Monitoring the condition of road surfaces can bring benefits to drivers, ranging from increased safety and comfort, to cost reduction. In this context, accelerometer-based approaches offer a cheap and robust solution to the mitigation of common issues that arise from other types of sensors, like cameras, namely computational cost concerns and inability in low visibility scenarios. Nonetheless, as more applications compete for resources in modern vehicles, it becomes imperative to design such applications considering power efficiency constraints (TinyML): to this end, we propose a lightweight machine learning model to detect road irregularities (speed bumps, potholes, and manholes, including the side of impact) using 4 Inertial Measurement Unit (IMU) mounted near each vehicle wheel. Furthermore, to have a real-time monitoring system, we deploy an XGBoost model on a Field-Programmable Gate Array (FPGA) through the use of the Conifer framework, to translate the model using High Level Synthesis (HLS) language. Our solution achieves an accuracy of 88.1%, with 750 microseconds of inference time, showcasing a robust performance in varied scenarios.

Multipath interference poses substantial challenges to global navigation satellite system (GNSS) receivers, leading to inaccuracies in the time of arrival (TOA) measurement of the line of sight (LOS) signal. Therefore, to mitigate the impact of multipath on receivers, the problem has been approached at several system development stages—signal design, reception, and processing. While efforts and advancements have been achieved over the years at each stage seeking navigation robustness, this article focuses on the signal processing stage by presenting a review of advanced multipath mitigation techniques using adaptive channel parameter estimation at the correlation level. The multipath mitigation literature often resorts to optimistic assumptions—high signal-to-noise ratio (SNR), static multipath channel, single fading channel distribution, and so on—while in real-world scenarios noise is prominent, the number of paths and their states vary at different rates, channels are nonstationary, along with other nonideal conditions. Moreover, it is important to directly compare different techniques to characterize their applicability and limitations. An analysis of adaptive algorithms is conducted for multipath mitigation applications.

Integrity monitoring is a key criterion for achieving robust and safe navigation systems. This work explores two integrity frameworks: the classical methods and their respective evolution towards the road vehicle urban scenario, and the artificial intelligence-based methods, where the monitoring process is accomplished by data analysis and learning techniques. In most cases, machine learning outperforms traditional models, which are often observed under controlled, non-real-time conditions, by employing simple algorithms that may have limited success in real-world applications. An overview is provided on how these algorithms have been used, including a comparison of their characteristics and performances, offering insights into how they can evolve and possible future directions to achieve more reliable solutions.

Realistic Global Navigation Satellite System (GNSS) synthetic data is essential for the research and development of vehicular applications, such as Advanced Driver Assistance Systems (ADAS), autonomous driving, and solutions or scenarios that are difficult and expensive to test in the real world, such as vehicular cooperative positioning. However, generating GNSS synthetic data is complex due to satellite dynamics, signal characteristics, and various noise and error sources. This complexity increases in automotive contexts by vehicle movement and environmental factors influencing signal propagation, with multipath effects being particularly challenging to simulate accurately. This paper introduces a novel pipeline that leverages a 3D virtual environment to produce more realistic GNSS synthetic data for automotive applications. The pipeline integrates the CARLA Simulator and GPSoft's SatNav Toolbox for Matlab, with custom-developed modules that generate raw GNSS measurements incorporating environment- and location-specific multipath effects. Our contributions include a tailored simulation pipeline for automotive applications, with integration of GNSS satellite orbits within CARLA, a dynamic multipath model reflecting obstacles in the simulated environment, and a synthetic dataset generated by this approach available to the community. Evaluation on CARLA's Town03 map showed that while standard multipath models result in unrealistic uniform effects, our dynamic model produces effects that correlate with the vehicle's surroundings, accurately reflecting real-world conditions such as increased errors in urban areas and lack of signals in tunnels. This approach can support the research, development, and validation of GNSS positioning algorithms and Artificial Intelligence (AI) model training, with potential applications extending also beyond the automotive context.