Throughout the world in countries where is a substantial volume of vehicular traffic, the incidence of road collisions, and the resulting deaths, injuries and property damage, is regarded as a significant social problem. In many instances, the traffic accidents often occur from late at night to early morning, and especially when the driver is distracted. Driving at night is one of the most hazardous situations commonly faced by the driver. It is now well established that the rate of fatal traffic accidents is 3 to 4 times higher at night than at daytime . It is established that under nighttime conditions many visual abilities such as spatial resolution, contrast discrimination, stereoscopic depth perception, accommodation, response and reaction time are degraded [1]. So that nighttime driving is a serious issue for more investigations, especially for the appearance of unexpected obstacles. This critical issue motivates us to discover what reactions happened in the human brain when a driver encountered the unexpected obstacle. Driver reactions to the sudden incidental appearance of an object such as a child rushing out may differ depending on the driver’s attention to the peripheral scene. A more complete understanding of the attentive mechanisms of the brain will improve driver responses and increase driving safety.
For mental workload studies, when assessing workload in driving, as well as task demand, is an important loading factor. For example, the workload of freeway driving when operating a high-speed car should be assessed in terms of information processing load and time on nighttime driving. It must be noted that the consequences of task performance over a period, such as fatigue increment and vigilance decrement, has a complex relationship with the effects of task demand, when the demand is mainly mental.
There are many studies in 1960s and 1970s dealing with the effects of time-on-task on
vigilance or sustained attention [2]. Some of these studies manipulated task demand such as event rate and described the change in performance over time-on-task [3].
In recent mental workload studies, on the other hand, the effects of time-on-task have been neglected. Jex defined mental workload as “the operator’s evaluation of the attentional load margin”[4]. Eggemeier defined it as “the degree of process capacity that is expanded during task performance”[5]; and Wickens wrote that “the concept of workload is fundamentally defined by this relationship between resource supply and task demand”[6].
There are several measures and assessment techniques that are said to be sensitive to mental workload. Among them are heart rate variability (HRV), event-related potentials (ERP), dual-task methods, and the two major rating scales known as the SWAT (the Subjective Workload Assessment Technique) and the NASA-TLX(Task Load Index). We take ERPs as an important measure for our investigation.
K.Eba et al. introduced a real driving experiment to observe the brain activities related to driving situation [7]. In a car driving task with and without an unexpected dummy doll rushing out, they recorded the homodynamic activities of the frontal lobe by near infrared spectroscopy (NIRS). As a result, they concluded that the right rostromedial prefrontal cortex plays an important role in spatial attentive recognition of driving scene. By the improvement of driving simulation technology, we can use the driving simulation to save the time and costs.
The use of driving simulation for vehicle design and driver perception studies is expanding rapidly. This is largely because how applicable driving simulation is to the real world is unclear, however analyses of perceptual criteria carried out in driving simulation experiments are controversial. Keneny and Panerai [8] suggested that, in driving simulators with a large field of view, longitudinal speed can be estimated correctly from visual information. On the other hand, recent psychophysical studies have revealed an unexpectedly important contribution of vestibular cues in distance perception and steering, prompting a re-evaluation of the role of visuo–vestibular interaction in driving simulation studies.
For the event related subjects, some specific features of EEG are expected to occur in the brain activities respecting to different situations. Moreover, the Event-Related Potential [9-11]
analysis has widely used for the EEG data processing. The interested target is called a single event within the experiments, thus the brain activities related to the event were extracted for further analysis. The key problem to perform such a work is the inability to dynamically quantify cognitive changes in the human capacity. A way to determine the relationship between different stimuli and human cognitive responses accompanying correct, incorrect and absent motor responses is the use of event-related brain potential (ERP) signals. Moreover, we concern about the unexpected obstacle dodging task related to the ERP response.
There are some similar studies about incongruent cognitive state. In these studies, they proposed an incongruent situation to induce negative brain activities by visual or auditory stimulus [11]. The broad negative wave peaks in the surface EEG around 400 ms after a semantically incongruous word in a meaningful sentence [12, 13]. And there are also many studies proposed that the waveform can be elicited in response to semantic processing of non-verbal stories [14].
In our study, we want to investigate the EEG dynamics related to the unexpected obstacle dodging task. With combining the technology of virtual reality (VR), a realistic stimuli environment is provided to subjects in our research. A surprising task is provided to the subjects with a broken-down car appears in the middle of the road. The subjects are requested to dodge the broken-off car as soon as possible and dodge collision in the experiments. One of the main purpose of our research is to investigate EEG changes relate to surprising status by anglicizing the subjects’ EEG features corresponding to the With-Cue task and the Without-Cue task. Another is the classification of different driving style in unexpected obstacle dodging tasks.
This thesis is organized as follow. In Chapter 2, the experimental design is introduced in first section, and the following section is about the experimental setup of hardware and software. In section three, the subjects and data acquisition are introduced here. In Chapter 3, we explore the analysis procedure by applying ICA, power spectrum analysis, and correlation coefficient. The experimental results of EEG signals are described in Chapter 4. Detailed
discussions of our experimental results are given in Chapter 5. Finally, the conclusions are summarized in Chapter 6.