The package provides operators and classes useful for predicting future
trajectories of other agents. Pylot predicts future trajectories of obstacles
detected and tracked by the perception component. However, if you desire to
run the prediction components using perfectly tracked obstacles, you can pass
--perfect_obstacle_tracking when you’re running in simulation.
Execute the following command to run a prediction demo:
python3 pylot.py --flagfile=configs/prediction.conf
--prediction: Enables the prediction component of the pipeline.
--prediction_type: Sets which prediction operator to use. Pylot currently offers two prediction implementations: a simple linear predictor and R2P2.
--prediction_num_past_steps: Sets the number of past obstacle locations the prediction components uses. The duration of the history used for prediction is equal to the number of past steps multiplied by the time between each step run.
--prediction_num_future_steps: Sets the number of future steps to predict.
--evaluate_prediction: Enables computation and logging of accuracy metrics of the prediction component.
--visualize_prediction: Enables visualization of predicted obstacle trajectories.