基于深度学习的驾驶员分心驾驶行为（疲劳+危险行为）预警系统使用YOLOv5+Deepsort实现驾驶员的危险驾驶行为的预警监测
A traffic simulation software based on SUMO that incorporates driver behavior.
Datasets, code and models from our driver gaze estimation works over the last few years.
This is the codebase for our ICRA 2020 submission, GraphRQI: Classifying Driver Behaviors Using Graph Spectrums.
Akshay Rangesh and Mohan M. Trivedi, "HandyNet: A One-stop Solution to Detect, Segment, Localize & Analyze Driver Hands," IEEE Conference on Computer Vision and Pattern Recognition - 3D HUMANS Workshop, 2018.
The official repository for "Look where you’re going: Classifying drivers' attention through 3D gaze estimation"
Akshay Rangesh and Mohan Trivedi, "Forced Spatial Attention for Driver Foot Activity Classification," ICCV Workshop on Assistive Computer Vision and Robotics, 2019.
AI for social good /Risk assessment
Explore practical, real-world solutions with Power BI and Microsoft Fabric — from insightful dashboards to powerful data workflows. Perfect for learning, inspiration, and accelerating your data projects.
A centralized dataset repository for Driver Behavior Time Series Classification
A stateful vehicle simulation framework for modeling vehicle dynamics, driver behavior, environmental conditions, and fleet operations.
ADAS Driver Action Prediction using Sensor Fusion and Deep Learning for intelligent driver behaviour analysis and road safety enhancement.
Real-time collaborative parking optimization system using advanced algorithms including game theory Nash equilibrium, A* pathfinding, ML forecasting, and driver psychology modeling. CIS 505 project demonstrating practical algorithm applications in urban planning.
Offline trip-safety GUI and batch inference with a 19-feature decision-tree pipeline, 63 tests, and a reproducible ~78.8 MB Windows executable smoke test.
traffic sign detection using ML
Enterprise-grade AI-powered fleet intelligence platform built with FastAPI, PostgreSQL, Redis, Docker, and Machine Learning for real-time telemetry, predictive maintenance, driver behavior analysis, anomaly detection, and explainable AI.
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