W/CDM-MSFM-driven pedestrian path prediction at signalized crosswalk with mixed trafﬁc flow

Chen, Hao; Zhang, Xi; Yang, Wenyan; Jin, Wenqiang; Zhu, Wangwang; Zhao, Baixuan

doi:10.1080/21680566.2020.1828197

Cited by 5 publications

(1 citation statement)

References 34 publications

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“…OR "automated driving" OR "connected autonomous vehicle*" OR "CAVs") AND ("mix* traffic*" OR "heterogeneous traffic*" OR "mix* traffic flow")) AND 2-s2.0-85101020669 Modeling pedestrians road crossing [90] 2-s2.0-85099345593 Review paper [91] 2-s2.0-85092091552 Pedestrian path prediction at signalized crosswalk [92] 2-s2.0-85106318730 Key focus was connected buses and energy consumption [93] 2-s2.0-85097173824 Survey-based study about user perceptions [94] 2-s2.0-85093119962 User perception based on autonomous shuttle rides-user survey [95] 2-s2.0-85086721434 User perception based on autonomous shuttle rides-user survey [96] 2-s2.0-85076820374 Estimating pedestrian's waiting time at unsignalized mid-block crosswalks [97] 2-s2.0-85074453871 Pedestrians' perspective [98] 2-s2.0-85069684311 Vehicle-pedestrian encountering [99] 2-s2.0-85031293900 Review paper discussing future perspectives of AVs [100] 2-s2.0-85106204730 Prior focus was communication from in-vehicle and from infrastructure signage.…”

Section: Figurementioning

confidence: 99%

Autonomous Vehicles in Mixed Traffic Conditions—A Bibliometric Analysis

2022

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Autonomous Vehicles (AVs) with their immaculate sensing and navigating capabilities are expected to revolutionize urban mobility. Despite the expected benefits, this emerging technology has certain implications pertaining to their deployment in mixed traffic streams, owing to different driving logics than Human-driven Vehicles (HVs). Many researchers have been working to devise a sustainable urban transport system by considering the operational and safety aspects of mixed traffic during the transition phase. However, limited scholarly attention has been devoted to mapping an overview of this research area. This paper attempts to map the state of the art of scientific production about autonomous vehicles in mixed traffic conditions, using a bibliometric analysis of 374 documents extracted from the Scopus database from 1999 to 2021. The VOSviewer 1.1.18 and Biblioshiny 3.1 software were used to demonstrate the progress status of the publications concerned. The analysis revealed that the number of publications has continuously increased during the last five years. The text analysis showed that the author keywords “autonomous vehicles” and “mixed traffic” dominated the other author keywords because of their frequent occurrence. From thematic analysis, three research stages associated with AVs were identified; pre-development (1999–2017), development (2017–2020) and deployment (2021). The study highlighted the potential research areas, such as involvement of autonomous vehicles in transportation planning, interaction between autonomous vehicles and human driven vehicles, traffic and energy efficiencies associated with automated driving, penetration rates for autonomous vehicles in mixed traffic scenarios, and safe and efficient operation of autonomous vehicles in mixed traffic environment. Additionally, discussion on the three key aspects was conducted, including the impacts of AVs, their driving characteristics and strategies for their successful deployment in context of mixed traffic. This paper provides ample future directions to the people willing to work in this area of autonomous vehicles in mixed traffic conditions. The study also revealed current trends as well as potential future hotspots in the area of autonomous vehicles in mixed traffic.

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