Driver behaviour recognition

driver has a powerful smartphone equipped with many sensors at hand in the vehicle. Furthermore, recent advances in Machine Learning (ML) made it possible to analyze large amounts of data and to generate new outcomes. In this work we discuss how ML can be . A driving behavior recognition method based on Gaussian mixture hidden Markov model (GMM-HMM) is proposed. Firstly, preprocess the NGSIM data sample, take the surrounding vehicles lateral displacement, lateral speed are taken as the HMM observation sequence, the HMM driving behavior recognition model is established. proposed with a HMM method as the driver behavior recognition for emergency and normal lane changes. The authors trained the HMM models using driver behavior data from a driving simulator. In [17], a model for recognition of driving events using discrete HMMs is presented utilizing longitudinal and lateral acceleration and speed data from a.

We propose a driver behavior recognition method using Hidden Markov Models (HMMs) to characterize and detect driving maneuvers and place it in the framework of a cognitive model of human behavior. ally. Current driver behaviour recognition methods mostly target the bus or car drivers and can hardly be implemented for subways, because subway drivers follow a rigid work-ing code that needs a time sequence of movements to describe. In this study, we propose a recognition model to automatically recognise behaviours from single-frame images that. driver behavior recognition methods requires a thorough understanding of driver behavior and the construction of a model capable of explaining and reproducing drivers' behavioral characteristics. Among various driving actions, this study focused on lane change maneuvers. Some methods have been developed previously to esti-.

A machine learning based method to silently and continuously profile the driver by analyzing built-in vehicle sensors is proposed, finding the most relevant. simulator. Analysis of these models after training and recognition tests showed that driver behavior modeling and recognition of different types of lane changes. Graphical Models for Driver Behavior Recognition in a SmartCar. Nuria Oliver Alex P. Pentland. Media Laboratory. Massachusetts Institute of Technology (MIT).