# Empowering Pedestrian Safety: Unveiling a Lightweight Scheme for Improved Vehicle-Pedestrian Safety
## 4 Related Work  
Network congestion, context information sharing, pedestrian placement, and energy efficiency are just some of the issues that might arise while designing a V2P system. This section will detail recent initiatives, which have included both theoretical inquiries and actual answers to these issues. Four methods were presented in [28] to eliminate superfluous warnings to VRUs, helping alleviate network congestion. To facilitate V2P communication in emergency situations, the authors of [29] suggested a dedicated channel paradigm. To further anticipate deployment issues, Ref. [30] provided a technique to estimate CITS performance in dynamic vehicular networks. The success of the algorithms powering the CITS application may be determined using this technique by analyzing actual or predicted traffic volumes. Finally, in [31], the authors tried to put a number on VRU awareness by analyzing the relationship between the sampling frequency of VRU warnings and the tradeoff between VRU safety and network congestion. A novel measure, VRU awareness probability (VAP), was created to quantify the likelihood of detection by neighboring cars based on the flow of signals between operational VRUs. The authors of [32] employed automobiles as anchors in addition to satellites to solve the issue of pedestrian placement, thereby overcoming GPS-related inconsistencies. By monitoring the evolution of CSI over time, they were also able to devise a technique for estimating the propagation angle of a signal. Their solution only needs one antenna on the VRU device (instead of the standard eight) and is more precise. The Kalman filter was used to mitigate GPS errors by the authors of [33]. To examine bikers' behavior without placing real-life participants in danger, researchers in [16] simulated a VANET using data collected from a virtual riding environment. Both Refs. [34; 35] used machine learning to analyze pedestrian activity. In the former, decision tree classifiers were used to differentiate between friendly and hostile pedestrians while connected to a server, whereas four conventional classifying vectors were used when no server was available. The former addressed movement prediction as a classification task, whereas the latter used two distinct classification paradigms: the linear kernel support vector machine and the XG Boost classifier. While the sharing of contextual information is crucial for V2P systems, delays in carrying this out may have a major impact on the effectiveness of collision detection algorithms (CDAs), as shown in [36]. The scientists looked into the link between the two and created a curb detection module to boost the precision of collision detection just as the pedestrian steps off the curb. In [37; 38; 39], multi-access edge computing (MEC) was employed to increase efficiency by computational offloading to cloud servers, weighing the tradeoff between latency and energy usage in context information transmission. These efforts make it possible to utilize a mobile device as an OBU without draining its power supply too quickly. Finally, although VRU privacy and data security are essential issues, they are beyond the scope of this article. We do, however, briefly describe one attempt from [40], in which the authors use machine learning classifiers to provide a framework for the safe processing of massive amounts of data. Figure 9 summarizes the research directions and trends for V2P design. Context information exchange, precise pedestrian positioning, network congestion, and energy efficiency are challenges in designing V2P systems [41]. In Table 2, we describe the different challenges in V2P design. V2P systems can be classified according to several design criteria, contrasting the technologies utilized in V2P systems and design. ACA NLOS techniques, referred to as V2P systems, may be categorized in a variety of ways based on the parameters used. The different design parameters and associated classifications are illustrated in Figure 10.  
These categorizations affect V2P system design.