haoran-song / pip-planning-informed-prediction Goto Github PK

I'm sorry to bother you，It seems that the data I downloaded from the NGSIM is not complete. Could you send me a copy of your original data：us101-0750am-0805am-0820am-0835am.txt , i80-0400-0415.txt and i80-0500-0515-0530.txt.
Thank you so much!

Nice work. I have a question about the ego vehicle's future planning. In this work, it has been used as input. But for testing and evaluation, how can we get the ego vehicle's future planning trajectory if we do not have the ground truth? It seems that the ego vehicle's future planning trajectory is generated from the ground truth in the code.
Line 325 in data.py:

` def getPlanFuture(self, dsId, planId, refVehId, t):
# Traj of the reference veh
refColIndex = np.where(self.Tracks[dsId - 1][refVehId - 1][0, :] == t)[0][0]
refPos = self.Tracks[dsId - 1][refVehId - 1][1:3, refColIndex].transpose()
# Traj of the planned veh
planColIndex = np.where(self.Tracks[dsId - 1][planId - 1][0, :] == t)[0][0]
stpt = planColIndex
enpt = planColIndex + self.t_f + 1
planGroundTrue = self.Tracks[dsId - 1][planId - 1][1:3, stpt:enpt:self.d_s].transpose()
planFut = planGroundTrue.copy()
# Fitting the downsampled waypoints as the planned trajectory in testing and evaluation.
if self.fit_plan_traj:
wayPoint = np.arange(0, self.t_f + self.d_s, self.d_s)
wayPoint_to_fit = np.arange(0, self.t_f + 1, self.d_s * self.further_ds_plan)
planFut_to_fit = planFut[::self.further_ds_plan, ]
laterParam = fitting_traj_by_qs(wayPoint_to_fit, planFut_to_fit[:, 0])
longiParam = fitting_traj_by_qs(wayPoint_to_fit, planFut_to_fit[:, 1])
planFut[:, 0] = quintic_spline(wayPoint, *laterParam)
planFut[:, 1] = quintic_spline(wayPoint, *longiParam)'

    revPlanFut = np.flip(planFut[1:,] - refPos, axis=0).copy()
    return revPlanFut `

Thanks for your awesome work!
I'd like to ask about training time?
I found it takes me about 5 hours for one epoch.
Is it right?
Thanks!

您好，我在读您这篇文章代码的时候发现在处理目标车辆的历史轨迹数据与邻居车辆的历史轨迹数据时有 wholePeriod 参数，在处理目标车辆历史轨迹时wholePeriod参数置0，即以最后观察时刻为原点，在处理邻居车辆历史轨迹时wholePeriod参数置1，即用邻居车辆每一时刻的坐标减目标车辆对应时刻的坐标，这会使得邻居车辆与目标车辆不在同一坐标系中。所以为什么要在处理邻居车辆历史轨迹时将wholePeriod参数置1呢？

Dear author, which of the default parameters should I adjust when I don't have enough memory?

In the trained model you provided, the tar file in the downloaded file cannot be decompressed

您好：
非常棒的工作并且开源了代码。
想问下是否可开源预测结果可视化的代码？非常感谢。

最近在看你的prime论文 可以开源PRIME模型的训练代码么？

Hi,
Thank you very much for your excellent work! If I want to apply your work to intersections, what changes do I need to make? Can your achievements be directly applied to the intersections? And I want to predict the traffic situation in the Interaction dataset. What do I need to do? (I'm a beginner, and I don't know the logic at the bottom of the network. Please tell me more, thank you.)
Looking forward to your reply.

您好，请问fig.3和fig.4中的动态图是如何生成的呢？

你好，我在你给其他人回复的链接里下载三个数据集 但都是解压失败 是文件本身上传出错了吗 我已经试过好多次了，三个数据集都不行。。。。

您好，我有一个问题，当我关闭planning模块时，evaluate会报错：RuntimeError: Error(s) in loading state_dict for pipNet:
Unexpected key(s) in state_dict: "plan_lstm.weight_ih_l0", "plan_lstm.weight_hh_l0", "plan_lstm.bias_ih_l0", "plan_lstm.bias_hh_l0", "plan_conv_social.0.weight", "plan_conv_soc
ial.0.bias", "plan_conv_social.3.weight", "plan_conv_social.3.bias".
我不知道如何解决，希望您百忙之中可以解答我的问题

Thank you for your excellent work. Can the model trained in this project be used to predict real vehicles? In other words, can the LSTM-based architecture be used in real vehicles? Thank you!

Very good work! Looking forward to your open source, I hope to learn something!

Hi,

Thanks for the wonderful work. When is the code coming?

Best,

Usama.

Hello, thank you very much for the source code for vehicle trajectory prediction. When I unzip the downloaded trained model ,there will always be errors. It has been solved for a long time without success. Can you send me a copy to my email “[email protected]”? thank you.

Thank you for providing source code of your brilliant work on vehicle trajectory prediction. I have 2 questions about HighD dataset using in your work:

1. In the file 'preprocess_highD.m', the length of past trajectory is 30 frames and that of future trajectory is 50 frames, which is the same to your implementation in 'preprocess_ngsim.m'. However, as far as I know, the sampling rate of HighD dataset is 25Hz and is different from the sampling rate of NGSIM dataset, which is 10Hz. Could you please share the details about dealing with such problem?
2. It would be very kind of you if the source code on plotting the figures in your paper will be shared.