Detecting Driver Drowsiness Based on Sensors: A Review <s> Comparison of Simulated and Real Driving Conditions <s> This work investigates differences in lateral displacement when driving on curved and straight road sections in real-road and simulator conditions. We observed 100 licensed drivers on a rural road and 100 in a fixed-base simulator. Speed and lateral position on the real road were measured using videocameras. The analysis indicates that the mean vehicle lateral displacement is in general higher on the real road than in the simulator. However, these differences decrease for higher speeds at curved sections and for lower speeds at straight sections. It was also found that the standard deviation of the vehicle lateral displacement is significantly lower on the real road than the corresponding values in the simulator, at either curved or straight sections. Actual or potential applications of this research are related to a more realistic assessment of driving behavior scenarios derived on the basis of simulation experiment results. <s> BIB001 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Comparison of Simulated and Real Driving Conditions <s> We ran a randomized cross-over design study under sleep-deprived and non-sleep-deprived driving conditions to test the effects of sleep restriction on real driving performance. The study was performed in a sleep laboratory and on an open French highway. Twenty-two healthy male subjects (age = 21.5 +/- 2 years; distance driven per year = 12,225 +/- 4739 km (7641 +/- 2962 miles) [mean +/- S.D.]) drove 1000 km (625 miles) over 10 h during five 105 min sessions on an open highway. Self-rated fatigue and sleepiness before each session, number of inappropriate line crossings from video recordings and simple reaction time (RT) were measured. Total crossings increased after sleep restriction (535 crossings in the sleep-restricted condition versus 66 after non-restricted sleep (incidence rate ratio (IRR): 8.1; 95% confidence interval (95% CI): 3.2-20.5; p < 0.001)), from the first driving session. The interaction between the two factors (conditionxtime of day) was also significant (F(5, 105) = 3.229; p < 0.05). Increasing sleepiness score was associated with increasing crossings during the next driving session in the sleep-restricted (IRR: 1.9; 95% CI: 1.4-2.4) but not in the non-restricted condition (IRR: 1.0; 95% CI: 0.8-1.3). Increasing self-perceived fatigue was not associated with increasing crossings in either condition (IRR: 0.95; 95% CI: 0.93-0.98 and IRR: 1.0; 95% CI: 0.98-1.02). Rested subjects drove 1000 km with four shorts breaks with only a minor performance decrease. Sleep restriction induced important performance degradation even though time awake (8h) and session driving times (105 min) were relatively short. Major inter-individual differences were observed under sleep restriction. Performance degradation was associated with sleepiness and not fatigue. Sleepiness combined with fatigue significantly affected RT. Road safety campaigns should encourage drivers to avoid driving after sleep restriction, even on relatively short trips especially if they feel sleepy. <s> BIB002 </s> Detecting Driver Drowsiness Based on Sensors: A Review <s> Comparison of Simulated and Real Driving Conditions <s> Abstract As part of the HASTE European Project, effects of visual and cognitive demand on driving performance and driver state were systematically investigated by means of artificial, or surrogate, In-vehicle Information Systems (S-IVIS). The present paper reports results from simulated and real motorway driving. Data were collected in a fixed base simulator, a moving base simulator and an instrumented vehicle driven in real traffic. The data collected included speed, lane keeping performance, steering wheel movements, eye movements, physiological signals and self-reported driving performance. The results show that the effects of visual and cognitive load affect driving performance in qualitatively different ways. Visual demand led to reduced speed and increased lane keeping variation. By contrast, cognitive load did not affect speed and resulted in reduced lane keeping variation. Moreover, the cognitive load resulted in increased gaze concentration towards the road centre. Both S-IVIS had an effect on physiological signals and the drivers’ assessment of their own driving performance. The study also investigated differences between the three experimental settings (static simulator, moving base simulator and field). The results are discussed with respect to the development of a generic safety test regime for In-vehicle Information Systems. <s> BIB003
It is not advisable to force a drowsy driver to drive on roads. Consequently, many experiments have been conducted in simulated environments and the results of the experiments are then elaborately studied. Dinges et al. presented various challenges involved in real time drowsiness detection  . The subjective self-assessment of drowsiness can only be obtained from subjects in simulated environments. In real conditions, it is unfeasible to obtain this information without significantly distracting the driver from their primary task. Some researchers have conducted experiments to confirm the validity of simulated driving environments. For example, Blana et al. observed that the mean lateral displacement of the vehicle from the center of the roadway, obtained in real and simulated environments is statistically different for speeds higher than 70 km/h. This finding implies that real-road drivers feel less safe at higher speeds and, as a result, increase their lateral distance. The drivers in a simulated environment, however, did not appear to perceive this risk BIB001 . Most experiments using behavioral measures are conducted in a simulated environment and the results indicate that it is a reliable method to detect drowsiness. However, in real driving conditions, the results might be significantly different because a moving vehicle can present challenges such as variations in lighting, change in background and vibration noise, not to mention the use of sunglasses, caps, etc. Philip et al. compared drowsiness in simulated and real conditions and concluded that it can be equally studied in both environments but the reaction time and the sleepiness self-evaluation are more affected in a simulated environment which provides a more monotonous task BIB002 . Engstorm et al. observed that the physiological workload and steering activity was higher in a real environment. This result can be interpreted as an indication of increased effort, which seems reasonable given the higher actual risk in real traffic BIB003 . Hence, while developing a drowsiness detection system, the simulated environment should be as close to a replica of the real environment as possible.