6,460 research outputs found
Intention-aware Long Horizon Trajectory Prediction of Surrounding Vehicles using Dual LSTM Networks
As autonomous vehicles (AVs) need to interact with other road users, it is of
importance to comprehensively understand the dynamic traffic environment,
especially the future possible trajectories of surrounding vehicles. This paper
presents an algorithm for long-horizon trajectory prediction of surrounding
vehicles using a dual long short term memory (LSTM) network, which is capable
of effectively improving prediction accuracy in strongly interactive driving
environments. In contrast to traditional approaches which require trajectory
matching and manual feature selection, this method can automatically learn
high-level spatial-temporal features of driver behaviors from naturalistic
driving data through sequence learning. By employing two blocks of LSTMs, the
proposed method feeds the sequential trajectory to the first LSTM for driver
intention recognition as an intermediate indicator, which is immediately
followed by a second LSTM for future trajectory prediction. Test results from
real-world highway driving data show that the proposed method can, in
comparison to state-of-art methods, output more accurate and reasonable
estimate of different future trajectories over 5s time horizon with root mean
square error (RMSE) for longitudinal and lateral prediction less than 5.77m and
0.49m, respectively.Comment: Published at the 21st International Conference on Intelligent
Transportation Systems (ITSC), 201
Modeling Typhoon Event-Induced Landslides Using GIS-Based Logistic Regression: A Case Study of Alishan Forestry Railway, Taiwan
This study develops a model for evaluating the hazard level of landslides at Alishan Forestry Railway, Taiwan, by using logistic regression with the assistance of a geographical information system (GIS). A typhoon event-induced landslide inventory, independent variables, and a triggering factor were used to build the model. The environmental factors such as bedrock lithology from the geology database; topographic aspect, terrain roughness, profile curvature, and distance to river, from the topographic database; and the vegetation index value from SPOT 4 satellite images were used as variables that influence landslide occurrence. The area under curve (AUC) of a receiver operator characteristic (ROC) curve was used to validate the model. Effects of parameters on landslide occurrence were assessed from the corresponding coefficient that appears in the logistic regression function. Thereafter, the model was applied to predict the probability of landslides for rainfall data of different return periods. Using a predicted map of probability, the study area was classified into four ranks of landslide susceptibility: low, medium, high, and very high. As a result, most high susceptibility areas are located on the western portion of the study area. Several train stations and railways are located on sites with a high susceptibility ranking
A step into the world of Pakistanis: oral health education for Pakistani adults in Hong Kong
Includes bibliographical references (p. 32).Questionnaire in English and Urdu.published_or_final_versio
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