Positioning and navigation are critical functions of automated driving functions, which help autonomous vehicles determine their absolute and relative positions in the environment that they operate. Integrity Monitoring (IM) systems, which are intended to assess the reliability and trustworthiness of the information provided by the navigation systems, are crucial for ensuring the safety of automated driving functions. This paper provides a comprehensive review of the existing IM frameworks for safety-critical navigation applications and expands on the state-of-the-art of the most recent development of such systems for connected automated vehicles. We mainly focus on IM methods for Global Navigation Satellite Systems (GNSS) and Inertial Navigation Systems (INS). However, we also cover IM for map assisted and wireless signal augmented navigation systems, which are promising for high-performance navigation applications, such as automated driving functions. For each main category of solutions, key aspects such as the characteristics of measurement errors and faults related to various data sources are discussed to provide deeper insights into designing of reliable IM systems. Also, some of the major open research challenges to the best knowledge of the authors have been identified and discussed.

This paper investigates a tightly-coupled Global Position System (GPS)/Ultra-Wideband (UWB)/Inertial Navigation System (INS) cooperative positioning scheme using a Robust Kalman Filter (RKF) supported by V2I communication. The scheme proposes a method that uses range measurements of UWB units transmitted among the terminals as augmentation inputs of the observations. The UWB range inputs are used to reform the GPS observation equations that consist of pseudo-range and Doppler measurements and the updated observation equation is processed in a tightly-coupled GPS/UWB/INS integrated positioning equation using an adaptive Robust Kalman Filter. The result of the trial conducted on the roof of the Nottingham Geospatial Institute (NGI) at the University of Nottingham shows that the integrated solution provides better accuracy and improves the availability of the system in GPS denied environments. RKF can eliminate the effects of gross errors. Additionally, the internal and external reliabilities of the system are enhanced when the UWB observables received from the moving terminals are involved in the positioning algorithm.

The accurate position is a key requirement for autonomous vehicles. Although Global Navigation Satellite Systems (GNSS) are widely used in many applications, their performance is often disturbed, particularly in urban areas. Therefore, many studies consider multi-sensor integration and cooperative positioning (CP) approaches to provide additional degrees of freedom to address the shortcomings of GNSS. However, few studies adopted real-world datasets and internode ranging outliers within CP is left untouched, leading to unexpected challenges in practical applications. To address this, we propose a Robust Cooperative Positioning (RCP) scheme that augments the GPS with the Ultra-Wideband (UWB) system. A field experiment is conducted to generate a real-world dataset to evaluate the RCP scheme. Moreover, the analysis of the collected dataset enables us to optimise a simple but effective Robust Kalman Filter (RKF) to mitigate the influence of outlier measurements and improve the robustness of the proposed solution. Finally, a simulated dataset is derived from the real-world data to comprehensively study the performance of the proposed RCP method in urban canyon scenarios. Our results demonstrate that the proposed solution can crucially improve positioning performance when the number of visible GPS satellite is limited and is robust against various adverse effects in such environments.

Detection of estrus is a key determinant of profitability of dairy herds, but estrus is increasingly difficult to observe in the modern dairy cow with shorter duration and less-intense estrus. Concurrent with the unfavorable correlation between milk yield and fertility, estrus-detection rates have declined to less than 50%. We tested ultra-wideband (UWB) radio technology (Thales Research & Technology Ltd., Reading, UK) for proof of concept that estrus could be detected in dairy cows (two 1-wk-long trials; n=16 cows, 8 in each test). The 3-dimensional positions of 12 cows with synchronized estrous cycles and 4 pregnant control cows were monitored continuously using UWB mobile units operating within a network of 8 base units for a period of 7d. In the study, 10 cows exhibited estrus as confirmed by visual observation, activity monitoring, and milk progesterone concentrations. Automated software was developed for analysis of UWB data to detect cows in estrus and report the onset of estrus in real time. The UWB technology accurately detected 9 out of 10 cows in estrus. In addition, UWB technology accurately confirmed all 6 cows not in estrus. In conclusion, UWB technology can accurately detect estrus and hence we have demonstrated proof of concept for a novel technology that has significant potential to improve estrus-detection rates.

While automotive original equipment manufacturers and IT companies are developing and demonstrating self-driving cars, true autonomy will not be realised in the near future due in part to the technology readiness level of the existing systems as well as issues of ethics, security, governance and standards surrounding the implementation of autonomy for road transport. However, advances in cellular phones and networks, satellite-based positioning and communications, cloud computing, combined with a rise in the volumes of available data, allied with a reduction in their costs, offer the very real possibility of connecting vehicles, one to another and to smart city infrastructure as part of the Internet of Things (IoT). Data from connected vehicles, when combined with other information, may provide valuable intelligence to traffic managers and other stakeholders via cooperative intelligent transport system (C-ITS) platforms. Nevertheless, many issues face the implementation of a truly connected IoT in general and C-ITS in particular.

For Connected and Autonomous Vehicle (CAV) applications, the location solution is desired to provide better than 0.1m real-time positioning accuracy. This level of accuracy can only be achieved by using geodetic GNSS receivers under an open sky observation condition, and each unit costs around £20,000. This kind of geodetic GNSS receiver is not a good option for mass market use in terms of price and ubiquity aspects. Therefore, using low-cost receiver to achieve real-time, high accuracy and ubiquitous positioning performance could be a future trend. This paper will first establish a framework of assessing low-cost receivers based on required navigation performance (RNP) concept in aviation and required accuracy categories in ITS. Then adynamic test that was conducted to simulate the future CAV driving environment will be introduced. Under the guidance of the former established framework, the collected data was post-processed to explore the real positioning performance of both two grades receivers. By comparing real-time/post-processed results and high-end/low-cost receivers, the limitations and technical gaps between two types of receivers, as well as current positioning solution and required positioning performance will be identified.