Description
Hello,
I would like to use the deterministic framework on datasets other than the default ones (JAAD, PIE, ETH, UCY).
The dataset at my disposal is a CSV with the following features:
['Position_X (m)', 'Position_Y (m)'
'Velocity_X (m/s)', 'Velocity_Y (m/s)',
'Acceleration_X (m/s^2)', 'Acceleration_Y (g)',
'Yaw Angle (rad)', 'Yaw Rate (rad/s)',
'Lateral Offset Left (m)', 'Lateral Offset Right (m)',
'Curvature Left (1)', 'Curvature Right (1)',
'Curvature Derivative Left (1)', 'Curvature Derivative Right (1)',
'Heading Angle Left (rad)']
I was wondering if you had any hints on how to process this data in a way compatible with the SGNet.
At the moment I am trying to recreate a structure similar to the ETH-processed dataset:
(input_x, input_x_st, target_y, target_y_st, first_history_index, scene_name, timestep)
My intuition is that recreating (input_x, input_x_st, target_y) and maybe modifying the input layers of the SGNet, it should work. However, I cannot understand what "input_x_st" is, if you had an insight into this structure it could be useful.
In general, any suggestion, also not related to this approach, will be really appreciated.
Thank you