mczhi / predictive-decision Goto Github PK

Hi MCZhi,

Thank you for sharing such an interesting work. I have some questions about those ground truth trajectories of other agents when training interaction-aware predictor.

1. Do those ground truth trajectories of other agents are generated from log file and so are fixed during training? If so, how does it make sure that other agents will have corresponding reactions on the ego's planning trajectory?

2. What is the difference between the ego-conditioned auto-regressive planning method and traditional imitation learning methods?

I read the source code and found that the input obs.waypoint_paths of the planner already contains 51 waypoints. The trajectory sampling part will first use the interpolation method to form the route formed by these waypoints into a CubicSpline2D curve x=f(s),y=f( s), and then generate_trajectory_on_frenet will generate various trajectories giving different accelerations (due to the difference in acceleration, some new trajectories will indeed be generated). But I found the calculate_global_trajectory function still uses the CubicSpline2D curve formed by route to calculate fx and fy, which means that even if many trajectories are generated , but the waypoints from the route still need to be passed through in theory. So if I understand correctly, the waypoint provided by obs.waypoint_paths is actually similar to the target point of the carla simulator. I'm wondering if there's something wrong with my understanding. And if this is the case, can it be possible to use only two waypoints as the starting point and end point, and then generate more diverse trajectories?
In addition, I don't quite understand why calculate_global_trajectory uses CubicSpline2D's i_yaw based on the original route to calculate fx and fy.
I'm very sorry if there is any misunderstanding, I am not very familiar with the smarts simulator.

Hello, Zhiyu,
Thank you for your work,
I would like to ask if during the training session, the code will report an error 'Environment multi scenario does not exist.'
May I ask how to solve it.
Thank you!

Hi, thanks for your work!

I have some questions about the success rate about the baseline method in table II.
I have used the pretrained model provided in the repo for the test.py, but the success rate of "Ours" methods is only 80% for the first scene, which is lower than the 98% in the paper.

My command line is:
nohup python test.py --name baseline --use_interaction --model_path PATH/interaction_aware_predictor.pth

Did I make any mistakes in my reproduction steps?

Hi Zhiyu, the error comes then testing. Could you pls let me let me know the reason ? Thx a lot.
Running command: python test.py --model_path interaction_aware_predictor.pth

Training is OK.
Running command: python train.py --use_exploration --use_interaction

Thank you for your work. I have downloaded the pre-trained model ckpt you provided, but when I tested it, an error occurred. I can ensure that I installed the environment dependencies correctly, so how should I solve this problem?

RuntimeError: Error(s) in loading state_dict for Predictor:
        Unexpected key(s) in state_dict: "decoder.plan_input.weight", "decoder.plan_input.bias", "decoder.state_input.weight", "decoder.state_input.bias".
        size mismatch for decoder.cell.weight_ih: copying a param with shape torch.Size([1152, 128]) from checkpoint, the shape in current model is torch.Size([1152, 3]).

Would u pls release the trained model ? Thx a lot.