ETA Prediction with Graph Neural Networks in Google Maps[C]. CIKM, 2021. Link Golovnin O, Perevozchikov N. E-STGCN: Enhanced Spatial-Temporal Graph Convolutional Network for Road Traffic Forecasting[C]//2021 International Conference on Information Technology and Nanotechnology (ITNT). IEEE, ...
Compared with the existing traffic information perception network (Vehicle-to-Everything, Google Maps, Waze, Apple Map public transportation information systems and traffic management center, etc.), the main features of the comprehensive traffic information perception system are embodied in the physical ...
The test showed that the P-value of each predictor and the whole predictors simultaneously were greater than 0.05 which indicated that there were no time-varying variables in the model and the proportional hazard assumption was fitted (p = 0.1274) (Annex- V). The model’s fitness test ...
python scripts/train.py --dataset_path <path-to-lyft-data-directory> --config_name l5_agent_predictor --debug nuScenes dataset: First train a spatial planner: python scripts/train.py --dataset_path <path-to-nuScenes-data-directory> --config_name nusc_spatial_planner --debug Then train ...
Predictor parameter estimates and 95% confidence intervals were exponentiated to provide estimates of percent change in exposure. Significant differences between outdoor and enclosed bus stations and bus stops were expressed as percent increases in exposure. Significant differences in exposures between the ...
The remainder of this section discusses models 1 and 2, used as stepping stones to achieve the better model 3, and a test of the effectiveness of road class as a predictor of motorized flow. Model 1 is the initial attempt to use road class to predict cycling, and used for calibration pu...
Currently, integrating neural network predictors with planners has gradually attracted attention. For example, a mathematical control framework based on MPC encompassing an RNN architecture was proposed [30]. Similarly, an MPC-based motion planning algorithm that incorporates a GNN predictor was built to...
The Random Forest learner was able to accurately predict 63.208% of the model evaluation test samples with a Cohen’s Kappa (k) value of 0.51. The confusion matrix for the Random Forest predictor is presented in Table11. Table 11 Confusion matrix for the Random Forest Predictor ...
a temporal predictor is designed to capture short-term and long-term dependencies of air quality, utilizing deep LSTM (Long Short Term Memory) networks. These solutions are only based on sensor data. In the study, distinct sub-models are employed to discern spatial correlations among specific st...
The Head section is made up of predictor 1, predictor 2 and predictor 3. The images undergo feature extraction, feature fusion to eventually form three sizes of detection heads, which are used to output three different sizes of targets: large, medium and small. ...