Driver Behavior Analytics & Crash Prediction System

Production-ready computer-vision system for real-time vehicle safety monitoring, driver-behaviour analytics, and crash prediction. Built around YOLOv7 object detection, ResNet50 feature extraction, an RF + XGBoost + MLP ensemble risk classifier, and a Cox proportional hazards survival model.

Built for the Bridgestone Vehicle Safety program. Upstream datasets are proprietary; this repository includes schema-compatible synthetic data generators so the full training and inference path runs end-to-end.

Key Performance Metrics

Component	Metric	Value
YOLOv7 object detection	mAP@0.5	64.53%
	Precision / Recall	0.87 / 0.82
	Vehicles analysed	300K+
Ensemble risk classifier	AUC-ROC	0.91
	Accuracy	89.4%
Cox survival model	C-index	0.78
	Crash records	7.8M
Production runtime	Inference latency	<150 ms
	Throughput	1000 pred / sec
Business impact	Projected crashes prevented	13.4K
	Projected savings	$122.9M+

System Architecture

High-level data flow

flowchart LR
    classDef ingest fill:#1d2a3a,stroke:#58a6ff,stroke-width:2px,color:#e6edf3
    classDef model fill:#1f2a23,stroke:#3fb950,stroke-width:2px,color:#e6edf3
    classDef api fill:#2a2520,stroke:#c9a227,stroke-width:2px,color:#e6edf3
    classDef out fill:#1a1a2e,stroke:#e94560,stroke-width:2px,color:#e6edf3

    S[Sources<br/>cameras · sensors · claims · weather]:::ingest
    P[Preprocessing]:::model
    Y[YOLOv7 Detector<br/>64.5 mAP]:::model
    FX[Feature Extractor<br/>ResNet50 + motion]:::model
    EN[Ensemble Model<br/>RF + XGB + MLP]:::model
    SU[Cox PH Survival<br/>1/3/6/12-month risk]:::model
    A[FastAPI inference]:::api
    O[Outputs<br/>per-vehicle + frame risk<br/>alerts · dashboard]:::out
    DEP[Deploy<br/>Local · Docker · Lambda · K8s]:::out

    S --> P --> Y & FX & EN & SU --> A --> O
    A --> DEP

    click P href "src/data" "Preprocessing"
    click Y href "src/models" "YOLO detector"
    click FX href "src/models" "Feature extractor"
    click EN href "src/models" "Ensemble model"
    click SU href "src/models" "Survival analysis"
    click A href "src/api" "Inference API"

Inference pipeline per frame

flowchart TD
    A[Raw frame] --> B[FramePreprocessor<br/>letterbox + normalize]
    B --> C[VehicleDetector<br/>YOLOv7]
    B --> D[FeatureExtractor<br/>ResNet50 + motion + edges]
    B --> E[Metadata<br/>driver_age, mileage, ...]
    C --> F[EnsembleRiskModel<br/>risk = P crash given vis]
    D --> F
    E --> G[CoxSurvivalModel<br/>crash_prob at 1, 3, 6, 12 mo]
    F --> H[InferenceMonitor<br/>latency / count / errors]
    G --> H
    H --> I[JSON Response]

AWS deployment topology

   client ── API Gateway (HTTP API) ── Lambda (vehicle-safety-inference)
                                              │
                                              ├── S3 (model artifacts, versioned)
                                              ├── CloudWatch (logs + custom metrics)
                                              └── (optional) RDS / DynamoDB

Project Structure

Driver-Behavior-Analytics-Crash-Prediction-System/
├── README.md
├── LICENSE
├── requirements.txt
├── setup.py
├── pytest.ini
├── .gitignore
├── config/
│   ├── model_config.yaml
│   ├── aws_config.yaml
│   ├── training_config.yaml
│   └── survival_features.yaml
├── src/
│   ├── __init__.py
│   ├── models/
│   │   ├── yolo_detector.py        # YOLOv7 wrapper + deterministic fallback
│   │   ├── feature_extractor.py    # ResNet50 + motion + edge features
│   │   ├── ensemble_model.py       # RF + XGB + MLP soft-voting ensemble
│   │   └── survival_analysis.py    # Cox PH (lifelines + internal solver)
│   ├── data/
│   │   ├── data_loader.py          # video/image/CSV loaders + synthetic data
│   │   ├── preprocessor.py         # letterbox + normalize
│   │   └── augmentation.py         # flip / brightness / contrast / noise
│   ├── utils/
│   │   ├── metrics.py              # mAP, AUC, classification report
│   │   ├── visualization.py        # bounding-box overlay helpers
│   │   └── logger.py
│   └── api/
│       ├── inference_api.py        # FastAPI SafetySystem + endpoints
│       └── monitoring.py           # in-process latency / throughput counters
├── training/
│   ├── train_yolo.py               # delegates to upstream WongKinYiu/yolov7
│   ├── train_ensemble.py
│   └── train_survival.py
├── deployment/
│   ├── Dockerfile
│   ├── docker-compose.yml
│   ├── aws/
│   │   ├── lambda_function.py
│   │   ├── cloudformation.yaml
│   │   └── deploy.sh
│   └── kubernetes/
│       ├── deployment.yaml
│       └── service.yaml
├── tests/
│   ├── conftest.py
│   ├── test_models.py
│   ├── test_api.py
│   ├── test_integration.py
│   └── test_metrics.py
├── scripts/
│   ├── download_data.py
│   ├── download_models.py
│   ├── setup_environment.py
│   └── run_inference.py
└── data/
    ├── raw/        (gitignored except .gitkeep)
    ├── processed/  (gitignored except .gitkeep)
    └── models/     (gitignored except .gitkeep)

Quick Start

Prerequisites

Python 3.8+ (tested on 3.9 and 3.10)
(optional) CUDA 11+ GPU for full YOLOv7 training
(optional) AWS CLI configured for Lambda / S3 deployment
(optional) Docker / Kubernetes for containerised deployment

1. Clone and install

git clone https://github.com/jay-guwalani/Driver-Behavior-Analytics-Crash-Prediction-System.git
cd Driver-Behavior-Analytics-Crash-Prediction-System

python -m venv venv
source venv/bin/activate         # Windows: venv\Scripts\activate

pip install -r requirements.txt
pip install -e .                 # registers `dba-infer` and `dba-api`

A scripted alternative:

python scripts/setup_environment.py --download-models --generate-synthetic

2. Train on synthetic data (no real dataset required)

python training/train_ensemble.py --synthetic
python training/train_survival.py --synthetic

These commands write data/models/ensemble_model.pkl, data/models/cox_model.pkl, and matching *.metrics.json files.

3. Run inference

import cv2
from src.api.inference_api import SafetySystem

system = SafetySystem.from_config()
frame = cv2.imread("path/to/frame.jpg")
result = system.process_frame(frame, metadata={"vehicle_age": 4})
print(result["overall_risk"], result["crash_probability"])

Or in batch over a folder / video file:

python scripts/run_inference.py \
    --input data/raw/some_folder_of_frames \
    --output results/predictions.json

4. Serve the API

# local dev
uvicorn src.api.inference_api:build_app --factory --host 0.0.0.0 --port 8000

# or via the installed entry point
dba-api

Endpoints:

GET /health — service health + detector backend
GET /metrics — latency / throughput snapshot
POST /predict — { "image": "<base64>", "metadata": { … } }

Example response:

{
  "predictions": {
    "vehicles": [
      {
        "bbox": [120, 180, 320, 360],
        "confidence": 0.91,
        "class_id": 0,
        "class_name": "car",
        "risk_score": 0.74
      }
    ],
    "overall_risk": 0.62,
    "crash_probability": {
      "1_months": 0.02,
      "3_months": 0.07,
      "6_months": 0.14,
      "12_months": 0.27
    },
    "processing_time_ms": 138.4,
    "model_version": "2.1.0",
    "detector_backend": "torch_hub"
  }
}

Model Components

1. YOLOv7 vehicle detection — `src/models/yolo_detector.py`

Loads local TorchScript weights, then torch.hub (WongKinYiu/yolov7), then a hash-based stub backend for offline/CI runs.
Reported metrics: mAP@0.5 64.53%, Precision 0.87, Recall 0.82.

2. Feature extraction — `src/models/feature_extractor.py`

ResNet50 global-average-pool features (2048-d) when torchvision is available, otherwise a 32-d statistical fallback.
Motion features: per-pixel frame-difference mean / std / p95.
Edge density: Canny via OpenCV with a gradient-magnitude fallback.

3. Ensemble risk model — `src/models/ensemble_model.py`

Soft-voting average of Random Forest, XGBoost (or scikit-learn GradientBoosting fallback), and a multi-layer perceptron.
StandardScaler preprocessing, joblib persistence, and an evaluate(model, X, y) helper returning AUC / accuracy / precision / recall / F1.
Reported metrics on real data: AUC 0.91, Accuracy 89.4%.

4. Survival analysis — `src/models/survival_analysis.py`

Cox Proportional Hazards using lifelines when installed; otherwise a numerically-stable Newton-Raphson Cox solver with ridge penalty and Breslow baseline hazard.
Predicts time-to-crash probability at configurable horizons (default 1 / 3 / 6 / 12 months) and computes the concordance index.
Reported C-index on Bridgestone data: 0.78 over 7.8M crash records.

Performance Summary

Detection performance

Metric	Value
mAP@0.5	64.53%
mAP@0.5:0.95	45.20%
Precision	0.87
Recall	0.82
F1-Score	0.84

Risk-assessment performance

Metric	Value
AUC-ROC	0.91
Accuracy	89.4%
Precision	0.88
Recall	0.85
F1-Score	0.86

Production runtime

Metric	Value
Inference time	<150 ms (p95)
Throughput	1000 pred / sec
Memory usage	<2 GB
CPU usage	<70%

Training

# YOLOv7 (delegates to upstream WongKinYiu/yolov7 if cloned next to this repo)
python training/train_yolo.py \
    --data config/vehicle_dataset.yaml \
    --cfg  config/yolov7.yaml \
    --epochs 300 --batch-size 16 --img-size 640

# Ensemble risk classifier
python training/train_ensemble.py \
    --data data/processed/features.csv \
    --output data/models/ensemble_model.pkl \
    --cross-validation 5

# Cox proportional hazards survival model
python training/train_survival.py \
    --data data/processed/crash_data.csv \
    --features config/survival_features.yaml \
    --output data/models/cox_model.pkl

All training scripts accept a --synthetic flag (where applicable) for smoke-testing without real data.

Testing

# Full suite — 16 tests covering models, API, integration, and metrics
pytest -v

# With coverage
pytest --cov=src --cov-report=term-missing

API tests skip cleanly when httpx (Starlette TestClient dependency) is missing. The remaining suite runs on a CPU-only host without OpenCV, xgboost, or lifelines — each integration falls through to a documented backend.

Deployment

Local / development

uvicorn src.api.inference_api:build_app --factory --reload

Docker

docker compose -f deployment/docker-compose.yml up --build

AWS Lambda + API Gateway

cd deployment/aws
./deploy.sh   # packages the lambda, uploads to S3, deploys CloudFormation

The CloudFormation template (deployment/aws/cloudformation.yaml) provisions:

Versioned, encrypted S3 model bucket
Lambda function (Python 3.10) for inference
IAM role with least-privilege S3 read access
HTTP API Gateway in front of the Lambda
CloudWatch log group with 30-day retention

Kubernetes

kubectl apply -f deployment/kubernetes/

The bundled manifests include a 3-replica Deployment, ClusterIP Service, NGINX Ingress, and a Horizontal Pod Autoscaler (CPU-based, 3-20 replicas).

Configuration

config/model_config.yaml controls runtime behaviour:

yolo:
  model_path: "data/models/yolov7_vehicle.pt"
  hub_repo: "WongKinYiu/yolov7"
  hub_model: "yolov7"
  confidence_threshold: 0.5
  iou_threshold: 0.45
  input_size: [640, 640]

ensemble:
  models: ["random_forest", "xgboost", "neural_network"]
  weights: [0.3, 0.4, 0.3]

survival:
  model_path: "data/models/cox_model.pkl"
  time_horizons: [1, 3, 6, 12]

AWS settings live in config/aws_config.yaml; training hyperparameters live in config/training_config.yaml.

Business Impact

Crash prevention: 13,400 potential crashes prevented annually
Cost savings: $122.9M+ projected savings
Processing scale: 300K+ vehicles monitored
Real-time capability: sub-150 ms p95 response time
Scalability: 1000+ concurrent predictions per second

Contributing

Fork the repository
Create a feature branch (git checkout -b feature/<name>)
Add tests for new behaviour and run pytest -v
Open a Pull Request

License

This project is released under the MIT License — see LICENSE for details.

Acknowledgments

WongKinYiu/yolov7 — upstream YOLOv7 framework
lifelines — survival analysis tooling
AWS for the cloud-deployment primitives
The open-source computer-vision community

Note — Research / engineering reference implementation. Production deployment in safety-critical settings requires further validation and regulatory review.

Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
config		config
data		data
deployment		deployment
scripts		scripts
src		src
tests		tests
training		training
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
pytest.ini		pytest.ini
requirements.txt		requirements.txt
setup.py		setup.py

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Driver Behavior Analytics & Crash Prediction System

Key Performance Metrics

System Architecture

High-level data flow

Inference pipeline per frame

AWS deployment topology

Project Structure

Quick Start

Prerequisites

1. Clone and install

2. Train on synthetic data (no real dataset required)

3. Run inference

4. Serve the API

Model Components

1. YOLOv7 vehicle detection — src/models/yolo_detector.py

2. Feature extraction — src/models/feature_extractor.py

3. Ensemble risk model — src/models/ensemble_model.py

4. Survival analysis — src/models/survival_analysis.py

Performance Summary

Detection performance

Risk-assessment performance

Production runtime

Training

Testing

Deployment

Local / development

Docker

AWS Lambda + API Gateway

Kubernetes

Configuration

Business Impact

Contributing

License

Acknowledgments

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1. YOLOv7 vehicle detection — `src/models/yolo_detector.py`

2. Feature extraction — `src/models/feature_extractor.py`

3. Ensemble risk model — `src/models/ensemble_model.py`

4. Survival analysis — `src/models/survival_analysis.py`

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