There are 3 repositories under driving-behavior topic.
DBNet: A Large-Scale Dataset for Driving Behavior Learning, CVPR 2018
Simple visualization of NGSIM I-80 dataset
Driving risk assessment with deep learning using a monocular camera. Related paper: https://arxiv.org/abs/1906.02859
NGSIM I-80 dataset Leader - Follower Vehicle Trajectory Pairs
Zenroad - Open-source telematics app for iOS. The application is suitable for UBI (Usage-based insurance), shared mobility, transportation, safe driving, tracking, family trackers, drive-coach, and other driving mobile applications
Automile offers a simple, smart, cutting-edge telematics solution for businesses to track and manage their business vehicles.
An Implementation of Fatigue Driving Detect, Huawei Cloud Track, 18th Challenge Cup
Automile offers a simple, smart, cutting-edge telematics solution for businesses to track and manage their business vehicles.
At Yuñ Solutions, we are committed to democratize technology and make information accessible to all. We are sharing the data collected from our proprietary OBD device (LEVIN) during beta testing. The shared data has been collected for almost 4 months on 30 cars.
Demo telematics app for React-Native. The application walks you through the telematics SDK integration.
A mobile app created off of Comma.ai mobile app chffr
Telematics SDK Login and authentication framework for Android apps
This is an app that demonstrates using of the Telematics SDK and walks you through the integration. The SDK tracks user location and driving behavior such as speeding, cornering, braking, distracted driving, and other parameters.
Deep Learning and Entity Embeddings to predict driving behaviour and cluster accident hotspots
This is an app that demonstrates using of the Telematics SDK and walks you through the integration. The SDK tracks user location and driving behavior such as speeding, cornering, braking, distracted driving, and other parameters.
Telematics SDK Login and authentication framework for iOS apps
This repository contains the algorithms implementation for vehicles scheduling, dispatching and planning in complicated scenarios such as intersection, junction etc. Currently we are developing learnable driving policies module via inverse reinforcement learning algorithms.
This is an app that demonstrates using of the Telematics SDK and walks you through the integration. The SDK tracks user location and driving behavior such as speeding, cornering, braking, distracted driving, and other parameters.
decision-making processes of human drivers
Driving behaviour classification using acceleration and rotation data.
End-to-End learning for cloning human driving behavioral
The Demo app introduces the Telematics SDK integration for Android.
Predicting driving behavior with machine learning involves using algorithms and statistical models to analyze data of driving pattern.
A driving simulator to test concentration while driving
A driver assistance system that will remind a driver to follow eco-driving principles when a certain principle is violated
Evolutionary Path Finding.
Learning when agent can talk to drivers using PazNet
Path Planning Project in the Udacity's Self Driving Car Engineer Course