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HABIT: Human Action Benchmark for Interactive Traffic in CARLA

Accepted at WACV 2026 | Paper | arXiv | Project Page

Realistic motion-captured pedestrian behaviors expose critical failures in autonomous driving agents that remain hidden in scripted simulations.

HABIT benchmark demo showing motion-captured pedestrians in CARLA

Available — The benchmark framework, motion data, routes, and behavior labels are ready for evaluation. Agent integration guides are being finalized. Star & watch this repo for updates.

Why HABIT?

State-of-the-art AD agents achieve near-zero collisions on the CARLA Leaderboard — but when faced with realistic, motion-captured pedestrian behaviors, they fail dramatically:

Model Collisions/km pMAIS3+ (%) FPBR
InterFuser 5.24 10.96 0.33
TransFuser 7.43 12.94 0.12
BEVDriver 7.19 12.35 —

All three agents achieve near-zero collisions/km on standard CARLA benchmarks.

Key Features

  • 4,730 CARLA-ready motion files — motion-captured pedestrian animations (from HumanML3D/AMASS) converted for CARLA's skeletal system
  • 111 Town10HD routes — diverse evaluation routes with varied weather conditions
  • Realistic pedestrian behaviors — crossing, attempting-to-cross, and not-crossing scenarios driven by real human motion data
  • Injury severity metrics (pMAIS3+) — probability of Maximum Abbreviated Injury Scale >= 3, computed from collision dynamics
  • Standard AD metrics — driving score, route completion, infraction penalties, collisions per km
  • False Positive Braking Rate (FPBR) — measures unnecessary braking responses to non-crossing pedestrians
  • Behavior CSVs — categorized pedestrian behavior labels for scenario control

Release Status

Component Status
Benchmark framework (evaluator, scenario runner, metrics) Available
111 Town10HD routes with weather variations Available
Behavior CSVs (Crossing, Attempting, Not Crossing) Available
Pedestrian spawn points Available
Agent interface + NPC/dummy reference agents Available
pMAIS3+ injury severity + FPBR metrics Available
Motion-capture data (4,730 curated .pkl files) Available
InterFuser / TransFuser / BEVDriver integration guides Coming soon
Data processing pipeline (motion retargeting tools) Coming soon
Video-to-motion pipeline Coming soon

The benchmark is fully available for evaluation. Download the motion data, start CARLA, and run your agent — see Getting Started.

Quick Start

1. Create conda environment

conda env create -f environment.yml
conda activate habit

2. Start CARLA 0.9.14

# Docker
docker pull carlasim/carla:0.9.14
docker run --privileged --gpus all --net=host -e DISPLAY=$DISPLAY \
  carlasim/carla:0.9.14 /bin/bash ./CarlaUE4.sh -RenderOffScreen

# Or native install (see docs/getting_started.md)

3. Set environment and run

export CARLA_ROOT=/path/to/carla
export PYTHONPATH=$CARLA_ROOT/PythonAPI/carla:$(pwd):$(pwd)/scenario_runner:$PYTHONPATH

bash scripts/run_evaluation.sh leaderboard/autoagents/npc_agent.py

Note: The full benchmark requires motion data in data/motions/. Download the 4,730 .pkl files from Google Drive and extract them there. See Getting Started for details.

How It Works

HABIT replaces CARLA's scripted pedestrian AI with motion-captured skeletal animations. Each evaluation route spawns pedestrians at designated points along the ego vehicle's path, playing back real human motion data through CARLA's bone animation system.

Route XML + Behavior CSVs + Motion Data
              |
              v
    PedBackgroundBehavior
    (skeletal animation playback)
              |
              v
  CARLA 0.9.14 (Town10HD)  <-->  AD Agent (AutonomousAgent interface)
              |
              v
    Metrics: Driving Score, pMAIS3+, FPBR

The evaluator uses the CARLA Leaderboard framework with custom scenario runners that handle pedestrian spawning, animation, and collision-aware injury computation.

Documentation

  • Getting Started — prerequisites, installation, and first evaluation run
  • Custom Agents — how to implement and run your own autonomous agent
  • Agent Environments — separate conda environments for InterFuser, TransFuser, BEVDriver
  • Metrics — detailed description of all evaluation metrics and the penalty system

Citation

@InProceedings{Ramesh_2026_WACV,
  author    = {Ramesh, Mohan and Azer, Mark and Flohr, Fabian},
  title     = {{HABIT}: Human Action Benchmark for Interactive Traffic in {CARLA}},
  booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  month     = {March},
  year      = {2026},
  pages     = {7148-7157}
}

What's New

  • Motion data released — 4,730 curated .pkl files now available for download
  • Paper-matched parameters — activation distance, collision threshold, and pedestrian counts aligned with WACV 2026 evaluation
  • Reproducible seeding — deterministic pedestrian spawning via random.seed(2000)
  • Semantic segmentation sensor — agents can now use sensor.camera.semantic_segmentation
  • set_animations() API — agents can access pedestrian ground truth for evaluation

Roadmap

  1. Agent integration guides — step-by-step setup for InterFuser, TransFuser, BEVDriver
  2. Data processing pipeline — tools for converting HumanML3D/AMASS motions to CARLA-ready format
  3. Benchmark generation tools — scripts for generating new routes, spawn points, behavior CSVs
  4. Video-to-motion pipeline — generate pedestrian motions from video footage

License

This project is licensed under the MIT License. See LICENSE for details.