# Integration and Field Evaluation of an IoV System for Enhancing Road Safety
## 1 Introduction  
Road safety is a primary global concern, as the World Health Organization reports that approximately 1.3 million people die each year as a result of road traffic crashes [1]. For the European Union (EU) alone, the number of vehicle accidents that resulted in death before 2020 was constantly over 20,000, with the European Commission establishing its 'Vision Zero' plan, an attempt to virtually eliminate traffic fatalities and serious injuries by 2050. Towards materializing its long-term goal, the EU established road safety guidelines dating back to 2010, aiming to reduce European road deaths by 50% by 2020 [2]. Even though significant progress has been made towards this goal with the total number of road traffic-related fatalities constantly decreasing since 2010, in 2017 the EU stated that reaching the objective of zero road fatalities by 2050 will be very challenging. The EU also acknowledged that the persistently high number of road traffic fatalities and serious injuries is a major societal problem, causing human suffering and unacceptable economic costs [3]. The number of people who died in road accidents in the EU in 2020 was 18,786, of which 44% were passenger car occupants and 16% were motorcycle occupants. While this constitutes a decrease in the number of people killed in road traffic accidents in the EUby 17% compared with 2019, this drop is largely due to restrictive measures on passenger transport due to the impact of COVID-19 [4]. Both the World Health Organization and the EU identify the lack of efficient speed control as one of the most important causes of road traffic accidents [1; 5]. Towards improving road safety, Internet of Vehicles (IoV) provides various technological solutions, employing a wide range of sensors, including LIDAR and ultrasonic sensors for collision avoidance [6], video or image-capturing sensors [7; 8; 9], radar sensors [10], inductive loops, and magnetic sensors [11]. Sensors data combined with communication systems for effective communication between vehicles (Vehicle-to-Vehicle or V2V communication), as well as between vehicles and infrastructure (Vehicle-to-Infrastructure or V2I communication) create effective IoV solutions for road safety [12; 13]. Due to the nature of each situation that each IoV system addresses, different communication systems and protocols can be employed, to better serve the target application [14]. In addition to novel sensors networks and communication systems, data analytics and data processing algorithms, often running on the edge of IoV networks, are being employed for effective and quick systems response [7] and/or early warning of the end users, often based on data related to the drivers' behavior, prior violation and accident records [15]. Cooperative Intelligent Transport Systems (C-ITS), is a category of Intelligent Transport Systems, made possible due to IoV. They enable communication between sensor systems, existing infrastructure and a variety of end users, ranging from the drivers themselves to traffic management center (TMC) operators, providing increased road safety and in-time detection of road infrastructure critical deficiencies [16]. However, at times, their integration with the existing infrastructure and/or their installation is associated with a rather high cost, ultimately undermining their use [17]. Because of that, numerous recent pieces of research focus on low-cost IoV systems, often employing roadside units with various types of MEMS sensors that are both affordable to produce in large quantities and energy efficient, making the overall solution more cost efficient [18; 19]. Among these systems, those employing magnetometers seem to be very popular, with a comprehensive review provided in [20]. Other systems employ artificial intelligence and power autonomous systems in order to effectively adjust the processing power on the edge and distribute it among various units to achieve power efficiency and lower operational cost [21].