{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:36:31Z","timestamp":1760142991732,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000140","name":"Safety through Disruption (Safe-D) University Transportation Center","doi-asserted-by":"publisher","award":["69A355177115"],"award-info":[{"award-number":["69A355177115"]}],"id":[{"id":"10.13039\/100000140","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Line-of-sight (LOS) sensors developed in newer vehicles have the potential to help avoid crash and near-crash scenarios with advanced driving-assistance systems; furthermore, connected vehicle technologies (CVT) also have a promising role in advancing vehicle safety. This study used crash and near-crash events from the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP2 NDS) to reconstruct crash events so that the applicable benefit of sensors in LOS systems and CVT can be compared. The benefits of CVT over LOS systems include additional reaction time before a predicted crash, as well as a lower deceleration value needed to prevent a crash. This work acts as a baseline effort to determine the potential safety benefits of CVT-enabled systems over LOS sensors alone.<\/jats:p>","DOI":"10.3390\/s24020484","type":"journal-article","created":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T07:47:16Z","timestamp":1705045636000},"page":"484","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["The Impact of Line-of-Sight and Connected Vehicle Technology on Mitigating and Preventing Crash and Near-Crash Events"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5055-7777","authenticated-orcid":false,"given":"Eileen","family":"Herbers","sequence":"first","affiliation":[{"name":"Virginia Tech Transportation Institute, Virginia Tech, Blacksburg, VA 24060, USA"},{"name":"Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA 24060, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3897-1430","authenticated-orcid":false,"given":"Zachary","family":"Doerzaph","sequence":"additional","affiliation":[{"name":"Virginia Tech Transportation Institute, Virginia Tech, Blacksburg, VA 24060, USA"},{"name":"Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA 24060, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Loren","family":"Stowe","sequence":"additional","affiliation":[{"name":"Virginia Tech Transportation Institute, Virginia Tech, Blacksburg, VA 24060, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, B., Han, Y., Wang, S., Tian, D., Cai, M., Liu, M., and Wang, L. (2022). A Review of Intelligent Connected Vehicle Cooperative Driving Development. Mathematics, 10.","DOI":"10.3390\/math10193635"},{"key":"ref_2","unstructured":"Chang, J., Hatcher, G., Hicks, D., Schneeberger, J., Staples, B., Sundarajan, S., Vasudevan, M., Wang, P., and Wunderlich, K. (2015). Estimated Benefits of Connected Vehicle Applications: Dynamic Mobility Applications, AERIS, V2I Safety, and Road Weather Management Applications, FHWA. FHWA-JPO-15-255."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.trc.2016.01.020","article-title":"How to assess the benefits of connected vehicles? A simulation framework for the design of cooperative traffic management strategies","volume":"67","author":"Billot","year":"2016","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_4","unstructured":"NHTSA (2023, September 09). Automated Vehicles for Safety, Available online: https:\/\/www.nhtsa.gov\/technology-innovation\/automated-vehicles-safety."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Shokravi, H., Shokravi, H., Bakhary, N., Heidarrezaei, M., Rahimian Koloor, S.S., and Petr\u016f, M. (2020). A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques. Sensors, 20.","DOI":"10.3390\/s20113274"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Schiegg, F.A., Llatser, I., Bischoff, D., and Volk, G. (2020). Collective Perception: A Safety Perspective. Sensors, 21.","DOI":"10.3390\/s21010159"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.aap.2018.02.025","article-title":"What are the factors that contribute to road accidents? An assessment of law enforcement views, ordinary drivers\u2019 opinions, and road accident records","volume":"115","author":"Rolison","year":"2018","journal-title":"Accid. Anal. Prev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"105299","DOI":"10.1016\/j.aap.2019.105299","article-title":"How many crashes can connected vehicle and automated vehicle technologies prevent: A meta-analysis","volume":"136","author":"Wang","year":"2020","journal-title":"Accid. Anal. Prev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.aap.2018.04.002","article-title":"Assessment of the safety benefits of vehicles\u2019 advanced driver assistance, connectivity and low level automation systems","volume":"117","author":"Yue","year":"2018","journal-title":"Accid. Anal. Prev."},{"key":"ref_10","unstructured":"NHTSA (2016). Vehicle-to-Vehicle Communication Technology for Light Vehicles, NHTSA. FMVSS No. 150."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Abdeen, M.A.R., Yasar, A., Benaida, M., Sheltami, T., Zavantis, D., and El-Hansali, Y. (2022). Evaluating the Impacts of Autonomous Vehicles\u2019 Market Penetration on a Complex Urban Freeway during Autonomous Vehicles\u2019 Transition Period. Sustainability, 14.","DOI":"10.3390\/su141610094"},{"key":"ref_12","unstructured":"Czapp, T., Chen, C.-L., Lawrence, S.S., and Wiacek, C. (2022). Real-World Effectiveness of Model Year 2015\u20132020 Advanced Driver Assistance Systems, Partnership for Analytics Research in Traffic Safety (PARTS). Public Release Case Number 22-3734."},{"key":"ref_13","first-page":"S9","article-title":"Injury Mitigation Estimates for an Intersection Driver Assistance System in Straight Crossing Path Crashes in the US","volume":"18","author":"Scanlon","year":"2017","journal-title":"Taylor Fr."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1109\/OJITS.2022.3165769","article-title":"A Nationwide Impact Assessment of Automated Driving Systems on Traffic Safety Using Multiagent Traffic Simulations","volume":"3","author":"Kitajima","year":"2022","journal-title":"IEEE Open J. Intell. Transp. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"966","DOI":"10.1109\/TITS.2020.3019050","article-title":"Vehicle Trajectory Prediction and Cut-In Collision Warning Model in a Connected Vehicle Environment","volume":"23","author":"Lyu","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"105972","DOI":"10.1016\/j.aap.2021.105972","article-title":"Connected vehicle-based road safety information system (CROSS): Framework and evaluation","volume":"151","author":"Jo","year":"2021","journal-title":"Accid. Anal. Prev."},{"key":"ref_17","first-page":"723","article-title":"Simulation for Non-line-of-sight Collision Avoidance Warning System Based on 5G Mobile Car Communication Network","volume":"35","author":"Guo","year":"2022","journal-title":"Sens. Mater."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Baek, M., Jeong, D., Choi, D., and Lee, S. (2020). Vehicle Trajectory Prediction and Collision Warning via Fusion of Multisensors and Wireless Vehicular Communications. Sensors, 20.","DOI":"10.3390\/s20010288"},{"key":"ref_19","unstructured":"Herbers, E., and Stowe, L. (2022). Impacts of Connected Vehicle Technology on Automated Vehicle Safety, Virginia Transportation Institute. 04-120."},{"key":"ref_20","unstructured":"Hankey, J.M., Perez, M.A., and McClafferty, J.A. (2016). Description of the SHRP2 Naturalistic Database and the Crash, Near-Crash, and Baseline Data Sets, Virginia Tech Transportation Institute\u2014The Strategic Highway Research Program 2 Transportation Research Board of the National Academies."},{"key":"ref_21","unstructured":"Brown, J.L., and Richard, C.M. (2020). Analysis of SHRP2 Speeding Data: Methods Used to Conduct the Reseearch, National Highway Traffic Safety Administration. DOT HS 812 793."},{"key":"ref_22","unstructured":"Lee, S.E., Llanera, E., Klauer, S., and Sudweeks, J. (2007). Analyses of Rear-End Crashes and Near-Crashes in the 100-Car Naturalistic Driving Study to Support Rear-Signaling Countermeasure Development, Virginia Tech Transportation Institute. DOT HS 810 846."},{"key":"ref_23","first-page":"236","article-title":"Hard Braking Events among Novice Teenage Drivers by Passenger Characteristics","volume":"2009","author":"Ouimet","year":"2009","journal-title":"Driv. Assess. Conf."},{"key":"ref_24","unstructured":"American Automobile Association (AAA) (2022). Automatic Emergency Braking Performance in the Context of Common Crash Scenarios, American Automobile Association."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.jsr.2020.03.012","article-title":"Near crash characteristics among risky drivers using the SHRP2 naturalistic driving study","volume":"73","author":"Seacrist","year":"2020","journal-title":"J. Saf. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"66","DOI":"10.3141\/2147-09","article-title":"Near Crashes as Crash Surrogate for Naturalistic Driving Studies","volume":"2147","author":"Guo","year":"2010","journal-title":"Transp. Res. Rec."},{"key":"ref_27","unstructured":"NHTSA (2016). General Estimates System (GES) Analytical User\u2019s Manual, U.S. Department of Transportation. DOT HS 812 320."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/2\/484\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:45:39Z","timestamp":1760103939000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/2\/484"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,12]]},"references-count":27,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["s24020484"],"URL":"https:\/\/doi.org\/10.3390\/s24020484","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2024,1,12]]}}}