A Comparative Conflict Resolution Dataset Derived from Argoverse-2: Scenarios with vs. without Autonomous Vehicles
Authors: Guopeng Li, Yiru Jiao, Simeon C. Calvert, and Hans van Lint
- This dataset is derived from the open Argoverse-2 motion forecasting data: [https://www.argoverse.org/av2.html#forecasting-link]
- The dataset, 2 metadata files, and the code to read and visualize the data can be downloaded through the 4.TU data platform: [https://data.4tu.nl/datasets/8d6ee0b0-8ed5-43f3-b1c9-7665cc163e87]
Reading and visualizing the dataset requires the following packages"
python >=3.8
numpy
scipy>=0.17.0
zarr
h5py
matplotlib
tqdm (optional)
- Put the
visual_utils.pyin the root dir. - Unzip the downloaded
data.zipfile to the root dir. - The dataset is stored in a compact
zarrform so even a 'potato' computer can handle this.
- Import the necessary packages:
import zarr
import os
from visual_utils import *
folder = 'av' # or 'hv'
root ='./data_3m/'+folder+'/'
log_ids = [name for name in os.listdir(root) if os.path.isdir(os.path.join(root, name))]
print('Number of scenarios: ', len(log_ids))- Read the data
'''
slices: len = nb_objects + 1, slices[n] and slices[n+1] gives the start/end indices of the n-th object
maps: lanes as NumPy array
type: len = nb_objects, contains 7 numbers with the following meanings:
-1: Static background
0: human-driven vehicles
1: pedestrians
2: motorcyclists
3: cyclists
4: buses
10: autonomous vehicles
timestep: timestamps in second, timestep[slices[n]: slices[n+1]] give the timestamps for the n-th object
motion: motion state, with 7 dimensions
motion[slices[n]: slices[n+1]] gives the motion of the n-th object, the 7 features are the following variables in order:
[x, y, vx, vy, ax, ay, yaw]
yaw is to the x-axis, between [-pi, pi]
'''
slices, timestep, motion, type, maps = read_scenario(log_ids[2441], root)- Visualize the data
# Visualize the 2441-st scenario
# the parameter other_road_users = True/False controls whether to plot the surrounding agents (True by default)
# The parameter direction = True/False controls whether to plot the start and end directions of the vehicles involved in the conflict (True by default)
fig, ax = visualize(log_ids[2441], root, other_road_users=True, direction=True)The metadata files is a table, shown as the example below, for AV-involved scenarios (metafile_av.csv) and AV-free scenarios (metafile_hv.csv), respectively.
| log_id | xi_start | yi_start | xj_start | yj_start | typei | xi_end | yi_end | xj_end | yj_end | typej | direction | PET | ifirst | angle_start | angle_end | start | end |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2441 | -0.028310 | 0.999599 | -0.278388 | -0.960469 | AV | -0.001506 | 0.999999 | 0.998085 | 0.061850 | HV | R-L | 3.7 | False | 162.213724 | 86.540315 | opposite | cross |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
These tables provide the following information about each scenario:
| column | dtype | information |
|---|---|---|
log_id |
str | index of the scenario |
[xi_start, yi_start] |
float | direction vector of the first* agent recorded in the scenario at the start** time |
[xj_start, yj_start] |
float | direction vector of the second agent recorded in the scenario at the start time |
typei |
str | agent type of the first agent recorded in the scenario, being one of {'AV','HV','Pedestrian','Motorcyclist','Cyclist','Bus'} |
[xi_end, yi_end] |
float | direction vector of the first* agent recorded in the scenario at the end*** time |
[xj_end, yj_end] |
float | direction vector of the second agent recorded in the scenario at the end time |
typej |
str | agent type of the second agent recorded in the scenario, being one of {'AV','HV','Pedestrian','Motorcyclist','Cyclist','Bus'} |
direction |
str | whether the second-passing vehicle moved from the left ('L-R') or the right ('R-L') of the first-passing agent |
PET |
float | post-encroachment-time |
ifirst |
bool | whether the first-passing agent is the first agent recorded in the scenario |
angle_start |
float | angle between the direction vectors of the two agents at the start time |
angle_end |
float | angle between the direction vectors of the two agents at the end time |
start |
str | whether the two agents ran parallel (P), crossed (C), or ran opposite (O) to each other before reaching the conflict point |
end |
str | whether the two agents ran parallel (P), crossed (C), or ran opposite (O) to each other after reaching the conflict point |
| Notes | * Note that the first agent does not necessarily pass the conflict point first. ** We consider the start time as 5 seconds before the first-passing agent passed the conflict point, or the start of the record if the time before passing the conflict point is less than 5 seconds. *** Similarly, the end time is 5 seconds after the second-passing vehicle passed the conflict point, or the end of the record if the time after passing the conflict point is less than 5 seconds. |
One can use the following code to select data in different conditions:
# AV passing the conflict point first
case_av_first = metafile_av[(metafile_av['ifirst'])] # In cases involving AV, the AV is the first recorded agent
# Pedestrian passing the conflict point second
case_ped_first_av_free = metafile_hv[(~metafile_hv['ifirst'])&(metafile_hv['typej']=='Pedestrian')]
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