| Autor: |
Jiujun Cheng, Huaichen Yan, Aiguo Zhou, Chunmei Liu, Ding Cheng, Shangce Gao, Di Zang, Deli Cheng |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 8, Pp 10754-10767 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2019.2957785 |
| Popis: |
Along with the Internet of Vehicles, some intelligent systems can help the medical vehicles transport medical supplies and patients. In terms of emergency issues like catastrophic natural disasters or serious accidents, safe and timely transportation for medical vehicles is particularly important. For assistance to medical vehicles on the road, models with position prediction can provide accurate position information of ambient vehicles in the next seconds. However, with the increasing number of vehicles on the road and the changing road environment, it is an important challenge to predict the location of vehicles in the road correctly. Current location prediction models for vehicles usually use the previous trajectory of vehicles, lacking the consideration of vehicle's state and real-time traffic information, which leads to relatively low accuracy and safety. Based on the deep belief nets (DBN) and long short-term memory (LSTM), this paper presents a location prediction model for assistance to medical vehicles (LPMVs), which fully considers vehicle's attributes, road information and driving environment as well as the relationship between the factors that influence vehicle driving behaviors and vehicle positions. By experiment, we prove that LPMVs can predict more accurately than current location prediction models and be a good choice for assistance to medical vehicles. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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