README.md

metadata

title: RoadSafety OpenEnv
emoji: 🏍️
colorFrom: blue
colorTo: red
sdk: docker
pinned: false

Road Safety OpenEnv

This is a fully compliant OpenEnv project modeling a real-world task: AI-driven autonomous crash detection and emergency response system.

Motivation & Domain

Every day, thousands of riders experience accidents. Immediate SOS dispatch saves lives. This environment uses a realistic IMU dataset containing simulated gyroscope, accelerometer, speed, and motion intensity readings representing motorcycle riders in transit.

The goal of the agent is to read these sequential data frames (simulating an onboard edge AI) and correctly decipher if simple motion variances are normal commuting or a crash requiring SOS dispatch.

Features

• Real-World Task Simulation: Processes 10-second data windows of 6-axis IMU sensors, speed, and overall motion intensity metrics.
• OpenEnv Spec Compliance: Implemented using openenv-core. Includes openenv.yaml. Uses strict Pydantic typed input/output models (RiderSafetyObservation, RiderSafetyAction).
• 3 Task Graders:
  • Easy: Identifies completely normal driving vs high intensity clear crashes. Graded exactly.
  • Medium: Partial rewards given. Evaluates edge case driving behaviors and borderline intense movements.
  • Hard: Complex anomalies. False alarms are strictly penalized with 0.0 reward.
• Robust Reward Design: Rewards emphasize correct identification and heavily penalize false emergency deployments to mimic a real service constraint.

Setup and Usage

Prerequisites

• Python 3.9+
• Docker (optional but recommended for Hugging Face Spaces integration)

Local Environment Setup

# Install dependencies
pip install -r requirements.txt

# Run the OpenEnv server
uvicorn server.app:app --host 0.0.0.0 --port 8000

Running Inference Baseline

This environment includes a completed inference.py ready to benchmark LLMs.

# Set specific credentials or use environment defaults
export API_BASE_URL="https://api.together.xyz/v1"
export MODEL_NAME="meta-llama/Llama-3-8b-chat-hf"
export HF_TOKEN="HF_TOKEN"

# Run inference
python inference.py

Docker Deployment

Ready for HuggingFace Spaces.

docker build -t roadsafety-env .
docker run -p 8000:8000 roadsafety-env

Environment Spaces

Observation Space:

• step_index (int): Current step in the sequence.
• sensor_summary (str): Summary string of speed, motion intensity, and 3D acceleration.
• audio_transcript (str): Environmental context.

Action Space:

• decision (str): Action to take. Must be one of IGNORE, MONITOR, or DISPATCH_SOS.
• message (str): The reasoning behind the decision.

Tasks Explained

Each sequence dynamically tests the agent's ability to maintain composure or react decisively. We load data from road_accident_imu_dataset_8000.csv to ensure varied telemetry. Scores enforce high precision and high recall performance constraints.