GAN-Based Generation of Synthetic Data for Vehicle Driving Events

Autor: Diego Tamayo-Urgilés, Sandra Sanchez-Gordon, Ángel Leonardo Valdivieso Caraguay, Myriam Hernández-Álvarez

Publikováno v: Applied Sciences, Vol 14, Iss 20, p 9269 (2024)

Developing solutions to reduce traffic accidents requires experimentation and much data. However, due to confidentiality issues, not all datasets used in previous research are publicly available, and those that are available may be insufficient for r

Externí odkaz: https://doaj.org/article/99afb1f2ca3d4310af369a0c60864c94

A comparison of traffic crash and connected vehicle event data on a freeway corridor with Hard-Shoulder Running.

Autor: Gupta N; Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, United States. Electronic address: guptanis@msu.edu., Cai Q; Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, United States. Electronic address: caiqiuqi@msu.edu., Jashami H; Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, United States. Electronic address: jashamih@oregonstate.edu., Savolainen PT; Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, United States. Electronic address: pete@msu.edu., Gates TJ; Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, United States. Electronic address: gatestim@msu.edu., Barrette T; Global Data Insights & Analytics, Ford Motor Company, United States. Electronic address: tbarre36@ford.com., Powell W; Global Data Insights & Analytics, Ford Motor Company, United States. Electronic address: wpowel49@ford.com.

Publikováno v: Accident; analysis and prevention [Accid Anal Prev] 2024 Dec 27; Vol. 211, pp. 107900. Date of Electronic Publication: 2024 Dec 27.