The effect of electronic word of mouth communication on purchase intention moderate by trust: a case online consumer of Bahawalpur Pakistan

The aim of this study is concerned with improving the previous research finding complete filling the research gaps and introducing the e-WOM on purchase intention and brand trust as a moderator between the e-WOM, and purchase intention an online user in Bahawalpur city Pakistan, therefore this study was a focus at linking the research gap of previous literature of past study based on individual awareness from the real-life experience. we collected data from the online user of the Bahawalpur Pakistan. In this study convenience sampling has been used to collect data and instruments of this study adopted from the previous study. The quantitative research methodology used to collect data, survey method was used to assemble data for this study, 300 questionnaire were distributed in Bahawalpur City due to the ease, reliability, and simplicity, effective recovery rate of 67% as a result 202 valid response was obtained for the effect of e-WOM on purchase intention and moderator analysis has been performed. Hypotheses of this research are analyzed by using Structural Equation Modeling (SEM) based on Partial Least Square (PLS). The result of this research is e-WOM significantly positive effect on purchase intention and moderator role of trust significantly affects the relationship between e-WOM, and purchase intention. The addition of brand trust in the model has contributed to the explanatory power, some studied was conduct on brand trust as a moderator and this study has contributed to the literature in this favor. significantly this study focused on current marketing research. Unlike past studies focused on western context, this study has extended the regional literature on e-WOM, and purchase intention to be intergrading in Bahawalpur Pakistan context. Lastly, future studies are recommended to examine the effect of trust in other countries allow for the comparison of the findings

Real-time drowsiness detection using wearable, lightweight EEG sensors

Driver drowsiness has always been a major concern for researchers and road use administrators. It has led to countless deaths accounting to significant percentile of deaths world over. Researchers have attempted to determine driver drowsiness using the following measures: (1) subjective measures (2) vehicle-based measures; (3) behavioral measures and (4) physiological measures.;Studies carried out to assess the efficacy of all the four measures, have brought out significant weaknesses in each of these measures. However detailed and comprehensive review has indicated that Physiological Measure namely EEG signal analysis provides most reliable and accurate information on driver drowsiness. In this paper a brief review of systems, and issues associated with them has been discussed with a view to evolve a novel system based on EEG signals especially for use in mine vehicles.;The feasibility of real-time drowsiness detection using commercially available, off-the-shelf, lightweight, wearable EEG sensors is explored. While EEG signals are known to be reliable indicators of fatigue and drowsiness, they have not been used widely due to their size and form factor. But the use of light-weight wearable EEGs alleviates this concern. Spectral analysis of EEG signals from these sensors using support vector machines is shown to classify drowsy states with high accuracy.;The system is validated using data collected on 23 subjects in fresh and drowsy states. The EEG signals are also used to characterize the blink duration and frequency of subjects. However, classification of drowsy states using blink analysis is shown to have lower accuracy than that using spectral analysis

Multimodal Features for Detection of Driver Stress and Fatigue: Review

Driver fatigue and stress significantly contribute to higher number of car accidents worldwide. Although, different detection approaches have been already commercialized and used by car producers (and third party companies), research activities in this field are still needed in order to increase the reliability of these alert systems. Also, in the context of automated driving, the driver mental state assessment will be an important part of cars in future. This paper presents state-of-the-art review of different approaches for driver fatigue and stress detection and evaluation. We describe in details various signals (biological, car and video) and derived features used for these tasks and we discuss their relevance and advantages. In order to make this review complete, we also describe different datasets, acquisition systems and experiment scenarios

Novel technologies for the detection and mitigation of drowsy driving

In the human control of motor vehicles, there are situations regularly encountered wherein the vehicle operator becomes drowsy and fatigued due to the influence of long work days, long driving hours, or low amounts of sleep. Although various methods are currently proposed to detect drowsiness in the operator, they are either obtrusive, expensive, or otherwise impractical. The method of drowsy driving detection through the collection of Steering Wheel Movement (SWM) signals has become an important measure as it lends itself to accurate, effective, and cost-effective drowsiness detection. In this dissertation, novel technologies for drowsiness detection using Inertial Measurement Units (IMUs) are investigated and described. IMUs are an umbrella group of kinetic sensors (including accelerometers and gyroscopes) which transduce physical motions into data. Driving performances were recorded using IMUs as the primary sensors, and the resulting data were used by artificial intelligence algorithms, specifically Support Vector Machines (SVMs) to determine whether or not the individual was still fit to operate a motor vehicle. Results demonstrated high accuracy of the method in classifying drowsiness. It was also shown that the use of a smartphone-based approach to IMU monitoring of drowsiness will result in the initiation of feedback mechanisms upon a positive detection of drowsiness. These feedback mechanisms are intended to notify the driver of their drowsy state, and to dissuade further driving which could lead to crashes and/or fatalities. The novel methods not only demonstrated the ability to qualitatively determine a drivers drowsy state, but they were also low-cost, easy to implement, and unobtrusive to drivers. The efficacy, ease of use, and ease of access to these methods could potentially eliminate many barriers to the implementation of the technologies. Ultimately, it is hoped that these findings will help enhance traveler safety and prevent deaths and injuries to users

The Effects of Body Posture on Vigilance Performance

Drivers of vehicles that use a driving automation system were tasked with supervising the vehicle to ensure it was functioning properly. This task required drivers to stay vigilant of the roadway while being ready to intervene in the case of an unexpected hazard that the driving automation system may not have detected. This study investigated whether reclining a drivers’ seatback to more comfortable postures would affect their vigilance performance over time. Vigilance performance was measured by correct detections, false alarms, response sensitivity, response bias, and response time to hazardous events. Forty-five participants were recruited and randomly assigned to a postural condition with a seatback that was upright, slightly reclined, or very reclined. Their performance and comfort were measured over the course of a 40-minute driving task that used SAE Level-2 automation. Participants were tasked with classifying whether the neighboring vehicles were hazardous or safe. Based on our performance measures, we found a vigilance decrement that was potentially caused by cognitive underload stemming from the low task demand. We also found that posture did not affect any of the performance measures and that comfort ratings were similar despite the postural manipulation. This result indicates that drivers of vehicles with a driving automation system are free to adjust their seatback from an upright to very reclined posture without concern for their vigilance performance

A driver-vehicle model for impaired motorists and strategies for planning autonomous vehicles

Vehicle drivers play an important role in transportation safety and vehicle design. Understanding the driver’s behavior, especially the impaired driver’s behavior, is crucial to improve the vehicle safety. The proposed impaired driver model is based on the optimal preview control and the linear quadratic regulator. Two important parameters that could be counted for in a mathematical model of the driver are the reaction time and the preview time. For the impaired driver model, the value of reaction time is increased while the value of preview time is decreased. The simulation results for the model of the impaired driver and the vehicle produce a larger lateral deviation than the one of a normal driver, as revealed in the experiments conducted by the previous studies. The investigation on vehicle parameters reveals that the changes of parameters may improve the overall performance of the impaired driver-vehicle system. The controller for autonomous vehicles developed from the studies of the driver model may eliminate the negative effect of impaired drivers. The preview capability of driver is introduced to the design of the controller by using the preview control theory. The preview information of the path in terms of the lateral position and the velocity profile enhances the performance of the autonomous vehicle. The neural network is presented as a feasible alternative approach to implement the future path in design of autonomous vehicle controller. The neural network weights the path data and provides the adjustment as the preparation to the vehicle

DECISION-MAKING PROCESSES, DRIVING PERFORMANCE, AND ACUTE RESPONSES TO ALCOHOL IN DUI OFFENDERS

Alcohol-impaired driving is a major cause of motor vehicle accident and death in the United States. People who are arrested for DUI (Driving under the Influence) are at high risk to reoffend; approximately one in three of these individuals will commit another DUI offense in the three years following their first conviction (Nochajski & Stasiewicz, 2006). This high risk for recidivism in these individuals suggests that cognitive characteristics may contribute to a pattern of pathological decision making leading to impaired driving. Indeed, individuals with a history of DUI report higher rates of impulsiveness and behavioral dysregulation compared to their nonoffending peers. Relatively little research, however, has used laboratory methods to identify the specific behavioral characteristics, such as poor inhibitory control or heightened sensitivity to immediate reward, which may differentiate DUI offenders from nonoffenders. Further, little is known about how individuals with a history of DUI respond following an acute dose of alcohol. Study 1 examined impulsivity in 20 adults with a recent DUI conviction and 20 adults with no history of DUI using self-report and behavioral measures of impulsivity. This study also used a novel decision-making paradigm to examine how different levels of risk and reward influenced the decision to drive after drinking in both groups. Results of this study found that DUI offenders did not differ from controls in their performance on behavioral measures of impulsivity. They did, however, report higher levels of impulsivity and demonstrated a greater willingness to tolerate higher levels of risk for more modest rewards. Study 2 examined the acute effects of alcohol and expectancy manipulation on driving performance and decision making in the same group of participants. Neither alcohol nor expectancy manipulation exerted a systematic effect on decision making in either group. Alcohol impaired driving performance equally in both groups, but the DUI group perceived themselves as less impaired by alcohol. Expectancy manipulation eliminated this group difference in perceived driving ability. Taken together, these findings identify processes that risk of impaired driving in DUI offenders. They may perceive themselves as less impaired by alcohol, leading to risky decision making when drinking. Expectancy manipulation may be a viable method of reducing risky decision making in DUI offenders

Autonomous Control and Automotive Simulator Based Driver Training Methodologies for Vehicle Run-Off-Road and Recovery Events

Traffic fatalities and injuries continue to demand the attention of researchers and governments across the world as they remain significant factors in public health and safety. Enhanced legislature along with vehicle and roadway technology has helped to reduce the impact of traffic crashes in many scenarios. However, one specifically troublesome area of traffic safety, which persists, is run-off-road (ROR) where a vehicle\u27s wheels leave the paved portion of the roadway and begin traveling on the shoulder or side of the road. Large percentages of fatal and injury traffic crashes are attributable to ROR. One of the most critical reasons why ROR scenarios quickly evolve into serious crashes is poor driver performance. Drivers are unprepared to safely handle the situation and often execute dangerous maneuvers, such as overcorrection or sudden braking, which can lead to devastating results. Currently implemented ROR countermeasures such as roadway infrastructure modifications and vehicle safety systems have helped to mitigate some ROR events but remain limited in their approach. A complete solution must directly address the primary factor contributing to ROR crashes which is driver performance errors. Four vehicle safety control systems, based on sliding control, linear quadratic, state flow, and classical theories, were developed to autonomously recover a vehicle from ROR without driver intervention. The vehicle response was simulated for each controller under a variety of common road departure and return scenarios. The results showed that the linear quadratic and sliding control methodologies outperformed the other controllers in terms of overall stability. However, the linear quadratic controller was the only design to safely recover the vehicle in all of the simulation conditions examined. On average, it performed the recovery almost 50 percent faster and with 40 percent less lateral error than the sliding controller at the expense of higher yaw rates. The performance of the linear quadratic and sliding algorithms was investigated further to include more complex vehicle modeling, state estimation techniques, and sensor measurement noise. The two controllers were simulated amongst a variety of ROR conditions where typical driver performance was inadequate to safely operate the vehicle. The sliding controller recovered the fastest within the nominal conditions but exhibited large variability in performance amongst the more extreme ROR scenarios. Despite some small sacrifice in lateral error and yaw rate, the linear quadratic controller demonstrated a higher level of consistency and stability amongst the various conditions examined. Overall, the linear quadratic controller recovered the vehicle 25 percent faster than the sliding controller while using 70 percent less steering, which combined with its robust performance, indicates its high potential as an autonomous ROR countermeasure. The present status of autonomous vehicle control research for ROR remains premature for commercial implementation; in the meantime, another countermeasure which directly addresses driver performance is driver education and training. An automotive simulator based ROR training program was developed to instruct drivers on how to perform a safe and effective recovery from ROR. A pilot study, involving seventeen human subject participants, was conducted to evaluate the effectiveness of the training program and whether the participants\u27 ROR recovery skills increased following the training. Based on specific evaluation criteria and a developed scoring system, it was shown that drivers did learn from the training program and were able to better utilize proper recovery methods. The pilot study also revealed that drivers improved their recovery scores by an average of 78 percent. Building on the success observed in the pilot study, a second human subject study was used to validate the simulator as an effective tool for replicating the ROR experience with the additional benefit of receiving insight into driver reactions to ROR. Analysis of variance results of subjective questionnaire data and objective performance evaluation parameters showed strong correlations to ROR crash data and previous ROR study conclusions. In particular, higher vehicle velocities, curved roads, and higher friction coefficient differences between the road and shoulder all negatively impacted drivers\u27 recoveries from ROR. The only non-significant impact found was that of the roadway edge, indicating a possible limitation of the simulator system with respect to that particular environment variable. The validation study provides a foundation for further evaluation and development of a simulator based ROR recovery training program to help equip drivers with the skills to safely recognize and recover from this dangerous and often deadly scenario. Finally, building on the findings of the pilot study and validation study, a total of 75 individuals participated in a pre-post experiment to examine the effect of a training video on improving driver performance during a set of simulated ROR scenarios (e.g., on a high speed highway, a horizontal curve, and a residential rural road). In each scenario, the vehicle was unexpectedly forced into an ROR scenario for which the drivers were instructed to recover as safely as possible. The treatment group then watched a custom ROR training video while the control group viewed a placebo video. The participants then drove the same simulated ROR scenarios. The results suggest that the training video had a significant positive effect on drivers\u27 steering response on all three roadway conditions as well as improvements in vehicle stability, subjectively rated demand on the driver, and self-evaluated performance in the highway scenario. Under the highway conditions, 84 percent of the treatment group and 52 percent of the control group recovered from the ROR events. In total, the treatment group recovered from the ROR events 58 percent of the time while the control group recovered 45 percent of the time. The results of this study suggest that even a short video about recovering from ROR events can significantly influence a driver\u27s ability to recover. It is possible that additional training may have further benefits in recovering from ROR events

Driving Manoeuvre Recognition using Mobile Sensors

Automobiles are integral in today's society as they are used for transportation, commerce, and public services. The ubiquity of automotive transportation creates a demand for active safety technologies for the consumer. Recently, the widespread use and improved sensing and computing capabilities of mobile platforms have enabled the development of systems that can measure, detect, and analyze driver behaviour. Most systems performing driver behaviour analysis depend on recognizing driver manoeuvres. Improved accuracy in manoeuvre detection has the potential to improve driving safety, through applications such as monitoring for insurance, the detection of aggressive, distracted or fatigued driving, and for new driver training. This thesis develops algorithms for estimating vehicle kinematics and recognizing driver manoeuvres using a smartphone device. A kinematic model of the car is first introduced to express the vehicle's position and orientation. An Extended Kalman Filter (EKF) is developed to estimate the vehicle's positions, velocities, and accelerations using mobile measurements from inertial measurement units and the Global Positioning System (GPS). The approach is tested in simulation and validated on trip data using an On-board Diagnostic (OBD) device as the ground truth. The 2D state estimator is demonstrated to be an effective filter for measurement noise. Manoeuvre recognition is then formulated as a time-series classification problem. To account for an arbitrary orientation of the mobile device with respect to the vehicle, a novel method is proposed to estimate the phone's rotation matrix relative to the car using PCA on the gyroscope signal. Experimental results demonstrate that e Principal Component (PC) corresponds to a frame axis in the vehicle reference frame, so that the PCA projection matrix can be used to align the mobile device measurement data to the vehicle frame. A major impediment to classifier-manoeuvre recognition is the need for training data, specifically collecting enough data and generating an accurate ground truth. To address this problem, a novel training process is proposed to train the classifier using only simulation data. Training on simulation data bypasses these two issues as data can be cheaply generated and the ground truth is known. In this thesis, a driving simulator is developed using a Markov Decision Process (MDP) to generate simulated data for classifier training. Following training data generation, feature selection is performed using simple features such as velocity and angular velocity. A manoeuvre segmentation classifier is trained using multi-class SVMs. Validation was performed using data collected from driving sessions. A grid search was employed for parameter tuning. The classifier was found to have a 0.8158 average precision rate and a 0.8279 average recall rate across all manoeuvres resulting in an average F1 score of 0.8194 on the dataset