38 research outputs found
A Deep Learning Based Model for Driving Risk Assessment
In this paper a novel multilayer model is proposed for assessing driving risk. Studying aggressive behavior via massive driving data is essential for protecting road traffic safety and reducing losses of human life and property in smart city context. In particular, identifying aggressive behavior and driving risk are multi-factors combined evaluation process, which must be processed with time and environment. For instance, improper time and environment may facilitate abnormal driving behavior. The proposed Dynamic Multilayer Model consists of identifying instant aggressive driving behavior that can be visited within specific time windows and calculating individual driving risk via Deep Neural Networks based classification algorithms. Validation results show that the proposed methods are particularly effective for identifying driving aggressiveness and risk level via real dataset of 2129 drivers’ driving behavior
A Machine Learning Approach for Driver Identification Based on CAN-BUS Sensor Data
Driver identification is a momentous field of modern decorated vehicles in
the controller area network (CAN-BUS) perspective. Many conventional systems
are used to identify the driver. One step ahead, most of the researchers use
sensor data of CAN-BUS but there are some difficulties because of the variation
of the protocol of different models of vehicle. Our aim is to identify the
driver through supervised learning algorithms based on driving behavior
analysis. To determine the driver, a driver verification technique is proposed
that evaluate driving pattern using the measurement of CAN sensor data. In this
paper on-board diagnostic (OBD-II) is used to capture the data from the CAN-BUS
sensor and the sensors are listed under SAE J1979 statement. According to the
service of OBD-II, drive identification is possible. However, we have gained
two types of accuracy on a complete data set with 10 drivers and a partial data
set with two drivers. The accuracy is good with less number of drivers compared
to the higher number of drivers. We have achieved statistically significant
results in terms of accuracy in contrast to the baseline algorith