  Traffic incidents involving vulnerable road users (VRUs) constitute a
significant proportion of global road accidents. Advances in traffic
communication ecosystems, coupled with sophisticated signal processing and
machine learning techniques, have facilitated the utilization of data from
diverse sensors. Despite these advancements and the availability of extensive
datasets, substantial progress is required to mitigate traffic casualties. This
paper provides a comprehensive survey of state-of-the-art technologies and
methodologies to enhance the safety of VRUs. The study delves into the
communication networks between vehicles and VRUs, emphasizing the integration
of advanced sensors and the availability of relevant datasets. It explores
preprocessing techniques and data fusion methods to enhance sensor data
quality. Furthermore, our study assesses critical simulation environments
essential for developing and testing VRU safety systems. Our research also
highlights recent advances in VRU detection and classification algorithms,
addressing challenges such as variable environmental conditions. Additionally,
we cover cutting-edge research in predicting VRU intentions and behaviors,
which is crucial for proactive collision avoidance strategies. Through this
survey, we aim to provide a comprehensive understanding of the current
landscape of VRU safety technologies, identifying areas of progress and areas
needing further research and development.
