AutonomousDriving
autonomous-driving-carla
CARLA Autonomous Driving Dataset
Custom datasets for autonomous driving in CARLA simulator
Created for CMPE 789 - Robot Perception at Rochester Institute of Technology
📊 Dataset Overview
This repository contains two custom-generated datasets from the CARLA 0.9.15 simulator for training autonomous driving perception models:
Dataset
Task
Images
Format
Size
YOLO Dataset
Object Detection
4,000
YOLOv8/v11
~1.2 GB
UFLD Dataset
Lane Detection
10,000… See the full description on the dataset page: https://huggingface.co/datasets/jkdxbns/autonomous-driving-carla.autonomous-driving-rss-traffic-flow-coherence-state-scoring-v0.1What this dataset tests
Whether a system can score traffic-flow coherence
before and after an ego action.
This is not collision detection.
It measures systemic stability.
Required outputs
pre_action_coherence_score
post_action_coherence_score
coherence_delta
shockwave_generation_flag
braking_propagation_depth
systemic_risk_score
Scoring conventions
coherence scores range 0 to 1
coherence_delta may be negative or positive
shockwave flag is 0 or 1
braking propagation depth… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/autonomous-driving-rss-traffic-flow-coherence-state-scoring-v0.1.autonomous-driving-social-coherence-field-mapping-v0.1What this dataset tests
Whether a system can score
the coherence of a multi-agent intention field.
This is not collision prediction.
It is social alignment measurement.
Required outputs
dominant_scene_intention
coherence_score
tension_index
conflict_pairs
cooperative_clusters
right_of_way_clarity
Scoring conventions
coherence and tension range 0 to 1
right_of_way_clarity is low, medium, or high
conflict_pairs names agent pairs likely to contest the same space… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/autonomous-driving-social-coherence-field-mapping-v0.1.autonomous-driving-intention-field-extraction-v0.1What this dataset tests
Whether a system can infer agent intentions
from context cues in complex driving scenes.
This is not trajectory prediction.
It is intention inference.
Required outputs
agent_id
inferred_intention
intention_confidence
time_horizon_s
alternative_intentions
stability_score
Scoring conventions
confidence and stability range 0 to 1
time horizon is seconds into the near future
Use case
Layer one of Intention Field and Social Coherence Maps.
This enables… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/autonomous-driving-intention-field-extraction-v0.1.autonomous-driving-ethical-cost-field-construction-v0.1
What this dataset tests
Whether an intelligence system can constructan ethical cost field for a driving scene.
The task is not to choose an action.The task is to model how harm distributes across agents.
Required outputs
ethical cost field
agent harm vectors
aggregate deformation score
rights infringement index
uncertainty band
Use case
Foundation layer for ethical navigation systems.Trains models to map harm before selecting actions.… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/autonomous-driving-ethical-cost-field-construction-v0.1.autonomous-driving-counterfactual-branch-generation-v0.1What this dataset tests
Whether a system can generate plausible counterfactual branches
from a seed driving scene.
This is not imagination.
It is constrained alternative generation.
Required outputs
branch_id
initiating_agent
counterfactual_action
plausibility_score
simulated_outcome_summary
branch_tree_depth
Scoring conventions
plausibility ranges 0 to 1
depth is the branch depth in the scenario tree
outcome summary describes the simulated end state and recovery
Use case… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/autonomous-driving-counterfactual-branch-generation-v0.1.