Pedestrian safety is a critical concern in urban environments, particularly with the increasing presence of automated vehicles (AVs) on the roads. Because of the unpredictable movement of pedestrians, a significant challenge lies in the limited understanding of factors contributing to pedestrian–AV collisions. This study addresses this gap by analyzing pedestrian crashes involving AVs using association rules mining (ARM). Data, including crash reports from the California Department of Motor Vehicles, comprised 46 pedestrian crashes involving AVs, categorized by precrash mode: autonomous mode (24 crashes) and conventional mode (22 crashes). The ARM algorithm was employed to uncover significant relationships and patterns in the crash data. A total of 67 association rules were generated across three distinct scenarios—intersections, within 150 ft of intersections, and midblock locations—revealing key associations between factors such as time of day, location, vehicle and pedestrian behavior, and environmental conditions. The study’s findings offer valuable insights into pedestrian safety in the context of precrash modes of AVs and provide important guidance for developing targeted safety measures and policies to reduce pedestrian–AV collisions.
HuW.CicchinoJ. B.An Examination of the Increases in Pedestrian Motor-Vehicle Crash Fatalities during 2009–2016. Journal of Safety Research, Vol. 67, 2018, pp. 37–44. https://doi.org/10.1016/j.jsr.2018.09.009.
3.
PlazaL.Collision Between Vehicle Controlled by Developmental Automated Driving System and Pedestrian, Tempe, Arizona, 2018.
4.
LiuQ.WangX.WuX.GlaserY.HeL.Crash Comparison of Autonomous and Conventional Vehicles Using Pre-Crash Scenario Typology. Accident Analysis & Prevention, Vol. 159, 2021, p. 106281. https://doi.org/10.1016/j.aap.2021.106281.
5.
RezwanaS.LownesN.Interactions and Behaviors of Pedestrians with Autonomous Vehicles: A Synthesis. Future Transportation, Vol. 4, No. 3, 2024, pp. 722–745. https://doi.org/10.3390/futuretransp4030034.
6.
GalvaoL. G.AbbodM.KalganovaT.PaladeV.HudaM. N.Pedestrian and Vehicle Detection in Autonomous Vehicle Perception Systems—A Review. Sensors, Vol. 21, No. 21, 2021, p. 7267.
7.
RageshN. K.RajeshR.Pedestrian Detection in Automotive Safety: Understanding State-of-the-Art. IEEE Access, Vol. 7, 2019, pp. 47864–47890.
8.
ZhangY.HeH.LiJ.LiY.SeeJ.LinW.Variational Pedestrian Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2021, pp. 11617–11626.
9.
NavarroP. J.FernandezC.BorrazR.AlonsoD.A Machine Learning Approach to Pedestrian Detection for Autonomous Vehicles Using High-Definition 3D Range Data. Sensors, Vol. 17, No. 1, 2016, p. 18.
10.
ZhangH.PopD. O.RogozanA.BensrhairA.Accelerate High Resolution Image Pedestrian Detection with Non-Pedestrian Area Estimation. IEEE Access, Vol. 9, 2021, pp. 8625–8636.
11.
CamaraF.BellottoN.CosarS.NathanaelD.AlthoffM.WuJ.RuenzJ.DietrichA.FoxC. W.Pedestrian Models for Autonomous Driving Part I: Low-Level Models, from Sensing to Tracking. IEEE Transactions on Intelligent Transportation Systems, Vol. 22, No. 10, 2020, pp. 6131–6151.
BrarJ. S.CaulfieldB.Impact of Autonomous Vehicles on Pedestrians’ Safety. Presented at the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Piscataway, NJ, 2017.
14.
ReigS.NormanS.MoralesC. G.DasS.SteinfeldA.ForlizziJ.A Field Study of Pedestrians and Autonomous Vehicles. New York, NY, USA, 2018.
15.
SusilawatiS.WongW. J.PangZ. J.Safety Effectiveness of Autonomous Vehicles and Connected Autonomous Vehicles in Reducing Pedestrian Crashes. Transportation Research Record: Journal of the Transportation Research Board, 2023. 2677: 1605–1618. https://doi.org/10.1177/03611981221108984.
16.
UtriainenR.PöllänenM.Prioritizing Safety or Traffic Flow? Qualitative Study on Highly Automated Vehicles’ Potential to Prevent Pedestrian Crashes with Two Different Ambitions. Sustainability, Vol. 12, No. 8, 2020, p. 3206. https://doi.org/10.3390/su12083206.
17.
TrumppR.BayerleinH.GesbertD.Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance. Presented at the 2022 IEEE Intelligent Vehicles Symposium (IV), Piscataway, NJ, 2022.
18.
KohliP.ChadhaA.Enabling Pedestrian Safety Using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-Driving Car Crash. Advances in Information and Communication: Proceedings of the 2019 Future of Information and Communication Conference (FICC), Springer, Cham, Vol. 1, 2020, pp. 261–279.
19.
UtriainenR.The Potential Impacts of Automated Vehicles on Pedestrian Safety in a Four-Season Country. Journal of Intelligent Transportation Systems, Vol. 25, No. 2, 2020, pp. 188–196. https://doi.org/10.1080/15472450.2020.1845671.
20.
AloziA. R.HusseinM.Evaluating the Safety of Autonomous Vehicle–Pedestrian Interactions: An Extreme Value Theory Approach. Analytic Methods in Accident Research, Vol. 35, 2022, p. 100230. https://doi.org/10.1016/j.amar.2022.100230.
21.
SinghS.AliY.HaqueM. M.A Bayesian Extreme Value Theory Modelling Framework to Assess Corridor-Wide Pedestrian Safety Using Autonomous Vehicle Sensor Data. Accident Analysis Prevention, Vol. 195, 2024, p. 107416. https://doi.org/10.1016/j.aap.2023.107416.
22.
CombsT. S.SandtL. S.ClamannM. P.McDonaldN. C.Automated Vehicles and Pedestrian Safety: Exploring the Promise and Limits of Pedestrian Detection. American Journal of Preventive Medicine, Vol. 56, No. 1, 2019, pp. 1–7. https://doi.org/10.1016/j.amepre.2018.06.024.
23.
BernátJ.VertaľP.DemčákováL. Development of Test System for Forensics Investigation of Collision between Vehicle and Pedestrian. Transportation Research Procedia, Vol. 55, 2021, pp. 1563–1569. https://doi.org/10.1016/j.trpro.2021.07.145.
24.
SchachnerM.SinzW.ThomsonR.KlugC.Development and Evaluation of Potential Accident Scenarios Involving Pedestrians and AEB-Equipped Vehicles to Demonstrate the Efficiency of an Enhanced Open-Source Simulation Framework. Accident Analysis & Prevention, Vol. 148, 2020, p. 105831. https://doi.org/10.1016/j.aap.2020.105831.
25.
GillG.BigazziA.Honey-RosésJ.BardutzE.Autonomy Bias: A Deception Experiment to Isolate the Effect of Vehicle Automation on Perceptions of Pedestrian Comfort & Safety. Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 104, 2024, pp. 170–185. https://doi.org/10.1016/j.trf.2024.05.020.
26.
PickeringJ. E.PodsiadlyM.BurnhamK. J.A Model-to-Decision Approach for the Autonomous Vehicle (AV) Ethical Dilemma: AV Collision with a Barrier/Pedestrian(s). IFAC-PapersOnLine, Vol. 52, No. 8, 2019, pp. 257–264. https://doi.org/10.1016/j.ifacol.2019.08.080.
27.
DozzaM.BodaC.-N.JaberL.ThalyaP.LubbeN.How Do Drivers Negotiate Intersections with Pedestrians? The Importance of Pedestrian Time-to-Arrival and Visibility. Accident Analysis & Prevention, Vol. 141, 2020, p. 105524. https://doi.org/10.1016/j.aap.2020.105524.
28.
RoàuI.-A.CarabuleaL.BuzduganI.-D.AntonyaC.Time-to-Collision for the Pedestrian Protection System Simulation. Transportation Research Procedia, Vol. 74, 2023, pp. 1325–1332. https://doi.org/10.1016/j.trpro.2023.11.278.
29.
AlmeidaR.SousaE.MachadoD.FreitasE.Interaction between Pedestrians and Autonomous Vehicles: Protocol Analysis and Preliminary Results. Transportation Research Procedia, Vol. 72, 2023, pp. 3268–3275. https://doi.org/10.1016/j.trpro.2023.11.861.
30.
TranA.-T.MuraleedharanA.OkudaH.SuzukiT.Scenario-Based Stochastic MPC for Vehicle Speed Control Considering the Interaction with Pedestrians. IFAC-PapersOnLine, Vol. 53, No. 2, 2020, pp. 15325–15331. https://doi.org/10.1016/j.ifacol.2020.12.2341.
31.
GehrkeS. R.PhairC. D.RussoB. J.SmaglikE. J.Observed Sidewalk Autonomous Delivery Robot Interactions with Pedestrians | Bicyclists. Transportation Research Interdisciplinary Perspectives, Vol. 18, 2023, p. 100789. https://doi.org/10.1016/j.trip.2023.100789.
32.
AgrawalR.ImielińskiT.SwamiA.Mining Association Rules between Sets of Items in Large Databases. SIGMOD Rec., Vol. 22, No. 2, 1993, pp. 207–216. https://doi.org/10.1145/170036.170072.
33.
BrinM. F.Botulinum Toxin: Chemistry, Pharmacology, Toxicity, and Immunology. Muscle & Nerve, Vol. 20, No. S6, 1997, pp. 146–168. https://doi.org/10.1002/(SICI)1097-4598(1997)6+<146::AID-MUS10>3.0.CO;2-4.
34.
AgrawalR.SrikantR.Fast Algorithms for Mining Association Rules in Large Databases. San Francisco, CA, USA, 1994.
35.
ZhaoQ.BhowmickS.Association Rule Mining: A Survey. Nanyang Technological University, Singapore, 2003.
IJERPH and Free Full-Text and Divergent Effects of Factors on Crash Severity under Autonomous and Conventional Driving Modes Using a Hierarchical Bayesian Approach. https://www.mdpi.com/1660-4601/19/18/11358. Accessed July 28, 2024.
38.
DasS.AvelarR. E.DixonK. K.SunX.Transportation Research Board. Pedestrian Crash Analysis Using Association Rules Mining. Proceedings of the 96th Annual Meeting of the Transportation Research Board, Washington, D.C., 2017.
39.
HossainA.SunX.ThapaR.CodjoeJ.Applying Association Rules Mining to Investigate Pedestrian Fatal and Injury Crash Patterns Under Different Lighting Conditions. Transportation Research Record: Journal of the Transportation Research Board, 2022. 2676: 659–672. https://doi.org/10.1177/03611981221076120.
40.
HongJ.TamakloeR.ParkD.Application of Association Rules Mining Algorithm for Hazardous Materials Transportation Crashes on Expressway. Accident Analysis & Prevention, Vol. 142, 2020, p. 105497.
41.
BarmanS.BandyopadhyayaR.Analyzing Urban Midblock Crash Severity Outcomes Using Proposed Three-Step Pattern Clustering. Journal of Transportation Engineering, Part A: Systems, Vol. 149, No. 7, 2023, p. 05023003. https://doi.org/10.1061/JTEPBS.TEENG-7641.
42.
AlverY.OnelcinP.CicekliA.Abdel-AtyM.Evaluation of Pedestrian Critical Gap and Crossing Speed at Midblock Crossing Using Image Processing. Accident Analysis & Prevention, Vol. 156, 2021, p. 106127.
43.
CuiZ.NambisanS. S.Methodology for Evaluating the Safety of Midblock Pedestrian Crossings. Transportation Research Record: Journal of the Transportation Research Board, Vol. 1828, No. 1, 2003, pp. 75–82. https://doi.org/10.3141/1828-09.
44.
BhagavathulaR.GibbonsR. B.Lighting Strategies to Increase Nighttime Pedestrian Visibility at Midblock Crosswalks. Sustainability, Vol. 15, No. 2, 2023, p. 1455. https://doi.org/10.3390/su15021455.
45.
ZouF.OgleJ.JinW.GerardP.PettyD.RobbA.Pedestrian Behavior Interacting with Autonomous Vehicles during Unmarked Midblock Multilane Crossings: Role of Infrastructure Design, AV Operations and Signaling. Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 100, 2024, pp. 84–100. https://doi.org/10.1016/j.trf.2023.11.003.
46.
RasouliA.TsotsosJ. K.Autonomous Vehicles That Interact With Pedestrians: A Survey of Theory and Practice. IEEE Transactions on Intelligent Transportation Systems, Vol. 21, No. 3, 2020, pp. 900–918. https://doi.org/10.1109/TITS.2019.2901817.
47.
ZouF.Studying Pedestrian’s Unmarked Midblock Crossing Behavior on a Multilane Road When Interacting with Autonomous Vehicles Using Virtual Reality. PhD dissertation, Department of Civil Engineering, Clemson University, Clemson, SC, 2023.
48.
ZhuH.Iryo-AsanoM.AlhajyaseenW. K. M.NakamuraH.DiasC.Interactions Between Autonomous Vehicles and Pedestrians at Unsignalized Mid-Block Crosswalks Considering Occlusions by Opposing Vehicles. Accident Analysis & Prevention, Vol. 163, 2021, p. 106468. https://doi.org/10.1016/j.aap.2021.106468.
49.
DoğanG.ErgenB.A New Hybrid Mobile CNN Approach for Crosswalk Recognition in Autonomous Vehicles. Multimedia Tools and Applications, Vol. 83, No. 26, 2024, pp. 67747–67762. https://doi.org/10.1007/s11042-024-18199-8.
50.
KayaÖ.ÇodurM. Y.MustafarajE.Automatic Detection of Pedestrian Crosswalk with Faster R-CNN and YOLOv7. Buildings, Vol. 13, No. 4, 2023, p. 1070. https://doi.org/10.3390/buildings13041070.
51.
GuidoliniR.ScartL. G.JesusL. F.CardosoV. B.BadueC.Oliveira-SantosT.Handling Pedestrians in Crosswalks Using Deep Neural Networks in the IARA Autonomous Car. International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, 2018.
52.
SchneemannF.HeinemannP.Context-Based Detection of Pedestrian Crossing Intention for Autonomous Driving in Urban Environments. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016, pp. 2243–2248.
53.
PanA.ZhangX.NakamuraH.AlhajyaseenW.Investigating the Efficiency and Safety of Signalized Intersections under Mixed Flow Conditions of Autonomous and Human-Driven Vehicles. Arabian Journal for Science and Engineering, Vol. 45, 2020, pp. 8607–8618.
54.
ChenC.WangJ.XuQ.WangJ.LiK.Mixed Platoon Control of Automated and Human-Driven Vehicles at a Signalized Intersection: Dynamical Analysis and Optimal Control. Transportation research part C: emerging technologies, Vol. 127, 2021, p. 103138.
55.
XiongB.-K.JiangR.Speed Advice for Connected Vehicles at an Isolated Signalized Intersection in a Mixed Traffic Flow Considering Stochasticity of Human Driven Vehicles. IEEE transactions on intelligent transportation systems, Vol. 23, No. 8, 2021, pp. 11261–11272.
56.
MahbubA. I.LeV.-A.MalikopoulosA. A.Safety-Aware and Data-Driven Predictive Control for Connected Automated Vehicles at a Mixed Traffic Signalized Intersection⋆. IFAC-PapersOnLine, Vol. 55, No. 24, 2022, pp. 51–56.
57.
JiangX.ZhangJ.ShiX.ChengJ.Learning the Policy for Mixed Electric Platoon Control of Automated and Human-Driven Vehicles at Signalized Intersection: A Random Search Approach. IEEE Transactions on Intelligent Transportation Systems, Vol. 24, No. 5, 2023, pp. 5131–5143.
58.
KarbasiA.O’Hern.S. Investigating the Impact of Connected and Automated Vehicles on Signalized and Unsignalized Intersections Safety in Mixed Traffic. Future transportation, Vol. 2, No. 1, 2022, pp. 24–40.
59.
MoradlooN.MahdiniaI.KhattakA. J. Safety in Higher Level Automated Vehicles: Investigating Edge Cases in Crashes of Vehicles Equipped with Automated Driving Systems. Accident Analysis & Prevention, Vol. 203, 2024, P. 107607. https://doi.org/10.1016/j.aap.2024.107607.
60.
ChouguleA.ChamolaV.SamA.YuF. R.SikdarB.A Comprehensive Review on Limitations of Autonomous Driving and Its Impact on Accidents and Collisions. IEEE Open Journal of Vehicular Technology, Vol. 5, 2024, pp. 142–161. https://doi.org/10.1109/OJVT.2023.3335180.
61.
WangSLiZ.Exploring the Mechanism of Crashes with Automated Vehicles Using Statistical Modeling Approaches. PLOS ONE, Vol. 14, No. 3, 2019, p. e0214550. https://doi.org/10.1371/journal.pone.0214550.
