# Empowering Pedestrian Safety: Unveiling a Lightweight Scheme for Improved Vehicle-Pedestrian Safety
## 4 Related Work
[35] propose a method for the early detection of pedestrian movement transitions using smart devices, enabling advanced protection for vulnerable road users (VRUs) by facilitating communication with intelligent vehicles and infrastructure. The study utilizes human activity recognition to categorize pedestrians into four states (waiting, starting, moving, and stopping) and employs machine learning classifiers, including a support-vector machine and XGBoost, for movement transition detection. Empirical studies based on real-world data from 11 test subjects and 79 scenes demonstrate the effectiveness of the proposed method, achieving a robust and early detection of movement transitions with an F1-score of 85%. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Challenge** & **Description** & **Efforts** & **Comments** \\ \hline \multirow{6}{*}{Context Information Exchange} & \multirow{6}{*}{*} & \multirow{6}{*}{Information is valuable for accurate collision detection} & \multirow{6}{*}{*} & \(\bullet\) & The static information is insufficient for accurate collision prediction \\  & & & & (speed, position, and age) & \(\bullet\) & The dynamic information requires complex machine learning and pre-processing \\  & & & & Dynamic information & \(\bullet\) & Additional information is needed such as weather conditions, VRU age, time of day, degree of risk: approaching road, crossing road, near vehicle, VRU distraction level, VRU \\  & & & & & Activity: stopping, walking, crossing curb, surrounding environment: indoor, outdoor, in-vehicle. \\ \hline \multirow{6}{*}{Precise Pedestrian Positioning} & \multirow{6}{*}{*} & \multirow{6}{*}{Pedestrian positioning} & \multirow{6}{*}{*} & There are three types of solutions: smartphone GPS has 3–10 m error due to multipath effect or signal noise and attenuation & \(\bullet\) & Hardware Has better receiver & \(\bullet\) & Filter-based approaches can be used to counteract multipath effects. \\  & & & & & better receiver technology & \(\bullet\) & To eliminate false positives in situations like people crossing the street using an overhead bridge, for example, more study is needed in the field of 3D localization. \\  & & & & & Software: Such as filters (Wavelet, Vondrak) & \(\bullet\) & New parameters like channel-busy ratio, packet delivery ratio, and VRU awareness metrics. \\ \hline \multirow{6}{*}{Network Congestion} & \multirow{6}{*}{*} & \multirow{6}{*}{High number of CAMs mean better safety but greater network load} & \multirow{6}{*}{*} & Optimizing network usage Clustering algorithms. & \(\bullet\) & New parameters like channel-busy ratio, packet delivery ratio, and VRU awareness metrics. \\  & & & & & \(\bullet\) & Multihop clustering algorithm. \\  & & & & & \(\bullet\) & Aggregate local mobility algorithm. \\ \hline \multirow{6}{*}{Energy Efficiency} & \multirow{6}{*}{*} & \multirow{6}{*}{Impact of computation on smartphone battery life.} & \multirow{6}{*}{\(\bullet\)} & Offloading schemes to nearby infrastructure. & \(\bullet\) & Extent of risk determines if computation should be executed locally or offloaded. \\ \cline{1-1} \cline{5-5}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Challenges to V2P system design.