scholarly journals Sensor-Based Extraction Approaches of In-Vehicle Information for Driver Behavior Analysis

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5197
Author(s):  
Beomjun Kim ◽  
Yunju Baek
Keyword(s):  
Behavior Analysis ◽  
Electronic Control Unit ◽  
Driver Behavior ◽  
Sensor Data ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Steering Wheel ◽  
Automatic Extraction ◽  
Area Network ◽  
Vehicle Information

Advances in vehicle technology have resulted in the development of vehicles equipped with sensors to acquire standardized information such as engine speed and vehicle speed from the in-vehicle controller area network (CAN) system. However, there are challenges in acquiring proprietary information from CAN frames, such as the brake pedal and steering wheel operation, which are essential for driver behavior analysis. Such information extraction requires electronic control unit identifier analysis and accompanying data interpretation. In this paper, we present a system for the automatic extraction of proprietary in-vehicle information using sensor data correlated with the desired information. First, the proposed system estimates the vehicle’s driving status through threshold-, random forest-, and long short-term memory-based techniques using inertial measurement unit and global positioning system values. Then, the system segments in-vehicle CAN frames using the estimation and evaluates each segment with our scoring method to select suitable candidates by examining the similarity between each candidate and its estimation through the suggested distance matching technique. We conduct comprehensive experiments of the proposed system using real vehicles in an urban environment. Performance evaluation shows that the estimation accuracy of the driving condition is 84.20%, and the extraction accuracy of the in-vehicle information is 82.31%, which implies that the presented approaches are quite feasible for automatic extraction of proprietary in-vehicle information.

scholarly journals Extraction Method of Driver’s Mental Component Based on Empirical Mode Decomposition and Approximate Entropy Statistic Characteristic in Vehicle Running State

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Shuan-Feng Zhao ◽  
Wei Guo ◽  
Chuan-wei Zhang
Keyword(s):  
Empirical Mode Decomposition ◽  
Driver Behavior ◽  
Approximate Entropy ◽  
Driving Behavior ◽  
Steering Wheel ◽  
State Data ◽  
The Road ◽  
Mode Decomposition ◽  
Function Selection ◽  
Vehicle Information

In the driver fatigue monitoring technology, the essence is to capture and analyze the driver behavior information, such as eyes, face, heart, and EEG activity during driving. However, ECG and EEG monitoring are limited by the installation electrodes and are not commercially available. The most common fatigue detection method is the analysis of driver behavior, that is, to determine whether the driver is tired by recording and analyzing the behavior characteristics of steering wheel and brake. The driver usually adjusts his or her actions based on the observed road conditions. Obviously the road path information is directly contained in the vehicle driving state; if you want to judge the driver’s driving behavior by vehicle driving status information, the first task is to remove the road information from the vehicle driving state data. Therefore, this paper proposes an effective intrinsic mode function selection method for the approximate entropy of empirical mode decomposition considering the characteristics of the frequency distribution of road and vehicle information and the unsteady and nonlinear characteristics of the driver closed-loop driving system in vehicle driving state data. The objective is to extract the effective component of the driving behavior information and to weaken the road information component. Finally the effectiveness of the proposed method is verified by simulating driving experiments.

Intelligent Tire Based Pressure Monitoring Algorithm

2017 ◽  
Author(s):  
Seyedmeysam Khaleghian ◽  
Saied Taheri
Keyword(s):  
Experimental Data ◽  
Angular Velocity ◽  
Angular Acceleration ◽  
Sensor Data ◽  
Measurement Unit ◽  
Steering Wheel ◽  
Pressure Monitoring ◽  
Radial Acceleration ◽  
Instrumented Vehicle ◽  
Monitoring Algorithm

Intelligent tire is a relatively new technology that provides useful tire-road contact information by directly monitoring the interaction between the tire and the road. Different types of sensors are attached to the tire inner-liner for this purpose; the sensor data then will be used to estimate the tire-road contact parameters as well as to monitor the tire conditions. In this study, a tri-axial accelerometer was used and a two-steps intelligent tire based pressure monitoring algorithm was developed in this study. First, the angular velocity of the wheel was estimated based on the parameters extracted from the acceleration components through a trained neural network. Then the estimated wheel angular velocity from the first step was used along with the acceleration components to estimate the power of radial acceleration. The estimated power was compared to the actual one and the tire pressure condition was judged to be “normal” or “low”. To train the neural networks, the experimental data collected using an instrumented vehicle was used. A VW Jetta 2003 was used for this purpose and instrumented with appropriate sensors; intelligent tires, steering wheel sensor to measure the steering angle, steering velocity and steering torque, encoders to measure the angular speed of the wheels and an Inertial Measurement Unit (IMU) to measure the vehicle linear and angular acceleration. Another set of experimental data with different tire pressures and different vehicle velocity was then used to validate the algorithm; good agreements were observed between the estimated tire pressures and the actual ones.

scholarly journals Lightweight Driver Behavior Identification Model with Sparse Learning on In-Vehicle CAN-BUS Sensor Data

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5030 ◽  
Author(s):  
Shan Ullah ◽  
Deok-Hwan Kim
Keyword(s):  
Embedded System ◽  
Performance Metrics ◽  
Short Term Memory ◽  
Can Bus ◽  
Driver Behavior ◽  
Window Size ◽  
Sensor Data ◽  
Sparse Learning ◽  
Area Network ◽  
Time Step

This study focuses on driver-behavior identification and its application to finding embedded solutions in a connected car environment. We present a lightweight, end-to-end deep-learning framework for performing driver-behavior identification using in-vehicle controller area network (CAN-BUS) sensor data. The proposed method outperforms the state-of-the-art driver-behavior profiling models. Particularly, it exhibits significantly reduced computations (i.e., reduced numbers both of floating-point operations and parameters), more efficient memory usage (compact model size), and less inference time. The proposed architecture features depth-wise convolution, along with augmented recurrent neural networks (long short-term memory or gated recurrent unit), for time-series classification. The minimum time-step length (window size) required in the proposed method is significantly lower than that required by recent algorithms. We compared our results with compressed versions of existing models by applying efficient channel pruning on several layers of current models. Furthermore, our network can adapt to new classes using sparse-learning techniques, that is, by freezing relatively strong nodes at the fully connected layer for the existing classes and improving the weaker nodes by retraining them using data regarding the new classes. We successfully deploy the proposed method in a container environment using NVIDIA Docker in an embedded system (Xavier, TX2, and Nano) and comprehensively evaluate it with regard to numerous performance metrics.

Robust-PCA-based hierarchical plane extraction for application to geometric 3D indoor mapping

2014 ◽  
Vol 41 (2) ◽  
pp. 203-212 ◽  
Author(s):  
Suyong Yeon ◽  
ChangHyun Jun ◽  
Hyunga Choi ◽  
Jaehyeon Kang ◽  
Youngmok Yun ◽  
...  
Keyword(s):  
Principal Component ◽  
Point Clouds ◽  
Previous Method ◽  
Divide And Conquer ◽  
Sensor Data ◽  
Estimation Accuracy ◽  
Content Type ◽  
Extraction Algorithm ◽  
Plane Extraction ◽  
Scan Data

Purpose – The authors aim to propose a novel plane extraction algorithm for geometric 3D indoor mapping with range scan data. Design/methodology/approach – The proposed method utilizes a divide-and-conquer step to efficiently handle huge amounts of point clouds not in a whole group, but in forms of separate sub-groups with similar plane parameters. This method adopts robust principal component analysis to enhance estimation accuracy. Findings – Experimental results verify that the method not only shows enhanced performance in the plane extraction, but also broadens the domain of interest of the plane registration to an information-poor environment (such as simple indoor corridors), while the previous method only adequately works in an information-rich environment (such as a space with many features). Originality/value – The proposed algorithm has three advantages over the current state-of-the-art method in that it is fast, utilizes more inlier sensor data that does not become contaminated by severe sensor noise and extracts more accurate plane parameters.

scholarly journals Vehicle Sideslip Angle Estimation Using Two Single-Antenna GPS Receivers

2010 ◽  
Author(s):  
Jong-Hwa Yoon ◽  
Huei Peng
Keyword(s):  
Low Cost ◽  
Longitudinal Velocity ◽  
Stability Control ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Sideslip Angle ◽  
Gps Receivers ◽  
Single Antenna ◽  
Active Safety Systems ◽  
Safety Systems

Knowing vehicle sideslip angle accurately is critical for active safety systems such as Electronic Stability Control (ESC). Vehicle sideslip angle can be measured through optical speed sensors, or dual-antenna GPS. These measurement systems are costly (∼$5k to $100k), which prohibits wide adoption of such systems. This paper demonstrates that the vehicle sideslip angle can be estimated in real-time by using two low-cost single-antenna GPS receivers. Fast sampled signals from an Inertial Measurement Unit (IMU) compensate for the slow update rate of the GPS receivers through an Extended Kalman Filter (EKF). Bias errors of the IMU measurements are estimated through an EKF to improve the sideslip estimation accuracy. A key challenge of the proposed method lies in the synchronization of the two GPS receivers, which is achieved through an extrapolated update method. Analysis reveals that the estimation accuracy of the proposed method relies mainly on vehicle yaw rate and longitudinal velocity. Experimental results confirm the feasibility of the proposed method.

scholarly journals OUTDOOR AR-APPLICATION FOR THE DIGITAL MAP TABLE

2020 ◽  
Vol XLIV-3/W1-2020 ◽  
pp. 93-98
Author(s):  
S. Maier ◽  
T. Gostner ◽  
F. van de Camp ◽  
A. H. Hoppe
Keyword(s):  
Augmented Reality ◽  
Geographical Location ◽  
Satellite System ◽  
Reference Points ◽  
Sensor Data ◽  
Measurement Unit ◽  
Positional Accuracy ◽  
Digital Map ◽  
The World ◽  
Global Navigation Satellite

Abstract. In many fields today, it is necessary that a team has to do operational planning for a precise geographical location. Examples for this are staff work, the preparation of surveillance tasks at major events or state visits and sensor deployment planning for military and civil reconnaissance. For these purposes, Fraunhofer IOSB is developing the Digital Map Table (DigLT). When making important decisions, it is often helpful or even necessary to assess a situation on site. An augmented reality (AR) solution could be useful for this assessment. For the visualization of markers at specific geographical coordinates in augmented reality, a smartphone has to be aware of its position relative to the world. It is using the sensor data of the camera and inertial measurement unit (IMU) for AR while determining its absolute location and direction with the Global Navigation Satellite System (GNSS) and its magnetic compass. To validate the positional accuracy of AR markers, we investigated the current state of the art and existing solutions. A prototype application has been developed and connected to the DigLT. With this application, it is possible to place markers at geographical coordinates that will show up at the correct location in augmented reality at anyplace in the world. Additionally, a function was implemented that lets the user select a point from the environment in augmented reality, whose geographical coordinates are sent to the DigLT. The accuracy and practicality of the placement of markers were examined using geodetic reference points. As a result, we can conclude that it is possible to mark larger objects like a car or a house, but the accuracy mainly depends on the internal compass, which causes a rotational error that increases with the distance to the target.

An EKF-based multiple data fusion for mobile robot indoor localization

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Guangbing Zhou ◽  
Jing Luo ◽  
Shugong Xu ◽  
Shunqing Zhang ◽  
Shige Meng ◽  
...  
Keyword(s):  
Data Fusion ◽  
Mobile Robot ◽  
Autonomous Navigation ◽  
Indoor Localization ◽  
Robot Navigation ◽  
Sensor Data ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Content Type ◽  
Multiple Data

Purpose Indoor localization is a key tool for robot navigation in indoor environments. Traditionally, robot navigation depends on one sensor to perform autonomous localization. This paper aims to enhance the navigation performance of mobile robots, a multiple data fusion (MDF) method is proposed for indoor environments. Design/methodology/approach Here, multiple sensor data i.e. collected information of inertial measurement unit, odometer and laser radar, are used. Then, an extended Kalman filter (EKF) is used to incorporate these multiple data and the mobile robot can perform autonomous localization according to the proposed EKF-based MDF method in complex indoor environments. Findings The proposed method has experimentally been verified in the different indoor environments, i.e. office, passageway and exhibition hall. Experimental results show that the EKF-based MDF method can achieve the best localization performance and robustness in the process of navigation. Originality/value Indoor localization precision is mostly related to the collected data from multiple sensors. The proposed method can incorporate these collected data reasonably and can guide the mobile robot to perform autonomous navigation (AN) in indoor environments. Therefore, the output of this paper would be used for AN in complex and unknown indoor environments.

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