# Advancing Road Infrastructure Safety with the Remotely Piloted Safety Cone
## 1 Introduction  
The construction industry is one of the pillars of the global economy, accounting for 9-15% of Gross Domestic Product in most developed countries and up to 25% in developing countries [1]. This sector has been characterised by a low adoption of automated technologies compared to other industries, such as manufacturing, resulting in persistently low productivity and increased occupational hazards [2, 3]. Despite being historically one of the sectors with the lowest technological adoption, construction today faces significant challenges related to efficiency, sustainability, and occupational safety, which have led to an increasing implementation of automated solutions [3]. The introduction of robotic technologies in this industry aims to increase productivity, reduce labour costs, improve safety conditions, and mitigate adverse environmental effects [2]. These systems offer economicand operational advantages and the possibility of transforming the built environment towards safer and more sustainable schemes [4]. Among the different construction areas, roadworks present a particularly challenging scenario due to the constant exposure of workers to vehicular traffic. Occupational accidents in road maintenance areas constitute one of Europe's most critical occupational safety problems. The occupational fatality rate in construction is significantly higher than the average for other sectors, accounting for up to a quarter of fatal occupational accidents in the EU, with direct exposure to traffic during marking, maintenance, and asphalting being of particular concern [5; 6]. These accidents generate high economic and social costs, including compensation, productivity losses, and cost overruns associated with delays and healthcare [5; 6]. To mitigate these issues, road maintenance automation demands integrating IoT sensors, autonomous navigation vehicles, and robotic technologies with precision positioning and control systems to optimise inspection and repair tasks [7]. Infrastructure such as 5G connectivity and high-visibility road markings is needed to guide Global Navigation Satellite System (GNSS) based navigation systems and computer vision, which are essential for the coordination of autonomous vehicles [8]. Additionally, workers require specialised training in operating autonomous systems and data analytics to manage these advanced technologies effectively [9]. All this must comply with regulations such as Directive 2008/96/EC and Law 37/2015, which regulate the safety and management of road infrastructures [10; 11; 12]. Several autonomous machines coordinate in this automated framework, each performing specific yet interconnected tasks. Such collaboration introduces new challenges for operational safety management, particularly in areas with active traffic and the movement of heavy machinery. Systems capable of real-time monitoring, control, and adaptive delineation of shared workspaces are essential in these dynamic environments, significantly reducing collision risks, task interference, and unauthorised intrusions [13; 14]. Within this context, robotic traffic cones emerge as a relevant technological advancement, directly addressing these operational and safety challenges. By autonomously delimiting work zones, these robotic cones effectively reduce operators' direct exposure to traffic hazards, significantly improving safety conditions on construction sites. Moreover, their capability to dynamically define and control operational areas aligns closely with the European Union's strategic objectives regarding digitalisation, sustainability, and occupational risk prevention [15]. In recent years, research has increasingly explored the robotisation of traffic cones to enhance safety in construction zones. The concept of the smart traffic cone, which utilised GNSS for disturbance detection and alert transmission, was introduced in previous work [16]. The application of reinforcement learning techniques to enable autonomous navigation of simulated cone robots was explored in previous studies [17].