Differentiating Alcohol-Induced Driving Behavior Using Steering Wheel Signals

2012 ◽  
Vol 13 (3) ◽  
pp. 1355-1368 ◽  
Author(s):  
Devashish Das ◽  
Shiyu Zhou ◽  
John D. Lee
Keyword(s):  
Driving Behavior ◽  
Steering Wheel

16-Bit A/D CAN Interface Steering Wheel Angle and Torque Acquisition System

2013 ◽  
Vol 846-847 ◽  
pp. 144-147
Author(s):  
Yun Hu Zhang ◽  
Chao Zhang
Keyword(s):  
Data Collection ◽  
High Precision ◽  
Can Bus ◽  
Driving Behavior ◽  
Software Implementation ◽  
Host Computer ◽  
Steering Wheel ◽  
Torque Sensor ◽  
Vehicle Handling ◽  
Torque Values

In this paper, a steering wheel angle and torque acquisition program consisted of a steering wheel angle and torque sensor, 16-bit Σ-A/D converter, SCM and CAN bus chip is described. It describes the A / D conversion, angle and torque values calculation , CAN Communication and host computer acquisition software implementation and other important processes for the method. On this basis, the steering wheel angle and torque data collection realization is finished,and it has operated in the actual vehicle environments. The results show that the system is of high precision, good stability, and it is suitable for driving behavior tests and vehicle handling and stability test.

Looking at Mind Wandering During Driving Through the Windows of PCA and t-SNE

2018 ◽  
Vol 62 (1) ◽  
pp. 1863-1867 ◽  
Author(s):  
Areen Alsaid ◽  
John D. Lee ◽  
Daniel M. Roberts ◽  
Daniela Barrigan ◽  
Carryl L. Baldwin
Keyword(s):  
Performance Measures ◽  
Principal Component ◽  
Mind Wandering ◽  
Lateral Position ◽  
Driving Behavior ◽  
Driving Safety ◽  
Steering Wheel ◽  
Attentional State ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques

Mind wandering is a poorly understood phenomenon that can undermine driving safety. Driving performance measures have been found to be associated with mind wandering (e.g., steering wheel movements, standard deviation of lateral position, and speed variation). However, no one measure can fully describe the driver behavior associated with mind wandering. Therefore, in this paper we explore the effect of mind wandering on nine steering measures with data collected from a study that included nine drivers over two sessions of driving over five days. Participants were periodically probed to report their attentional state–whether they were mind wandering or focusing on the task. We used two dimensionality-reduction techniques—Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE)—to visualize the dimensions underlying the nine measures. Comparing PCA to t-SNE highlights the benefits of t-SNE in revealing the fine structure that differentiates driving behavior. These visualizations show that a) driver engagement increased during roadway curve segments, and b) mind wandering manifests itself through several types of steering behavior.

Driver Behavior Comparison Between Static and Dynamic Simulation for Advanced Driving Maneuvers

2011 ◽  
Vol 20 (2) ◽  
pp. 143-161 ◽  
Author(s):  
B. J. Correia Grácio ◽  
M. Wentink ◽  
A. R. Valente Pais
Keyword(s):  
Paired Comparison ◽  
Driving Behavior ◽  
Driving Simulation ◽  
Lateral Acceleration ◽  
Steering Wheel ◽  
Control Input ◽  
Inertial Motion ◽  
Average Speed ◽  
Motion Condition ◽  
Fixed Base

In advanced driving maneuvers, such as a slalom maneuver, it is assumed that drivers use all the available cues to optimize their driving performance. For example, in curve driving, drivers use lateral acceleration to adjust car velocity. The same result can be found in driving simulation. However, for comparable curves, drivers drove faster in fixed-base simulators than when actually driving a car. This difference in driving behavior decreases with the use of inertial motion feedback in simulators. The literature suggests that the beneficial effect of inertial cues in driving behavior increases with the difficulty of the maneuver. Therefore, for an extreme maneuver such as a fast slalom, a change in driving behavior is expected when a fixed-base condition is compared to a condition with inertial motion. It is hypothesized that driving behavior in a simulator changes when motion cues are present in extreme maneuvers. To test the hypothesis, a comparison between No-Motion and Motion car driving simulation was done, by measuring driving behavior in a fast slalom. A within-subjects design was used, with 20 subjects driving the fast slalom in both conditions. The average speed during the Motion condition was significantly lower than the average speed during the No-Motion condition. The same was found for the peak lateral acceleration generated by the car model. A power spectral density analysis performed on the steering wheel angle signal showed different control input behavior between the two experimental conditions. In addition, the results from a paired comparison showed that subjects preferred driving with motion feedback. From the lower driving speed and different control input on the steering wheel, we concluded that motion feedback led to a significant change in driving behavior.

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.

Driving behavior classification river sensing based on vehicle steering wheel operations

2008 ◽  
Author(s):  
Takashi Imamura ◽  
Hagito Yamashita ◽  
MD Rizal bin Othman ◽  
Zhong Zhang ◽  
Tetsuo Miyake
Keyword(s):  
Driving Behavior ◽  
Steering Wheel

1A1-I15 Detection of Deviant Driving Behavior based on Steering Wheel Operations

2008 ◽  
Vol 2008 (0) ◽  
pp. _1A1-I15_1-_1A1-I15_2
Author(s):  
Hagito YAMASHITA ◽  
Takashi IMAMURA ◽  
Mohd Rizal Bin OTHMAN ◽  
Zhong ZHANG ◽  
Tetuo MIYAKE
Keyword(s):  
Driving Behavior ◽  
Steering Wheel ◽  
Behavior Based

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 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 Promoting eco-driving behavior through multisensory stimulation: a preliminary study on the use of visual and haptic feedback in a virtual reality driving simulator

2021 ◽  
Author(s):  
Andrea Pietra ◽  
Marina Vazquez Rull ◽  
Roberta Etzi ◽  
Alberto Gallace ◽  
Giulia Wally Scurati ◽  
...  
Keyword(s):  
Virtual Reality ◽  
High Speed ◽  
Haptic Feedback ◽  
Driving Simulator ◽  
Visual Stimulation ◽  
Physiological State ◽  
Driving Behavior ◽  
Steering Wheel ◽  
Mean Power ◽  
Accelerator Pedal

AbstractThis paper describes the design and preliminary test of a virtual reality driving simulator capable of conveying haptic and visual messages to promote eco-sustainable driving behavior. The driving simulator was implemented through the Unity game engine; a large street environment, including high-speed and urban sections, was created to examine different driving behaviors. The hardware setup included a gaming driving seat, equipped with a steering wheel and pedals; the virtual scenarios were displayed through an Oculus Rift headset to guarantee an immersive experience. Haptic stimulation (i.e., vibrations) was delivered to the driver through the accelerator pedal, while visual stimuli (i.e., icons and colors) were shown on a virtual head-up display. The sensory feedbacks were presented both alone and in combination, providing information about excessive acceleration and speed. Four different virtual scenarios, each one including a distracting element (i.e., navigator, rain, call, and traffic), were also created. Ten participants tested the simulator. Fuel consumption was evaluated by calculating a mean power index (MPI) in reference to the sensory feedback presentation; physiological reactions and responses to a usability survey were also collected. The results revealed that the haptic and visuo-haptic feedback were responsible for an MPI reduction, respectively, for 14% and 11% compared with a condition of no feedback presentation; while visual feedback alone resulted in an MPI increase of 11%. The efficacy of haptic feedback was also accompanied by a more relaxing physiological state of the users, compared with the visual stimulation. The system’s usability was adequate, although haptic stimuli were rated slightly more intrusive than the visual ones. Overall, these preliminary results highlight how promising the use of the haptic channel can be in communicating and guiding the driver toward a more eco-sustainable behavior.

scholarly journals Glaucomatous visual fields and neurocognitive function are independently associated with poor lane maintenance during driving simulation

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
David E. Anderson ◽  
John P. Bader ◽  
Emily A. Boes ◽  
Meghal Gagrani ◽  
Lynette M. Smith ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Vehicle Control ◽  
Driving Behavior ◽  
Driving Performance ◽  
Driving Simulation ◽  
Sensor Data ◽  
Lateral Acceleration ◽  
Steering Wheel ◽  
Neurocognitive Performance ◽  
Loss Severity

Abstract Background Driving simulators are a safe alternative to on-road vehicles for studying driving behavior in glaucoma drivers. Visual field (VF) loss severity is associated with higher driving simulator crash risk, though mechanisms explaining this relationship remain unknown. Furthermore, associations between driving behavior and neurocognitive performance in glaucoma are unexplored. Here, we evaluated the hypothesis that VF loss severity and neurocognitive performance interact to influence simulated vehicle control in glaucoma drivers. Methods Glaucoma patients (n = 25) and suspects (n = 18) were recruited into the study. All had > 20/40 corrected visual acuity in each eye and were experienced field takers with at least three stable (reliability > 20%) fields over the last 2 years. Diagnosis of neurological disorder or cognitive impairment were exclusion criteria. Binocular VFs were derived from monocular Humphrey VFs to estimate a binocular VF index (OU-VFI). Montreal Cognitive Assessment (MoCA) was administered to assess global and sub-domain neurocognitive performance. National Eye Institute Visual Function Questionnaire (NEI-VFQ) was administered to assess peripheral vision and driving difficulties sub-scores. Driving performance was evaluated using a driving simulator with a 290° panoramic field of view constructed around a full-sized automotive cab. Vehicle control metrics, such as lateral acceleration variability and steering wheel variability, were calculated from vehicle sensor data while patients drove on a straight two-lane rural road. Linear mixed models were constructed to evaluate associations between driving performance and clinical characteristics. Results Patients were 9.5 years older than suspects (p = 0.015). OU-VFI in the glaucoma group ranged from 24 to 98% (85.6 ± 18.3; M ± SD). OU-VFI (p = .0066) was associated with MoCA total (p = .0066) and visuo-spatial and executive function sub-domain scores (p = .012). During driving simulation, patients showed greater steering wheel variability (p = 0.0001) and lateral acceleration variability (p < .0001) relative to suspects. Greater steering wheel variability was independently associated with OU-VFI (p = .0069), MoCA total scores (p = 0.028), and VFQ driving sub-scores (p = 0.0087), but not age (p = 0.61). Conclusions Poor vehicle control was independently associated with greater VF loss and worse neurocognitive performance, suggesting both factors contribute to information processing models of driving performance in glaucoma. Future research must demonstrate the external validity of current findings to on-road performance in glaucoma.

Sign in / Sign up

Export Citation Format

Share Document