[1]

China National Bureau of Statistics. 2020. National Statistics Bureau National Data. www.stats.gov.cn.

[2]

Olszewski P, Szagała P, Rabczenko D, Zielińska A. 2019. Investigating safety of vulnerable road users in selected EU countries. Journal of Safety Research 68:49−57

doi: 10.1016/j.jsr.2018.12.001
[3]

Wang YS, Song CP. 2003. Comparison of road traffic safety management between China and foreign countries. Transportation Business China 2:2

doi: 10.16748/j.cnki.cn11-3680/u.2002.03.007
[4]

Yuan Q, Chen H. 2017. Factor comparison of passenger-vehicle to vulnerable road user crashes in Beijing, China. International Journal of Crashworthiness 22:260−70

doi: 10.1080/13588265.2016.1248226
[5]

Yuan Q, Zhai X, Ji W, Yang T, Yu Y, et al. 2022. Correlation analysis on accident injury and risky behavior of vulnerable road users based on Bayesian general ordinal logit model. Sustainability 14(23):16048

doi: 10.3390/su142316048
[6]

Ling J, Fan Y, Hu L, Zhang Y, Cai L, et al. 2020. Visual analysis of 2019 novel coronavirus research hotspots based on CiteSpace and VOSviewer. Chinese Journal of Hospital Infection 30(10):1468−74

[7]

Van Eck NJ, Waltman L. 2010. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84:523−38

doi: 10.1007/s11192-009-0146-3
[8]

de Paulo AF, Porto GS. 2017. Solar energy technologies and open innovation: A study based on bibliometric and social network analysis. Energy Policy 108:228−38

doi: 10.1016/j.enpol.2017.06.007
[9]

Pavel MI, Tan SY, Abdullah A. 2022. Vision-based autonomous vehicle systems based on deep learning: A systematic literature review. Applied Sciences 12(14):6831

doi: 10.3390/app12146831
[10]

Marie-Axelle G, Varet F, Béatrice D, Achot K. 2020. The Role of Context and Perception of Road Rules in the Pedestrian Crossing Decision: A Challenge for the Autonomous Vehicle. International Conference on Applied Human Factors and Ergonomics, AHFE 2019 International Conference on Human Factors and Systems Interaction, July 24–28, 2019, Washington D.C., USA. Cham: Springer. pp. 221–33https://doi.org/10.1007/978-3-030-20040-4_20

[11]

Furuya H, Kim K, Bruder G, Wisniewski PJ, Welch GF. 2021. Autonomous Vehicle Visual Embodiment for Pedestrian Interactions in Crossing Scenarios: Virtual Drivers in AVs for Pedestrian Crossing. CHI '21: CHI Conference on Human Factors in Computing System, Yokohama, Japan, 2021. New York, USA: Association for Computing Machinery. pp. 1–7. https://doi.org/10.1145/3411763.3451626

[12]

Holländer K, Colley A, Mai C, Häkkilä J, Alt F, et al. 2019. Investigating the Influence of External Car Displays on Pedestrians' Crossing Behavior in Virtual Reality. MobileHCI '19: Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services, Taipei, Taiwan, 2019. New York, USA: Association for Computing Machinery. pp. 1-11. https://doi.org/10.1145/3338286.3340138

[13]

Liu H, Chen S, Zheng N, Wang Y; Ge JY, et al. 2022. Ground pedestrian and vehicle detections using imaging environment perception mechanisms and deep learning networks. Electronics 11(12):1873

doi: 10.3390/electronics11121873
[14]

Lee H, Kim SN. 2021. Perceived safety and pedestrian performance in Pedestrian Priority Streets (PPSs) in Seoul, Korea: A virtual reality experiment and trace mapping. International Journal of Environment Research and Public Health 18:5

doi: 10.3390/ijerph18052501
[15]

Liu W, Duan C, Yu B, Chai L, Zhao H. 2015. Multi pose pedestrian detection based on a posteriori HOG feature. Acta Electronica Sinica 43(2):217−24

[16]

Chen L, Ma N, Wang P, Li J, Wang P, et al. 2020. Survey of pedestrian action recognition techniques for autonomous driving. Tsinghua Science and Technology 25(4):458−70

doi: 10.26599/TST.2019.9010018
[17]

Yin H, Chen B, Chai Y, Liu Z. 2016. A Survey of Vision Based Object Detection and Tracking. Acta Automatica Sinica 42(10):1466−89

doi: 10.16383/j.aas.2016.c150823
[18]

Hu C, Liu J, Zhang K, Gao X. 2019. Research on pedestrian and cyclist target detection and tracking algorithm based on depth learning. Automobile Technology 7:19−23

doi: 10.19620/j.cnki.1000-3703.20180628
[19]

Bai Z, Li Z, Jiang B, Wang P. 2019. Real-time pedestrian detection for driving assistance system based on improved YOLOv2 model. Automotive Engineering 41(12):1416−23

[20]

Chen Y, Zhao D, Lv L, Zhang Q. 2018. Multi-task learning for dangerous object detection in autonomous driving. Information Sciences 432:559−71

doi: 10.1016/j.ins.2017.08.035
[21]

Ahmed S, Huda MN, Rajbhandari S, Saha C, Elshaw M, et al. 2019. Pedestrian and cyclist detection and intent estimation for autonomous vehicles: A survey. Applied Sciences 9:2335

doi: 10.3390/app9112335
[22]

Li T, Cao X, Xu Y. 2010. An effective crossing cyclist detection on a moving vehicle. 8th World Congress on Intelligent Control and Automation, 07−09 July 2010, Jinan, China. USA: IEEE. pp. 368−72. https://doi.org/10.1109/WCICA.2010.5554979

[23]

Cho H, Rybski PE, Zhang W. 2010. Vision-based bicyclist detection and tracking for intelligent vehicles. IEEE Intelligent Vehicles Symposium, La Jolla, CA, USA, 21−24 June 2010. USA: IEEE. pp. 454−61. https://doi.org/10.1109/IVS.2010.5548063

[24]

Cho H, Rybski PE, Zhang W. 2010. Vision-based bicycle detection and tracking using a deformable part model and an EKF algorithm. 13th International IEEE Conference on Intelligent Transportation Systems, Funchal, Portugal, 19−22 September 2010. USA: IEEE. pp. 1875−80. https://doi.org/10.1109/ITSC.2010.5624993

[25]

Yang K, Liu C, Zheng JY, Christopher L, Chen YB, et al. 2014. Bicyclist detection in large scale naturalistic driving video. 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), Qingdao, China, 8−11 October 2014. USA: IEEE. pp.1638−43. https://doi.org/10.1109/ITSC.2014.6957928

[26]

Tian W, Lauer M. 2015. Fast cyclist detection by cascaded detector and geometric constraint. IEEE 18th International Conference on Intelligent Transportation Systems, Gran Canaria, Spain, 15−18 September 2015. USA: IEEE. pp. 1286−91. https://doi.org/10.1109/ITSC.2015.211

[27]

Shao Z, Gu Y, Jiang R. 2019. Cycling behavior recognition based on convolutional neural network. Traffic Information and Safety 37(1):72−79

doi: 10.3963/j.issn.1674-4861.2019.01.010
[28]

Krizhevsky A, Sutskever I, Hinton GE. 2012. Imagenet classification with deep convolutional neural networks. Communications of the ACM 60:84−90

[29]

Lin TY, Goyal P, Girshick R, He K, Dollár P, et al. 2019. Focal Loss for Dense Object Detection. IEEE Transactions on Pattern Analysis & Machine Intelligence 42:318−27

doi: 10.1109/TPAMI.2018.2858826
[30]

Lin TY, Dollár P, Girshick R, He K, Hariharan B, et al. 2017. Feature Pyramid Networks for Object Detection. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21−26 July 2017. USA: IEEE. pp. 2117−25. https://doi.org/10.1109/CVPR.2017.106

[31]

Xu Q, Li Y, Wang G. 2019. Pedestrian and vehicle detection based on deep learning. Journal of Jilin University (Engineering and Technology Edition) 49(5):1661−67

doi: 10.13229/j.cnki.jdxbgxb20180642
[32]

Li Z, Zhong T, Zhao Y, Hu Y, Li H, et al. 2019. Pedestrian tracking algorithm for autonomous vehicle driving. Journal of Jilin University (Engineering and Technology Edition) 49(3):680−87

doi: 10.13229/j.cnki.jdxbgxb20180201
[33]

Fan J, Wang L, Chu W, Luo Y. 2019. Research on pedestrian recognition in off-road environment based on KDTree and European clustering. Automotive Engineering 41(12):1410−15

doi: 10.19562/j.chinasae.qcgc.2019.012.009
[34]

Gipps PG, Marksjö B. 1985. A micro-simulation model for pedestrian flows. Mathematics and Computers in Simulation 27(2−3):95−105

doi: 10.1016/0378-4754(85)90027-8
[35]

Matsushita S, Okazaki S. 1991. A study of simulation model for way finding behavior by experiments in mazes. Journal of Architecture, Planning, and Environmental Engineering 429:51−59

doi: 10.3130/aijax.429.0_51
[36]

Kwak JY, Ko BC, Nam JY. 2017. Pedestrian intention prediction based on dynamic fuzzy automata for vehicle driving at nighttime. Infrared Physics & Technology 81:41−51

doi: 10.1016/J.INFRARED.2016.12.014
[37]

Ouni F, Belloumi M. 2018. Spatio-temporal pattern of vulnerable road user’s collisions hot spots and related risk factors for injury severity in Tunisia. Transportation Research Part F: Psychology and Behaviour477−95

[38]

Koehler S, Goldhammer M, Bauer S, Zecha S, Doll K et al. 2013. Stationary detection of the pedestrian? s intention at intersections IEEE Intelligent Transportation Systems Magazine 5(4):87−99

doi: 10.1109/MITS.2013.2276939
[39]

Robbins CJ, Chapman P. 2018. Drivers' visual search behavior toward vulnerable road users at junctions as a function of cycling experience. Human Factors 60:889−901

doi: 10.1177/0018720818778960
[40]

Keller CG, Hermes C, Gavrila DM. 2011. Will the pedestrian cross? Probabilistic path prediction based on learned motion features. Joint Pattern Recognition Symposium, 33rd DAGM Symposium: Pattern Recognition, Frankfurt/Main, Germany, August 31 - September 2, 2011. vol 6835. Heidelberg: Springer, Berlin. pp. 386–95. https://doi.org/10.1007/978-3-642-23123-0_39

[41]

Keller CG, Gavrila DM. 2014. Will the pedestrian cross? A study on pedestrian path prediction IEEE Transactions on Intelligent Transportation Systems 15(2):494−506

doi: 10.1109/TITS.2013.2280766
[42]

Vilaça M, Macedo E, Tafidis P, Coelho MC. 2019. Multinomial logistic regression for prediction of vulnerable road users risk injuries based on spatial and temporal assessment. International Journal of Injury Control and Safety Promotion 26:379−90

doi: 10.1080/17457300.2019.1645185
[43]

Quintero R, Almeida J, Llorca DF, Sotelo MA. 2014. Pedestrian path prediction using body language traits. 2014 IEEE Intelligent Vehicles Symposium Proceedings, Dearborn, MI, USA, 08−11 June 2014. USA: IEEE. pp. 317−23. https://doi.org/10.1109/IVS.2014.6856498

[44]

Kooij JFP, Schneider N, Flohr F, Gavrila DM. 2014. Context-Based Pedestrian Path Prediction. Computer Vision – ECCV 2014. 13th European Conference, Proceedings, Part VI, Zurich, Switzerland, September 6-12, 2014. 8694: 618–33. Switzerland: Springer, Cham. https://doi.org/10.1007/978-3-319-10599-4_40

[45]

Jia Y, Cebon D. 2018. A strategy for avoiding collisions between heavy goods vehicles and cyclists. Proceedings of the Institution of Mechanical Engineers Part D: Journal of Automobile Engineering, 233(6):1367−79

doi: 10.1177/0954407018781671
[46]

Rehder E, Kloeden H. 2015. Goal-directed pedestrian prediction. 2015 IEEE International Conference on Computer Vision Workshop (ICCVW), Santiago, Chile, 7−13 December 2015. USA: IEEE. pp. 50−58. https://doi.org/10.1109/ICCVW.2015.28

[47]

Karasev V, Ayvaci A, Heisele B, Soatto S. 2016. Intent-aware long-term prediction of pedestrian motion. 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, 16−21 May 2016. USA: IEEE. pp. 2543−49. https://doi.org/10.1109/ICRA.2016.7487409

[48]

Si X, Yuan Q. 2022. Automated Driving Simulation Platform Design on Collision Avoidance Decision Making for Vulnerable Road Users. Man-Machine-Environment System Engineering: Proceedings of the 21st International Conference on MMESE, Beijing, October 23–25, 2021. Singapore: Springer. pp. 787–91. https://doi.org/10.1007/978-981-16-5963-8_107

[49]

Bandyopadhyay T, Jie CZ, Hsu D, Ang JMH, RusD, et al. 2013. Intention-Aware Pedestrian Avoidance. In Experimental Robotics, eds. Desai J, Dudek G, Khatib O, Kumar V. Switzerland: Springer Cham. pp. 963–77. https://doi.org/10.1007/978-3-319-00065-7_64

[50]

Yuan Q, Gao Y, Li Y. 2016. Suppose Future Traffic Accidents Based on Development of Self-driving Vehicles. International Conference on Man-Machine-Environment System Engineering (MMESE 2016), Xi'an, China, October 21−23, 2016. 406: XXII,635. Singapore: Springer. pp 253–61. https://doi.org/10.1007/978-981-10-2323-1_28

[51]

Vourgidis I, Maglaras L, Alfakeeh AS, Al-Bayatti AH, Ferrag MA. 2020. Use of smartphones for ensuring vulnerable road user safety through path prediction and early warning: an in-depth review of capabilities, limitations and their applications in cooperative intelligent transport systems. Sensors 20(4):997

doi: 10.3390/s20040997
[52]

Zhang Y, Liu S, Qiu Z, Yao D, Peng L. 2012. Analysis of pedestrian vehicle conflict parameters and safety evaluation. Journal of Harbin Institute of Technology 44(12):123−28

doi: 10.11918/j.issn.0367-6234.2012.12.022
[53]

Nakaoka M, Raksincharoensak P, Nagai M. 2008. Study on forward collision warning system adapted to driver characteristics and road environment. 2008 International Conference on Control, Automation and Systems, Seoul, Korea (South), 14−17 October 2008. USA: IEEE. pp. 2890−95. https://doi.org/10.1109/ICCAS.2008.4694250

[54]

Matsumi R, Raksincharoensak P, Nagai M. 2013. Autonomous braking control system for pedestrian collision avoidance by using potential field. IFAC Proceedings Volumes 46(21):328−34

doi: 10.3182/20130904-4-JP-2042.00064
[55]

Raksincharoensak P, Akamatsu Y, Moro K, Nagai M. 2013. Predictive braking assistance system for intersection safety based on risk potential. IFAC Proceedings Volumes 46(21):335−40

doi: 10.3182/20130904-4-JP-2042.00072
[56]

Abdul Hamid UZ, Zamzuri H, Yamada T, Abdul Rahman MA, Saito Y, et al. 2018. Modular design of artificial potential field and nonlinear model predictive control for a vehicle collision avoidance system with move blocking strategy. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 232(10):1353−73

doi: 10.1177/0954407017729057
[57]

Kovaceva J, Bálint A, Schindler R, Schneider A. 2020. Safety benefit assessment of autonomous emergency braking and steering systems for the protection of cyclists and pedestrians based on a combination of computer simulation and real-world test results. Accident Analysis and Prevention 136:105352

doi: 10.1016/j.aap.2019.105352
[58]

Ma G, Li Y, Guo Z, Yang L. 2013. Driving behavior hazard identification method. Highway Traffic Science and Technology 30(7):113−18

[59]

Yu F. 2016. Research on collision prediction system based on driver's handling behavior characteristics. Thesis. Hunan University, China.

[60]

Wu X, Miucic R, Yang S, Al-Stouhi S, Misener J, et al. 2014. Cars talk to phones: A DSRC based vehicle-pedestrian safety system. 2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall), Vancouver, BC, Canada, 14−17 September 2014. USA: IEEE. pp. 1−7. https://doi.org/10.1109/VTCFall.2014.6965898

[61]

Zhu S, Yu T, Li J. 2018. Traffic safety warning at intersections based on machine vision and information sharing. Journal of Automotive Safety and Energy Conservation 9(2):156−63

doi: 10.3969/j.issn.1674-8484.2018.02.005
[62]

Yang W, Zhao H, Shu H. 2019. Pedestrian collision avoidance strategy and simulation verification of automatic emergency braking system. Journal of Chongqing University (Natural Science Edition) 42(02):1−10

[63]

Bastani Zadeh R, Ghatee M. 2018. Three-phases smartphone-based warning system to protect vulnerable road users under fuzzy conditions. IEEE Transactions on Intelligent Transportation Systems 19(7):2086−98

doi: 10.1109/TITS.2017.2743709
[64]

Wu W, Chen R, Ma F, Jia H. 2019. Driving Methods for Autonomous Collision Avoidance of Vehicles against Pedestrian Speed Obstacles. Journal of Harbin University of Technology 51(09):74−80

doi: 10.11918/j.issn.0367-6234.201804180
[65]

Nguyen QH, Morold M, David K, Dressler F. 2020. Car-to-Pedestrian communication with MEC-support for adaptive safety of Vulnerable Road Users. Computer Communications 150:83−93

doi: 10.1016/j.comcom.2019.10.033
[66]

Wang H. 2019. Research on the impact of shared bicycle and shared car on urban traffic. Journal of Taiyuan Urban Vocational College 2019(5):4

doi: 10.3969/j.issn.1673-0046.2019.05.003
[67]

Li KQ, Xiong H, Liu JX, Xu Q, Wang JQ. 2022. Real-Time monocular joint perception network for autonomous driving. IEEE Transactions on Intelligent Transportation Systems 23(9):15864−77

doi: 10.1109/TITS.2022.3146087
[68]

Li K, Xiong H, Liu J. 2022. Multiple object motion trajectory prediction for Vulnerable Road User. China Journal of Highway and Transport 35(1):298−315

doi: 10.3969/j.issn.1001-7372.2022.01.026