Driver Mental Workload Evaluation And Application In Driver Assistance System

Traffic safety has always been the most serious problem in current society, statisticsshow that China has the highest number of traffic fatalities in the world. Therefore, depthanalysis of the driver, vehicle and traffic condition is needed in order to find appropriatesolutions to reduce the frequency of traffic accidents and casualties.Driver is responsible for the perception of traffic information and vehicle status, playingthe most important role in the closed-loop of driver-road-vehicle system. A driver needs toconcentrate during driving and analyze the state of vehicle dynamics and traffic conditions,then making a proper driving strategies and maneuver. Therefore, a driver does not only needto afford some physical workload but also mental workload. When the load is too high or toolow, it may lead the driver making wrong decisions or driving maneuver, resulting in trafficaccidents.Changes of the driving task makes the problem above particularly prominent. Driversoften undertake dual-task or multi-task operating conditions because of the growing trafficdensity, complexity of road condition and usage of vehicle information system, theirattention may be detached from the current driving condition that directly related to thedriving task. However, the brain’s ability to process information has certain limitations.When in a complex condition, unrelated tasks are bound to compete for the brain resourcethat should be used in driving task, so that driver’s mental workload increased in thiscondition. When the mental workload reaches a certain level, it makes the drivers feelpressure and tension, leading to errors in the perception and decision-making.In order to solve the problems above, this paper analyzed and summarized the researcharticles in the field of driver behavior home and abroad, and based on the knowledge andresearch methods of experimental psychology and psychophysiology, evaluation method ofdriver mental workload under dual-task condition using psychological and physiologicalparameters is established. The methods and results in the research of driver mentalworkload was then used in driver assistance systems to solve the key issues, providing atheoretical basis and technical support for the development of driver assistance system.Specific contents of this paper are as follows:1. Research on dynamic changes of driver physiological signal under multi-taskconditions. The purpose of this part of research is to explore and verify the relationshipbetween driver mental workload and physiological information, establishing an evaluation method of mental workload based on typical physiological indexes.Dual-task paradigm in experimental psychology is introduced into our experiment. Adual-task condition is carried out in driving simulator containing driving task and voiceinteraction task. ECG, skin conductance, respiration and the steering wheel angle is recordedacross the entire experimental trial. In the data analysis stage, three kinds of physiologicalindexes including heart rate variability, skin conductance level and respiratory rate areanalyzed. A combined measure of driver mental workload is then created using multipleregression analysis and physiological indexes above. Finally, steering entropy method isused in the analysis of steering wheel angle under dual-task condition.2. Driver mental workload evaluation based on physiological parameters and supportvector machine. In this part, a driver mental workload recognition model is established basedon kernel principal component analysis and support vector machine.A field study is carriedout aiming at the events in actual driving conditions such as receiving calls, sendingmessages and playing music. Two types of physiological signals including EEG and skinconductance are recorded. Independent component method is used as the filter of EEG, andmulti-dimensional wavelet packet decomposition technique is used to extract thetime-frequency domain parameters. Kernel principal component analysis method is used toreduce the dimension of input data. At last, support vector machine is selected as the mentalworkload evaluation model after the comparison of model performance among Logisticregression, BP neural network and support vector machine.3. Application of driver psychological and physiological characteristics in the driverassistance system. The research purpose of this part is the application of driver psychologicaland physiological characteristics in design of driver assistance system. In the developmentprocess of lane departure warning system, driving intent is recognized mainly through thechanges of steering wheel angle, position of the vehicle, turning signal and gaze direction.When unintentionally lane departure is recognized through these parameters, a warningsignal is released to the driver. Lane change experiment is designed and physiological andvehicle status information are collected. Typical physiological indexes is analyzed and usedin the recognition model of lane change intention. Finally, a recognition model with higheraccuracy is derived compared with the model using vehicle status information only.Research method of psychophysiology and experimental psychology is used in thispaper, a mental workload evaluation method under dual-task condition based onphysiological indexes is created. Research method of driver’s psychological andphysiological characteristics is then used in the key issues in driver assistance systems.