Neural Network Based Vehicular Location Prediction Model for Cooperative Active Safety Systems
Safety systems detect unsafe conditions and provide warnings for travellers to take action and avoid crashes. Estimation of the geographical location of a moving vehicle as to where it will be positioned next with high precision and short computation time is crucial for identifying dangers. To this...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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University of Zagreb, Faculty of Transport and Traffic Sciences
2018-04-01
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| Series: | Promet (Zagreb) |
| Subjects: | |
| Online Access: | https://traffic.fpz.hr/index.php/PROMTT/article/view/2500 |
| Summary: | Safety systems detect unsafe conditions and provide warnings for travellers to take action and avoid crashes. Estimation of the geographical location of a moving vehicle as to where it will be positioned next with high precision and short computation time is crucial for identifying dangers. To this end, navigational and dynamic data of a vehicle are processed in connection with the data received from neighbouring vehicles and infrastructure in the same vicinity. In this study, a vehicular location prediction model was developed using an artificial neural network for cooperative active safety systems. The model is intended to have a constant, shorter computation time as well as higher accuracy features. The performance of the proposed model was measured with a real-time testbed developed in this study. The results are compared with the performance of similar studies and the proposed model is shown to deliver a better performance than other models. |
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| ISSN: | 0353-5320 1848-4069 |