# Advancing Road Infrastructure Safety with the Remotely Piloted Safety Cone
## 4 Discussion  
This review of the state of the art reveals a clear trend in current work towards demonstrating multi-agent navigation, control, and formation algorithms using mobile platforms that integrate a traffic cone on their upper part. However, in most cases, the development focuses on the platform's autonomous movement without addressing the  
Figure 16: Unfiltered distances and distances filtered using a Kalman filter. Figure 17: Distance measured by the RPSC’s obstacle detector while navigating, showing a temporary reversal when encountering a person before resuming its path. structural, regulatory, and safety requirements that would allow such a system to be considered a fully functional robotic traffic cone. Cone formation systems have employed commercial autonomous vacuum cleaners, such as the iRobot Roomba 600 series, as mobile bases [18; 26]. This robot was originally designed for indoor domestic environments and is not prepared to operate on uneven terrain such as asphalt, gravel, or construction zones. Furthermore, its structure has exposed elements, such as power and communication wiring, representing a risk of improper manipulation. The system lacks safety mechanisms that would allow for its deactivation in the event of failure or external intervention, and it does not comply with European road regulations, such as UNECE R65 [23] or UNE-EN 13422:2019 [22], which renders its deployment in real-world environments impossible. An autonomous positioning system using computer vision, based on lane line detection through image segmentation and Hough transforms, was proposed in [21]. Although this approach enables the validation of navigation algorithms, it does not account for real-world construction scenarios where road markings may be absent or altered, as is often the case. Additionally, the platform incorporates a small cone, similar to those used in sports training, which does not meet the visibility, stability, and structural resistance requirements for road signage. Neither an absolute positioning system nor active safety measures are included. Other studies have focused on predictive control and formation tracking using robust mathematical models [20; 26]. However, these approaches are mainly validated in simulated or laboratory environments without considering the integration of high-precision GNSS sensors, incorporating redundant safety mechanisms, or complying with active signaling regulations. Likewise, the structure of the developed prototypes remains based on generic mobile platforms, on which a cone is mounted, without designing an integral element that could be approved as a signalling device. An approach based on reinforcement learning has been proposed, in which a simulated agent is trained to achieve objectives within a virtual environment [17]. While this work offers interesting autonomous control methodologies, it lacks validation under real-world conditions and does not address visibility, structural safety, or signalling regulations. Initial proposals introducing bright traffic cones with GNSS-based detection and alert capabilities have remained focused on passive and static functionalities, without addressing mobility, environmental interaction, or integration with autonomous systems [16]. Collectively, these works have contributed to validating navigation, tracking, or autonomous learning algorithms using mobile structures that carry cones; however, they have not addressed the fundamental problem of designing and implementing a robotic traffic cone that can operate autonomously, accurately, and safely in real-world road environments. The approach presented in this work begins with a fundamentally different conception: to develop a functional and autonomous robotic traffic cone.