Enhancing VRU Awareness through TTC-Based Trajectory Interception Probability for VAM Triggering

Over the past century, transportation technologies have evolved rapidly due to breakthroughs in automation, vehicle connectivity, and the rise of cooperative systems. These innovations are reshaping how vehicles, infrastructure, and road users interact, moving toward shared situational awareness and coordinated safety. In this context, protecting Vulnerable Road Users (VRUs, including pedestrians, cyclists, powered two-wheelers, and other unprotected participants, has become a priority, as they face a higher risk of injury or death in traffic accidents, making their safety a crucial objective for modern intelligent transportation systems. To address this challenge, the European Telecommunications Standards Institute (ETSI) has developed the VRU Basic Service (VBS) and the VRU Awareness Message (VAM) to enable VRUs to be consistently represented within Cooperative-ITS environments. VAM generation is regulated by a hybrid mechanism: it combines periodic transmission and seven event-driven triggering conditions, avoiding network overload. Among these conditions, the Trajectory Interception Probability (TIP) trigger is one of the most challenging to implement: indeed, while ETSI specifies that a VAM must be sent when the TIP changes by at least 10\% compared to the last transmission, it does not provide a computation method, leaving a gap available for research and studies to be filled. This thesis tackles this gap by developing and integrating an innovative method to compute TIP in real time, beginning from the Time-to-Collision (TTC) metric. Firstly, to ensure the focus is on actual safety-critical situations, the TTC domain is limited to a relevant temporal horizon, using minimum and maximum thresholds. Within the bounded range, several TTC to TIP mapping strategies were compared, including fixed intervals, linear and exponential functions. After a comparative analysis, the solution was to use a discrete exponential mapping: this function leads to an increase in collision probability more sharply as TTC decreases, reflecting the rapidly growing urgency of an imminent collision, while avoiding sudden jumps that could lead to unnecessary message triggering. Its smooth yet responsive behavior ensures compliance with ETSI’s standard requirements, achieving a robust and efficient implementation of the triggering condition. The proposed methodology, implemented within the VaN3Twin framework, starts from computing TTC from kinematic state variables, maps it to TIP through the chosen exponential function, and triggers a new VAM whenever the ETSI-defined condition is met, enabling a continuous frame-by-frame assessment of collision risk. The proposed approach was validated through a set of simulation scenarios designed to reproduce realistic interactions between vehicles and pedestrians. These scenarios were first used to identify the key parameters required by the chosen mapping function, such as the temporal evaluation window and the growth rate of the probability curve. Once defined, the calibrated parameters were applied again to the same scenarios to analyse how the introduction of the TIP-based condition influences VAM triggering behaviour. This two-step process allowed both the tuning of the method and the assessment of its impact on message generation, showing that the new condition ensures timely yet non-redundant transmissions an triggering conditions.