kumarrobotics / conformal_lattice_planner Goto Github PK

Dear Ke,

I am attempting to implement your code into Python.

Firstly, as far as I understand, by using the speed feedback policy, the search is running in the scheme of Spatial Lattice.
However, the prediction of other agents (or the IDM modeling of other agents) is based on the time axle.
How did you map the position of other agents into the occupancy of spatial lattice?
In the best case, could you tell me which part of the code is doing this?

Secondly, during a search in the spatiotemporal lattice, I found that if the motion primitive is too big ( expansion of distance/time is too big), the ego-motion might "jump" over the obstacle vehicle, which is not realistic.
For example, in a car-following scenario, motion primitive (endpoint) jumps over the car in front of the ego vehicle.
I hope you understand my question, and could you share your experience about how to deal with this?

Best regards,
Kailin

Dear Ke,

could you pleaes tell which part of your code corresponds to this Directed-graph Map Construction in your paper?

many thanks!

Sincerly, Kailin

"EgoPlanAction.h " was not found in conformal_lattice_planner_master

Dear Ke,

this part is not really clear in the paper:
"To ensure reasonable runtime, hybrid-A* is applied. Longitudinal speed is discretized to three intervals, while the temporal dimension is left undiscretized."

How is the longitudinal speed discretized? what is the temporal dimension?

And could you kindly tell me which part of the code corresponding to the Hybrid A* search?

Many thanks and kind regards,
Kailin