A V2P Collision Risk Warning Method based on LSTM in IOV
نویسندگان
چکیده
With the evolution of communication networks, Internet Vehicles (IOV) continues to accelerate safe and rapid development autonomous vehicles. Vehicle-to-Pedestrian (V2P) is a key technology in vehicles potential solution realize collaborative intelligence between pedestrians. However, existing V2P early warning system does not consider uncertainty pedestrian trajectory, determination collision area limited single point, resulting an inaccurate judgment improvement traffic efficiency. This paper designs new autonomous-oriented network architecture completes based on Long Range (LoRa). A anticollision model established, risk method proposed. In this method, danger index introduced into vehicle. The long short-term memory (LSTM) artificial neural used predict pedestrian’s so as deduce pedestrian-vehicle when trajectory uncertain. Meanwhile, confidence probability judge whether vehicle are warned. simulation shows that proposed has good performance, which can accurately warn under different speeds Global Positioning System (GPS) positioning errors. At same time, it reflects characteristics brought by using LSTM methods. Using LoRa verify experimental results show GPS accuracy submeter level, prediction greater than 98%. performance high detection rate.
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ژورنال
عنوان ژورنال: Security and Communication Networks
سال: 2022
ISSN: ['1939-0122', '1939-0114']
DOI: https://doi.org/10.1155/2022/7507573