A review on the estimation of vulnerable road user behavior for automated vehicles

In recent years, the development of automated support systems for vehicles, focusing on enhancing digital traffic facilities through new methods and techniques has gained immense popularity. However, in real-life scenarios, the developed automated support systems are not completely compatible in terms of ensuring safety, effectiveness, and reliability. A promising number of research have emphasized the importance of integrating vulnerable road users’ (VRUs) behavioral aspects into automated vehicle systems which might help to improve safety, effectiveness, reliability, and community acceptance. Addressing this research focus, this paper presents a comprehensive review focusing on VRU behavior analysis for automated vehicles, specifically examining studies related to VRUs’ behavior aspects. We reviewed 73 studies addressing one research question: What aspects of automated vehicles need attention to improve their safety for VRUs? This review highlights and represents the findings focusing on three major groups: (I) VRUs crossing intention prediction, (II) VRUs motion prediction, and (III) VRUs path/trajectory prediction, concentrating on ten key aspects: (i) Improvement of VRUs context understanding, (ii) Early phase detection and prediction, (iii) Computational/Processing time, (iv) Consistent/Continuous prediction, (v) Interactive navigation, (vi) Semanticity, (vii) Low-scale dataset, (viii) Long-term prediction, (ix) Real-time prediction, and (x) Explainability improvement. Besides, it also addresses several challenges, such as difficulties in observing multiple scenarios, understanding contextual information, and detecting group behavior, which is crucial for enhancing the reliability and acceptability of automated vehicles in future research. The findings of this review are significant to provide new insights and directions for developing driving support systems for automated vehicles by integrating VRUs behavior.