PEDS-37

Pedestrian and robot trajectories from PEDS over 37 combined scenarios.

Stats

• Trials: 2850
• Scenarios: bt_dense_group_conversation, bt_dense_queue_use, bt_dense_robot_group_conversation, bt_dense_robot_queue_use, bt_dense_robot_service_mobile, bt_dense_service_mobile, bt_sparse_compound, bt_sparse_group_conversation, bt_sparse_needs, bt_sparse_pair, bt_sparse_queue_use, bt_sparse_robot_compound, bt_sparse_robot_group_conversation, bt_sparse_robot_needs, bt_sparse_robot_pair, bt_sparse_robot_queue_use, bt_sparse_robot_service_mobile, bt_sparse_robot_service_static, bt_sparse_robot_sit, bt_sparse_service_mobile, bt_sparse_service_static, bt_sparse_sit, het_dense_mixed_speeds, het_dense_robot_in_group, het_dense_wheelchair_bottleneck, nav_dense_bottleneck, nav_dense_corridor, nav_dense_crossing, nav_dense_flow, nav_dense_robot_bottleneck, nav_dense_robot_corridor, nav_dense_robot_crossing, nav_dense_robot_flow, nav_sparse_bend, nav_sparse_corridor, nav_sparse_crossing, nav_sparse_flow, nav_sparse_merge, nav_sparse_robot_bend, nav_sparse_robot_corridor, nav_sparse_robot_crossing, nav_sparse_robot_flow, nav_sparse_robot_merge
• Pedestrian planners: hsfm, nsp, orca, sfm, socialgail, straight
• Mode: robots (heterogeneous human+robot)

Layout

• data/agent_states/bucket=<b>/density=<d>/scenario=<s>/planner=<p>/*.parquet - long-format trajectories, ~100_000 rows per row-group, zstd compressed. Partition values for bucket, density, scenario and planner are encoded in the path; mirrors the config/evaluation/<bucket>/<density>/<modality>.yaml input tree.
• data/metrics/kinematics_per_trial.parquet - per-trial scalar metrics (jerk, curvature, collisions).
• data/metrics/robot_metrics.parquet - per-trial robot KPIs (success, time-to-goal, path efficiency, personal-space violations). Robots-mode only.
• data/metrics/failures.parquet - per-trial failure cause classification. Robots-mode only.
• configs/scenarios/*.yaml - scenario YAML snapshots used at sweep time.
• croissant.json - Croissant 1.0 machine-readable schema.
• DATASHEET.md - Datasheet for Datasets (Gebru et al.) covering motivation, composition, collection, uses, distribution, maintenance.

Schema

agent_states (long-format trajectories)

field	dtype	unit	description
time	float64	s	sim time, monotonic per trial
agent_id	int64	-	unique within trial; humans use positive ids (auto-assigned from 1), robots use negative ids
x	float64	m	world-frame x position
y	float64	m	world-frame y position
vx	float64	m/s	world-frame x velocity
vy	float64	m/s	world-frame y velocity
radius	float64	m	agent collision radius
planner	string	-	pedestrian motion model (Hive partition column)
scenario	string	-	canonical scenario id (Hive partition column)
seed	int64	-	RNG seed for the trial
robot_policy	string	-	robot policy name; empty string in divergence-mode trials
bucket	string	-	scenario family: nav / bt (Hive partition column)
density	string	-	pedestrian density regime: sparse / dense (Hive partition column)
modality	string	-	scenario modality within (bucket, density), e.g. corridor, group_conversation
is_robot_scenario	bool	bool	true if scenario id is a robot variant of a pure-ped sibling
source_dir	string	-	originating sweep dir name (provenance)

kinematics_per_trial

field	dtype	unit	description
bucket	string	-	scenario family: nav / bt (Hive partition column)
scenario	string	-	canonical scenario id (Hive partition column)
planner	string	-	pedestrian motion model (Hive partition column)
robot_policy	string	-	robot policy name; empty string in divergence-mode trials
seed	int64	-	RNG seed for the trial
jerk	float64	m/s^3	mean per-agent jerk over the trial
curvature	float64	1/m	mean per-agent path curvature over the trial
collisions	int64	count	agent-agent collision events in the trial

robot_metrics

field	dtype	unit	description
scenario	string	-	canonical scenario id (Hive partition column)
bucket	string	-	scenario family: nav / bt (Hive partition column)
ped_planner	string	-	pedestrian planner used; alias of planner in metrics tables
robot_policy	string	-	robot policy name; empty string in divergence-mode trials
seed	int64	-	RNG seed for the trial
source_dir	string	-	originating sweep dir name (provenance)
success	float64	0/1	robot reached its goal within tolerance
time_to_goal_s	float64	s	robot wall-time to first goal arrival; NaN if no arrival
ttg_ratio	float64	ratio	time-to-goal divided by straight-line nominal time
path_efficiency	float64	ratio	robot arc-length / start-to-goal straight-line distance
n_robot_collisions	float64	count	frames in which robot overlapped any human
personal_space_violations	float64	count	frames in which a human entered the robot's personal-space disk
psv_per_sec	float64	1/s	personal_space_violations normalized by trial duration
frozen_at_end	float64	0/1	robot mean speed in last freeze-window seconds is below threshold
final_goal_dist	float64	m	robot-goal distance at trial end

failures

field	dtype	unit	description
scenario	string	-	canonical scenario id (Hive partition column)
bucket	string	-	scenario family: nav / bt (Hive partition column)
ped_planner	string	-	pedestrian planner used; alias of planner in metrics tables
robot_policy	string	-	robot policy name; empty string in divergence-mode trials
seed	int64	-	RNG seed for the trial
source_dir	string	-	originating sweep dir name (provenance)
cause	string	-	failure-cause label (success / collision / timeout_no_progress / ...)

Quick start

from datasets import load_dataset

# trajectories - stream a single (bucket, density, scenario, planner) partition
ds = load_dataset("neuripsqafyncakomkin/PEDS-37", "agent_states", split="train", streaming=True,
                  data_files="data/agent_states/bucket=nav/density=sparse/scenario=nav_sparse_corridor/planner=sfm/*.parquet")

# or all of one bucket (partition pushdown)
ds_nav = load_dataset("neuripsqafyncakomkin/PEDS-37", "agent_states", split="train", streaming=True,
                      data_files="data/agent_states/bucket=nav/**/*.parquet")

# per-trial scalar metrics
import pandas as pd
kin = pd.read_parquet("hf://datasets/neuripsqafyncakomkin/PEDS-37/data/metrics/kinematics_per_trial.parquet")
print(kin.groupby("planner")[["jerk", "curvature", "collisions"]].mean())

Citation

TODO bibtex (double blind)

License

CC BY 4.0 for the dataset.