scholarly journals Identifying Driver Behavior in Preturning Maneuvers Using In-Vehicle CANbus Signals

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
M. Zardosht ◽  
S. S. Beauchemin ◽  
M. A. Bauer
Keyword(s):  
Data Streams ◽  
Driver Behavior ◽  
Driving Behavior ◽  
Steering Wheel ◽  
Vehicle Speed ◽  
Statistical Features ◽  
Instrumented Vehicle ◽  
Using Data ◽  
Near Future ◽  
Driving Period

Our objective in this contribution is to categorize driver behavior in terms of preturning maneuvers. We analyze driving behavior in an urban environment prior to turns using data obtained from the CANbus of an instrumented vehicle during a one-hour driving period for 12 different individuals. CANbus data streams such as vehicle speed, gas pedal pressure, brake pedal pressure, steering wheel angle, and acceleration are collected and analyzed for 5, 10, and 15 seconds of driving prior to each turn. We consider all turns for each driver and extract statistical features from the signals and use cluster analysis to categorize drivers into groups reflecting different driving styles. The results show that using this approach we can effectively cluster drivers into two groups. The results show consistency in the membership within a cluster throughout the different timeframes. We conclude that driver behavior classification from such data streams is possible and we hope in the near future to devise driver descriptors that include additional maneuvers.

scholarly journals Sound Effects on Physiological State and Behavior of Drivers in a Highway Tunnel

2021 ◽  
Vol 12 ◽  
Author(s):  
Yanqun Yang ◽  
Yang Feng ◽  
Said M. Easa ◽  
Xiujing Yang ◽  
Jiang Liu ◽  
...  
Keyword(s):  
Physiological State ◽  
Driving Behavior ◽  
Driving Safety ◽  
Steering Wheel ◽  
Vehicle Speed ◽  
Accelerator Pedal ◽  
Acoustic Environment ◽  
Highway Tunnel ◽  
Significant Difference ◽  
Sound Environment

Driving behavior in a highway tunnel could be affected by external environmental factors like light, traffic flow, and acoustic environments, significantly when these factors suddenly change at the moment before and after entering a tunnel. It will cause tremendous physiological pressure on drivers because of the reduction of information and the narrow environment. The risks in driving behavior will increase, making drivers more vulnerable than driving on the regular highways. This research focuses on the usually neglected acoustic environment and its effect on drivers' physiological state and driving behavior. Based on the SIMLAB driving simulation platform of a highway tunnel, 45 drivers participated in the experiment. Five different sound scenarios were tested: original highway tunnel sound and a mix of it with four other sounds (slow music, fast music, voice prompt, and siren, respectively). The subjects' physiological state and driving behavior data were collected through heart rate variability (HRV) and electroencephalography (EEG). Also, vehicle operational data, including vehicle speed, steering wheel angle, brake pedal depth, and accelerator pedal depth, were collected. The results indicated that different sound scenarios in the highway tunnel showed significant differences in vehicle speed (p = 0.000, η2 = 0.167) and steering wheel angle (p = 0.007, η2 = 0.126). At the same time, they had no significant difference in HRV and EEG indicators. According to the results, slow music was the best kind of sound related to driving comfort, while the siren sound produced the strongest driver reaction in terms of mental alertness and stress level. The voice-prompt sound most likely caused driver fatigue and overload, but it was the most effective sound affecting safety. The subjective opinion of the drivers indicated that the best sound scenario for the overall experience was slow music (63%), followed by fast music (21%), original highway tunnel sound environment (13%), and voice-prompt sound (3%). The findings of this study will be valuable in improving acoustic environment quality and driving safety in highway tunnels.

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.

scholarly journals Validation of Vehicle Driving Simulator from Perspective of Velocity and Trajectory Based Driving Behavior under Curve Conditions

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8429
Author(s):  
Liang Chen ◽  
Jiming Xie ◽  
Simin Wu ◽  
Fengxiang Guo ◽  
Zheng Chen ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Short Term Memory ◽  
Road Traffic ◽  
Lateral Displacement ◽  
Safety Evaluation ◽  
Driving Behavior ◽  
Steering Wheel ◽  
Vehicle Speed ◽  
Driving Simulators ◽  
Convenient Setting

With their advantages of high experimental safety, convenient setting of scenes, and easy extraction of control parameters, driving simulators play an increasingly important role in scientific research, such as in road traffic environment safety evaluation and driving behavior characteristics research. Meanwhile, the demand for the validation of driving simulators is increasing as its applications are promoted. In order to validate a driving simulator in a complex environment, curve road conditions with different radii are considered as experimental evaluation scenarios. To attain this, this paper analyzes the reliability and accuracy of the experimental vehicle speed of a driving simulator. Then, qualitative and quantitative analysis of the lateral deviation of the vehicle trajectory is carried out, applying the cosine similarity method. Furthermore, a data-driven method was adopted which takes the longitudinal displacement, lateral displacement, vehicle speed and steering wheel angle of the vehicle as inputs and the lateral offset as the output. Thus, a curve trajectory planning model, a more comprehensive and human-like operation, is established. Based on directional long short-term memory (Bi–LSTM) and a recurrent neural network (RNN), a multiple Bi–LSTM (Mul–Bi–LSTM) is proposed. The prediction performance of LSTM, MLP model and Mul–Bi–LSTM are compared in detail on the validation set and testing set. The results show that the Mul–Bi–LSTM model can generate a trajectory which is very similar to the driver’s curve driving and have a preferable generalization performance. Therefore, this method can solve problems which cannot be realized in real complex scenes in the simulator validation. Selecting the trajectory as the validation parameter can more comprehensively and intuitively reflect the simulator’s curve driving state. Using a speed model and trajectory model instead of a real car experiment can improve the efficiency of simulator validation and lay a foundation for the standardization of simulator validation.

scholarly journals Lane Departure Warning Estimation Using Yaw Acceleration

2020 ◽  
Vol 11 (1) ◽  
pp. 102-111
Author(s):  
Em Poh Ping ◽  
J. Hossen ◽  
Wong Eng Kiong
Keyword(s):  
Vehicle Dynamics ◽  
Dynamic Responses ◽  
Dynamics Simulation ◽  
Steering Wheel ◽  
Mathematical Representation ◽  
Vehicle Speed ◽  
Lane Departure Warning ◽  
Model Based ◽  
Lane Departure ◽  
Proposed Model

AbstractLane departure collisions have contributed to the traffic accidents that cause millions of injuries and tens of thousands of casualties per year worldwide. Due to vision-based lane departure warning limitation from environmental conditions that affecting system performance, a model-based vehicle dynamics framework is proposed for estimating the lane departure event by using vehicle dynamics responses. The model-based vehicle dynamics framework mainly consists of a mathematical representation of 9-degree of freedom system, which permitted to pitch, roll, and yaw as well as to move in lateral and longitudinal directions with each tire allowed to rotate on its axle axis. The proposed model-based vehicle dynamics framework is created with a ride model, Calspan tire model, handling model, slip angle, and longitudinal slip subsystems. The vehicle speed and steering wheel angle datasets are used as the input in vehicle dynamics simulation for predicting lane departure event. Among the simulated vehicle dynamic responses, the yaw acceleration response is observed to provide earlier insight in predicting the future lane departure event compared to other vehicle dynamics responses. The proposed model-based vehicle dynamics framework had shown the effectiveness in estimating lane departure using steering wheel angle and vehicle speed inputs.

scholarly journals From anomaly detection to rumour detection using data streams of social platforms

2019 ◽  
Vol 12 (9) ◽  
pp. 1016-1029 ◽  
Author(s):  
Nguyen Thanh Tam ◽  
Matthias Weidlich ◽  
Bolong Zheng ◽  
Hongzhi Yin ◽  
Nguyen Quoc Viet Hung ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Data Streams ◽  
Using Data

Benchmarking Fuel Economy of Connected and Automated Vehicles in Real World Driving Conditions via Monte Carlo Simulation

2020 ◽  
Author(s):  
Shreshta Rajakumar Deshpande ◽  
Shobhit Gupta ◽  
Dennis Kibalama ◽  
Nicola Pivaro ◽  
Marcello Canova
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Fuel Economy ◽  
Real World ◽  
Human Subjects ◽  
Driver Model ◽  
Traffic Information ◽  
Vehicle Speed ◽  
Instrumented Vehicle ◽  
The Impact

Abstract Connectivity and automation have accelerated the development of algorithms that use route and real-time traffic information for improving energy efficiency. The evaluation of such benefits, however, requires establishing a reliable baseline that is representative of a real-world driving environment. In this context, virtual driver models are generally adopted to predict the vehicle speed based on route data and presence of lead vehicles, in a way that mimics the response of human drivers. This paper proposes an Enhanced Driver Model (EDM) that forecasts the human response when driving in urban conditions, considering the effects of Signal Phasing and Timing (SPaT) by introducing the concept of Line-of-Sight (LoS). The model was validated against data collected on an instrumented vehicle driven on public roads by different human subjects. Using this model, a Monte Carlo simulation is conducted to determine the statistical distribution of fuel consumption and travel time on a given route, varying driver behavior (aggressiveness), traffic conditions and SPaT. This allows one to quantify the impact of uncertainties associated to real-world driving in fuel economy estimates.

scholarly journals Data-Driven Modelling of Smart Building Ventilation Subsystem

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Grigore Stamatescu ◽  
Iulia Stamatescu ◽  
Nicoleta Arghira ◽  
Ioana Fagarasan
Keyword(s):  
Data Mining ◽  
Data Streams ◽  
Data Driven ◽  
Support Vector ◽  
Commercial Building ◽  
Monitoring And Control ◽  
Smart Building ◽  
Building Ventilation ◽  
Using Data ◽  
Rich Data

Considering the advances in building monitoring and control through networks of interconnected devices, effective handling of the associated rich data streams is becoming an important challenge. In many situations, the application of conventional system identification or approximate grey-box models, partly theoretic and partly data driven, is either unfeasible or unsuitable. The paper discusses and illustrates an application of black-box modelling achieved using data mining techniques with the purpose of smart building ventilation subsystem control. We present the implementation and evaluation of a data mining methodology on collected data from over one year of operation. The case study is carried out on four air handling units of a modern campus building for preliminary decision support for facility managers. The data processing and learning framework is based on two steps: raw data streams are compressed using the Symbolic Aggregate Approximation method, followed by the resulting segments being input into a Support Vector Machine algorithm. The results are useful for deriving the behaviour of each equipment in various modi of operation and can be built upon for fault detection or energy efficiency applications. Challenges related to online operation within a commercial Building Management System are also discussed as the approach shows promise for deployment.

Semi-Active Lateral Interconnected Air Suspension Hierarchical Control

2014 ◽  
Vol 1030-1032 ◽  
pp. 1537-1542 ◽  
Author(s):  
Li Qin Sun ◽  
Zhen Cui
Keyword(s):  
Model Simulation ◽  
Lower Layer ◽  
Hierarchical Control ◽  
Roll Angle ◽  
Steering Wheel ◽  
Vehicle Speed ◽  
Control Rules ◽  
Air Suspension ◽  
Vehicle Dynamic Model ◽  
Sport Mode

Based on semi-active interconnected air suspension system full vehicle dynamic model, Hierarchical control strategy is used to design LIAS control system to improve vehicle riding comfort.The upper layer is mode judgements layer ,and the lower layer is interconnection condition control and damping condition control layer.Interconnection condition control rules are carried out by taking vehicle speed, steering wheel angle and vehicle roll angle as input parameters.On base of the model,simulation results show that vehicle riding comfort increases by 29.2% and 3.37% through using mixed SH-ADD control than that of SH and ADD control individually,and vehicle roll angle reduces effectively under double-lane condition by using sport mode control rules.

Driving Performance under Nighttime Conditions of Visual Degradation

1967 ◽  
Vol 9 (5) ◽  
pp. 427-432 ◽  
Author(s):  
Francisco Matanzo ◽  
Thomas H. Rockwell
Keyword(s):  
Driving Performance ◽  
Task Group ◽  
Vehicle Speed ◽  
Dependent Variables ◽  
Visual Degradation ◽  
Four Levels ◽  
Instrumented Vehicle ◽  
Lane Position ◽  
Nighttime Driving ◽  
Very High

Nighttime driving performance was studied in relation to four different driving tasks and four levels of visual degradation. Four matched but task-differentiated groups of four Ss each drove an instrumented vehicle at night on a superhighway. The four levels of visual degradation presented the roadway to the driver at overall luminance levels of 5.228 mL, 2.688 mL, 0.755 mL, and 0.168 mL. The two dependent variables were vehicle speed and vehicle distance from the white shoulder line. The visual degradation caused the Ss to slow down and position the vehicle slightly farther away from the shoulder. It was found that a driver also is capable of driving at a constant speed and of maintaining a constant lane position at very high degrees of visual degradation. These results were explained by the different instructions given to each task group.

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