indicate less average vehicle delay and increased throughput with the proposed solution compared to the fixed-time traffic signal control, whilst ensuring safety. More detailed findings will be presented in the conference. In summary, the results of this study offer some important insights into enabling cooperation between vehicles and traffic infrastructure via V2I communications, in order to make more efficient real-time decisions about traffic conditions, whilst ensuring a higher degree of safety.

Reliable wireless communications between vehicles (V2V) and between vehicles and infrastructure (V2I) will play a key role in future transport networks. Where there is overlapping coverage of multiple Radio Access Technologies, with no cooperation between them, a vehicle can use the different technologies simultaneously. This paper proposes an uplink Multi Interface Scheduling System (MISS) located at an intermediate shim layer on the user side, to achieve eÿcient bandwidth aggregation, or lower end-to-end packet delay. MISS aims to find all the available networks that can meet multiple criteria based on user preference and required performance. Simulation results show that safety critical traffic can be prioritized where the resources are insufficient for all the services. Video delivery quality is also improved by prioritizing the most important frames. This algorithm is ideally suited to vehicular networks, where delivery of safety traffic and/or video is an essential requirement.

The Intelligent Transport Systems (ITS) wireless infrastructure needs to support various safety and non-safety services for both autonomous and non-autonomous vehicles.The existing wireless infrastructures can already be used for communicating with different mobile entities at various monetary costs.A packet scheduler, included in a shim layer between the network layer and the medium access (MAC) layer, which is able to schedule packets between uncoordinated Radio Access Technologies (RATs) without modification of the wireless standards, has been devised and its performance evaluated.In this paper, we focus on the influence of mobility type in heterogeneous wireless networks.Three cases are considered based on the mobility in the city: walking, cycling, and driving. Realistic simulations are performed by generating mobility traces of Oxford from Google Maps and overlaying the real locations of existing WiFi Access Points. Results demonstrate that the shim layer approach can accommodate different user profiles and can be a useful abstraction to support Intelligent Transport Systems where there is no coordination between different wireless operators.

As the number of vehicles continues to grow, parking spaces are at a premium in city streets. Additionally, due to the lack of knowledge about street parking spaces, heuristic circling the blocks not only costs drivers' time and fuel, but also increases city congestion. In the wake of recent trend to build convenient, green and energy-efficient smart cities, we rethink common techniques adopted by high-profile smart parking systems, and present a user-engaged (crowdsourcing) and sonar-based prototype to identify urban on-street parking spaces. The prototype includes an ultrasonic sensor, a GPS receiver and associated Arduino micro-controllers. It is mounted on the passenger side of a car to measure the distance from the vehicle to the nearest roadside obstacle. Multiple road tests are conducted around Wheatley, Oxford to gather results and emulate the crowdsourcing approach. By extracting parked vehicles' features from the collected trace, a supervised learning algorithm is developed to estimate roadside parking occupancy and spot illegal parking vehicles. A quantity estimation model is derived to calculate the required number of sensing units to cover urban streets. The estimation is quantitatively compared to a fixed sensing solution. The results show that the crowdsourcing way would need substantially fewer sensors compared to the fixed sensing system.

Automated vehicles will carry computing and communication platforms, and will have enhanced sensing capabilities. Safety around people along with obstacle detection and avoidance systems are key to their success. In controlled environments, automated vehicles can benefit from a remote processing approach to reduce cost and accelerate deployment on larger scales. In this paper we present a section of our intelligent transport systems testbed which evaluates the remote image processing approach with a novel heterogeneous wireless communication system. Hardware implementation is carried out for an experimental evaluation and comparison with the simulation results.

This paper presents a time division multiple access MAC protocol that is specifically designed for applications requiring periodic sensing of the sensor field. Numerical analysis is conducted to investigate the optimum transmission scheduling based on the signal to interference-noise-ratio (SINR) for ground level propagation model applied on wireless chain topology. The optimised transmission schedule considers the SINR value to enable simultaneous transmission from multiple nodes. The most significant advantages of this approach are reduced delay and improve the Packet Received Ratio PRR. Simulation is performed to evaluate the proposed protocol for intelligent transport system applications. The simulation results validate the MAC protocol for a fixed chain topology compared with the similar protocols.

This paper analyses the relative merits of fixed and mobile sensor solutions for monitoring street car parking availability. A fixed sensor solution requires placement of sensors under each allocated parking space. The mobile approach uses a ranger mounted on a moving vehicle which continuously monitors the presence of parked cars and spaces. One of the challenges of the mobile sensor solution is to account for the variability of the position of the vehicle across the road. This paper proposes a dual detector solution using sonar and lidar together with differential detection system to mitigate the uncertainty.

Given the future context of fully integrated Intelligent Transport Systems, reliable wireless communications is a necessity. Traditionally, a single wireless technology is selected for communication but by using heterogeneous wireless communication, advantage can be taken of the different transmission characteristics. This paper proposes a novel Multiple Interface Scheduling Algorithm (MISA) which schedules uplink packets over multiple technologies for which no coordination is required among access networks. The algorithm incorporates a dynamic scoring approach for each of the available networks based on selected system performance parameters. A key feature of the proposed scheduler is that it is located between the IP layer and the MAC layer, hence a single IP address is used, and it is not necessary to modify the wireless standards. A model has been developed to simulate the proposed algorithm and the system performance for three multi radio transmission diversity schemes. The results show the advantage that can be gained in throughput by cooperatively using multiple technologies.

Reliable wireless communications between vehicles (V2V) and between vehicles and infrastructure (V2I) will play a key role in future transport networks. Where there is overlapping coverage of multiple Radio Access Technologies, with no cooperation between them, a vehicle can use the different technologies simultaneously to enhance performance and improve resilience. This paper proposes an uplink Multi Interface Scheduling System (MISS) that incorporates intelligent interface selection, located at an intermediate shim layer on the user side, to achieve efficient bandwidth aggregation, or lower delay. The data can be sent on multiple technologies simultaneously or separately based on user preference and required performance. Rather than finding the best alternative amongst different technologies, this paper's algorithm aims to find all the available networks that can meet multiple criteria. Simulation has been carried out to determine the performance of the system in the presence of a range of different service types. The simulation results show that safety critical traffic can be prioritized in terms of throughput and delay where the resources are insufficient for all the services. This algorithm is ideally suited to vehicular networks, particularly where delivery of safety traffic is an essential requirement.

This paper proposes green energy efficiency metrics for low-power wireless sensors operating at ground level. The metrics are derived from our previous work on energy efficiency analysis for general wireless networks and a radio propagation model for near ground level wireless sensors. A numerical analysis is carried out to investigate the utilization of the green energy efficiency metrics for ground level communication in wireless sensor networks. The proposed metrics have been developed to calculate the optimal sensor deployment, antenna height and energy efficiency level for the near ground wireless sensor. As an application of the proposed metrics, the relationship between the energy efficiency and the spacing between the wireless sensor nodes is studied. The results provide an accurate guidance for energy efficient deployment of near ground level wireless sensors.