Real-time driver drowsiness feedback improves driver alertness and self-reported driving performance
Open accessAbstract
Driver drowsiness has been implicated as a major causal factor in road accidents. Tools that allow remote monitoring and management of driver fatigue are used in the mining and road transport industries. Increasing drivers' own awareness of their drowsiness levels using such tools may also reduce risk of accidents. The study examined the effects of real-time blink-velocity-derived drowsiness feedback on driver performance and levels of alertness in a military setting. A sample of 15 Army Reserve personnel (1 female) aged 21-59 (M=41.3, SD=11.1) volunteered to being monitored by an infra-red oculography-based Optalert Alertness Monitoring System (OAMS) while they performed their regular driving tasks, including on-duty tasks and commuting to and from duty, for a continuous period of 4-8 weeks. For approximately half that period, blink-velocity-derived Johns Drowsiness Scale (JDS) scores were fed back to the driver in a counterbalanced repeated-measures design, resulting in a total of 419 driving periods under "feedback" and 385 periods under "no-feedback" condition. Overall, the provision of real-time feedback resulted in reduced drowsiness (lower JDS scores) and improved alertness and driving performance ratings. The effect was small and varied across the 24-h circadian cycle but it remained robust after controlling for time of day and driving task duration. Both the number of JDS peaks counted for each trip and their duration declined in the presence of drowsiness feedback, indicating a dynamic pattern that is consistent with a genuine, entropy-reducing feedback mechanism (as distinct from random re-alerting) behind the observed effect. Its mechanisms and practical utility have yet to be fully explored. Direct examination of the alternative, random re-alerting explanation of this feedback effect is an important step for future research.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The finding that ATCO alertness positively correlates with occurrences is not consistent with previous research showing high alertness related to better performance.
“The finding is not consistent with previous research that showed that high alertness was related to better performance (Aidman, Chadunow, Johnson, & Reece, 2015; Galy & Mélan, 2015; Sallinen et al., 2017).”
From The Analysis of Occurrences Associated with Air Traffic Volume and Air Traffic Controllers’ Alertness for Fatigue Risk Management (2021) · cites this paper as Aidman, Chadunow, Johnson, & Reece, 2015
The Results (§3.2) report that drivers receiving feedback rated themselves as more alert and rated their safe-distance keeping more highly. This supports a relationship between alertness and one self-reported aspect of driving performance, but the paper does not establish better performance generally: its other performance appraisals were not significantly improved, and the cited result is not about ATCOs. (LLM, conf=0.85)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879 · see citing works
Subjective and objective sleepiness in the active individual
Åkerstedt, T., & Gillberg, M. (1990) · International Journal of Neuroscience · doi:10.3109/00207459008994241 · Cited by 3,088 · cited 3× · see citing works
“, 2007; Åkerstedt and Gillberg, 1990). The KSS is typically used as an instantaneous measure of sleepiness (Åkerstedt and Gillberg, 1990). The version utilised in this study had participants record their current level of alertness on a 10-point scale, which is a reverse of the Kaida et al. (2007) instrument, ranging f…”
MethodInfluentialThe link between fatigue and safety
Williamson, A. M., Lombardi, D. A., Folkard, S., Stutts, J. C., Courtney, T. K., & CONNOR, J. L. (2010) · Accident Analysis & Prevention · doi:10.1016/j.aap.2009.11.011 · Cited by 824 · cited 4× · see citing works
“Fatigue is also a construct that links factors such as time of day, time since waking, task duration and monotony, with safety-related outcomes (Ackerman, 2011; Williamson et al., 2011).”
BackgroundMethodInfluentialMonotony of road environment and driver fatigue: a simulator study.
Thiffault, P., & Bergeron, J. (2003) · Accident Analysis and Prevention · doi:10.1016/s0001-4575(02)00014-3 · Cited by 726 · cited 1×
“duration (Folkard, 1997) and monotony (Thiffault and Bergeron, 2003), such as that experienced in highway driving (Horne and Reyner, 1995b).”
BackgroundCharacteristics of crashes attributed to the driver having fallen asleep.
Pack, A., Pack, A. M., Rodgman, E., Cucchiara, A., Dinges, D., & Schwab, C. (1995) · Accident Analysis and Prevention · doi:10.1016/0001-4575(95)00034-8 · Cited by 575 · cited 1× · see citing works
“Accidents caused by driver drowsiness can have a similar fatality rate to alcohol-related crashes (Pack et al., 1995).”
BackgroundMay, J. F., & Baldwin, C. L. (2009) · Transportation Research Part F: Traffic Psychology and Behaviour · doi:10.1016/j.trf.2008.11.005 · Cited by 466 · cited 1× · see citing works
“In the driving context this leads to observable changes in driver performance such as reduced capacity to maintain speed, distance between vehicles and lanekeeping (May and Baldwin, 2009), all of which increase the risk of road accidents (Adell et al.”
BackgroundRelationship between alertness, performance, and body temperature in humans
Wright, K. P., Hull, J. T., & Czeisler, C. A. (2002) · American Journal of Physiology-Regulatory, Integrative and Comparative Physiology · doi:10.1152/ajpregu.00205.2002 · Cited by 447 · cited 2× · see citing works
“It is also well established that the 24 h circadian rhythm is marked with peaks and troughs in alertness levels as evidence by studies incorporating both subjective and objective sleepiness measures (Monk et al., 1997; Wright et al., 2002).”
BackgroundSleep related vehicle accidents
Cook, C. C. H., & Albery, I. (1995) · British medical journal · doi:10.1136/bmj.310.6991.1411b · Cited by 445 · see citing works
Perclos: A valid psychophysiological measure of alertness as assessed by psychomotor vigilance
Dinges, D., & Grace, R. (1998) · PsycEXTRA Dataset · doi:10.1037/e449092008-001 · Cited by 356 · see citing works
Managing fatigue: It's about sleep
Dawson, D. E., & McCulloch, K. M. (2005) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2005.03.002 · Cited by 339 · see citing works
Circadian rhythms in human performance and mood under constant conditions
Monk, T., Buysse, D. J., Reynolds, C., Berga, S., Jarrett, D., Begley, A., & Kupfer, D. (1997) · Journal of Sleep Research · doi:10.1046/j.1365-2869.1997.00023.x · Cited by 316 · cited 2× · see citing works
“It is also well established that the 24 h circadian rhythm is marked with peaks and troughs in alertness levels as evidence by studies incorporating both subjective and objective sleepiness measures (Monk et al., 1997; Wright et al., 2002).”
BackgroundSWEDISH NATIONAL ROAD AND TRANSPORT RESEARCH INSTITUTE
Knutsson, U. K. (2003) · doi:10.1016/s0386-1112(14)60150-3 · Cited by 298
Sleepiness and driving: the experience of UK car drivers
Maycock, G. (1996) · Journal of Sleep Research · doi:10.1111/j.1365-2869.1996.00229.x · Cited by 258 · see citing works
Sleep disorders, medical conditions, and road accident risk
Smolensky, M. H., Milia, L. V. D., Ohayon, M. M., & Philip, P. (2010) · Accident Analysis & Prevention · doi:10.1016/j.aap.2009.12.004 · Cited by 234 · cited 1× · see citing works
“, 2000), lack of sleep (Dawson and McCulloch, 2005), and medical conditions (Smolensky et al., 2011).”
BackgroundBlack times: temporal determinants of transport safety.
Folkard, S. (1997) · Accident Analysis and Prevention · doi:10.1016/s0001-4575(97)00021-3 · Cited by 230 · cited 1× · see citing works
“duration (Folkard, 1997) and monotony (Thiffault and Bergeron, 2003), such as that experienced in highway driving (Horne and Reyner, 1995b).”
BackgroundSleepiness and driving: the experience of U.K. car drivers.
Maycock, G. (1997) · Accident Analysis and Prevention · doi:10.1016/s0001-4575(97)00024-9 · Cited by 186 · cited 1× · see citing works
“Driver drowsiness has been implicated in road accidents both within professional (Maycock, 1997) and general driving populations (Horne and Reyner, 1995a).”
BackgroundHazard perception in novice and experienced drivers: The effects of sleepiness
Smith, S. S., Horswill, M. S., Chambers, B., & Wetton, M. A. (2009) · Accident Analysis & Prevention · doi:10.1016/j.aap.2009.03.016 · Cited by 168 · see citing works
The effects of a driver assistance system for safe speed and safe distance - A real-life field study
Adell, E., Várhelyi, A., & Fontana, M. D. (2011) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2010.04.006 · Cited by 166 · see citing works
Johns, M., Tucker, A., Chapman, R., Crowley, K., & Michael, N. (2007) · doi:10.1007/s11818-007-0311-y · Cited by 166 · cited 3×
“, 2008b) and sensitivity/specificity in detecting cognitive performance decline caused by sleep deprivation (Johns et al., 2007), driving (Johns et al.”
BackgroundMethodInfluentialHorne, J. A., & Reyner, L. A. (1995) · Journal of Sleep Research · doi:10.1111/j.1365-2869.1995.tb00222.x · Cited by 159 · see citing works
Work patterns and fatigue-related risk among junior doctors
Gander, P., Purnell, H., Garden, A., & Woodward, A. (2007) · Occupational and Environmental Medicine · doi:10.1136/oem.2006.030916 · Cited by 147 · see citing works
Beersma, D. G. M., & Achermann, P. (1995) · Journal of Sleep Research · doi:10.1111/j.1365-2869.1995.tb00147.x · Cited by 112 · see citing works
Kaida, K., Åkerstedt, T., Kecklund, G., Nilsson, J., & Axelsson, J. (2007) · Industrial Health · doi:10.2486/indhealth.45.520 · Cited by 75 · see citing works
A new method for assessing the risks of drowsiness while driving
Johns, M. W., Chapman, R. J., Crowley, K. E., & Tucker, A. (2008) · Somnologie - Schlafforschung und Schlafmedizin · doi:10.1007/s11818-008-0330-3 · Cited by 57 · see citing works
IMPLEMENTATION OF 12-HOUR SHIFTS IN A BRAZILIAN PETROCHEMICAL PLANT: IMPACT ON SLEEP AND ALERTNESS
Fischer, F. M., Moreno, C. R. D. C., Borges, F. N. D. S., & Louzada, F. M. (2000) · Chronobiology International · doi:10.1081/cbi-100101062 · Cited by 45 · see citing works
Ferguson, S., Baker, A., Lamond, N., Kennaway, D., & Dawson, D. (2010) · Applied Ergonomics · doi:10.1016/j.apergo.2010.05.001 · Cited by 45 · cited 1× · see citing works
“, 2009) and ‘fly-in fly-out’ mining workers (Ferguson et al., 2010).”
Life-space foam: A medium for motivational and cognitive dynamics
Ivancevic, V., & Aidman, E. (2007) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2007.04.025 · Cited by 31 · cited 1× · see citing works
“This dynamic pattern is consistent with reduced entropy (Ivancievic and Aidman, 2007).”
BackgroundSelf-citationA drowsiness detection system for pilots: Optalert.
Ma, C. (2009) · doi:10.3357/asem.21001.2009 · Cited by 13 · cited 2×
“, 2008a) and aircraft operation (Corbett, 2009).”
BackgroundWhat Psychology as a Science Owes Neal Miller: The Example of His Biofeedback Research
Taub, E. (2010) · doi:10.5298/1081-5937-38.3.108 · Cited by 5 · cited 2×
“If the observed difference in the average drowsiness levels (mean JDS scores over a driving period) was due to a genuine feedback effect then, according to biofeedback models (Taub, 2010) JDS peak duration should be shorter, indicating feedback-driven entropy reduction.”
Background
Flagged first, then by citations
11 of the 40 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- Aidman, E., Chadunow, C., Johnson, K., 2012. Effects of real-time drowsiness feedback on driver fatigue and performance in Army Reserve personnel. Paper presented at Defence Human Sciences Symposium, November 2012; Adelaide.
- Aidman, E., Chadunow, C., Johnson, K., Coombs, G., Fidock, J., 2012b. Driver Fatigue in an Army Reserve Regiment: A Multi-Method Field Study with Experimental Manipulation. DSTO-TR-2759. Defence Science & Technology Organisation, Edinburgh, South Australia, pp. 1-63.
- CaterpillarGlobalMining, 2008. Operator Fatigue Detection Technology Review. Caterpillar, Peoria, IL.
Cited by
Sorted by citations
A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving
Lohani, M., Payne, B. R., & Strayer, D. L. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00057 · Cited by 347 · see citing works
“, 2017) and warning signs or verbal communication (Schmidt et al., 2011; Aidman et al., 2015) can be used by an automated system to modify drivers’ cognitive state.”
BackgroundMethodTowards Detection of Bus Driver Fatigue Based on Robust Visual Analysis of Eye State
Mandal, B., Li, L., Wang, G. S., & Lin, J. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2582900 · Cited by 335 · see citing works
“IR cameras have also been used in several other systems for open eye detection, gaze direction estimation, and head pose tracking for driver fatigue detection [24], [26], [30], [37].”
BackgroundMethodDynamic driver fatigue detection using hidden Markov model in real driving condition
Fu, R., Wang, H., & Zhao, W. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.06.042 · Cited by 228 · see citing works
Tjolleng, A., Jung, K., Hong, W.-G., Lee, W., Lee, B., You, H., Son, J., & Park, S. (2016) · Applied Ergonomics · doi:10.1016/j.apergo.2016.09.013 · Cited by 143 · see citing works
A contextual and temporal algorithm for driver drowsiness detection
McDonald, A. D., Lee, J. D., Schwarz, C., & Brown, T. L. (2018) · Accident Analysis & Prevention · doi:10.1016/j.aap.2018.01.005 · Cited by 104 · see citing works
Mahajan, K., Velaga, N., Kumar, A., Choudhary, A., & Choudhary, P. (2019) · Transportation Research Part F: Traffic Psychology and Behaviour · doi:10.1016/j.trf.2018.10.028 · Cited by 89
“Morning rush hours, post-lunch or post mid-night periods are certain safety-critical timings, which are associated with low circadian rhythms and high decrement in alertness while driving (Zhang et al., 2014; Matthews et al., 2012; Aidman et al., 2015; Zhang et al., 2016).”
BackgroundSurvey and Synthesis of State of the Art in Driver Monitoring
Halin, A., Verly, J., & Droogenbroeck, M. V. (2021) · Sensors · doi:10.3390/s21165558 · Cited by 87 · see citing works
Driver fatigue detection based on prefrontal EEG using multi-entropy measures and hybrid model
Min, J., Chen, X., Zhang, Y., & Cai, M. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102857 · Cited by 84 · see citing works
Blink: Characteristics, Controls, and Relation to Dry Eyes
Rodriguez, J. D., Lane, K. J., Ousler, G. W., Angjeli, E., Smith, L. M., & Abelson, M. B. (2017) · Current Eye Research · doi:10.1080/02713683.2017.1381270 · Cited by 82 · see citing works
Using Muse: Rapid Mobile Assessment of Brain Performance
Krigolson, O., Hammerstrom, M. R., Abimbola, W., Trska, R., Wright, B., Hecker, K., & Binsted, G. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.634147 · Cited by 75 · see citing works
“In any event, one of the biggest problems with cognitive fatigue is that it is difficult to accurately assess with self-reporting being particularly problematic (Belz et al., 2004; Baranski, 2007; Schmidt et al., 2009; Aidman et al., 2015).”
BackgroundMethodPERCLOS-based technologies for detecting drowsiness: current evidence and future directions
Abe, T. (2023) · SLEEP Advances · doi:10.1093/sleepadvances/zpad006 · Cited by 88 · see citing works
“Additionally, JDS scores were found to decrease with caffeine intake [110, 111], decrease with real-time drowsiness feedback [112], and increase with benzodiazepine use [81].”
BackgroundA Systematic Survey of Driving Fatigue Monitoring
Zhang, Z., Ning, H., & Zhou, F. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3189346 · Cited by 82 · see citing works
“To prevent the influence of external factors such as light and wearing sunglasses, the use of infrared-based cameras can effectively solve this problem [6], [21], [46], [66], [90], [91], [97].”
BackgroundMethodInfluentialFatigue in transport: a review of exposure, risks, checks and controls
Phillips, R. O., Kecklund, G., Anund, A., & Sallinen, M. (2017) · Transport Reviews · doi:10.1080/01441647.2017.1349844 · Cited by 63 · see citing works
Cori, J. M., Anderson, C., Soleimanloo, S. S., Jackson, M. L., & Howard, M. E. (2019) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2019.03.004 · Cited by 63 · see citing works
Driving mental workload and performance of ageing drivers
Rahman, N. I. A., Dawal, S. Z. M., & Yusoff, N. (2020) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2020.01.019 · Cited by 57 · see citing works
Drowsiness measures for commercial motor vehicle operations
Sparrow, A. R., LaJambe, C. M., & Dongen, H. P. A. V. (2018) · Accident Analysis & Prevention · doi:10.1016/j.aap.2018.04.020 · Cited by 52 · see citing works
Bi, C., Huang, J., Xing, G., Jiang, L., Liu, X., & Chen, M. (2017) · IEEE/ACM International Conference on Internet-of-Things Design and Implementation (IoTDI) · doi:10.1145/3054977.3054979 · Cited by 19 · see citing works
“To keep the driver concentrating on the driving, the eective methods include alerting the driver for the incoming danger [4] or giving feedback for the driver’s level of drowsiness [1].”
MethodAidman, E., Jackson, S., & Kleitman, S. (2018) · Applied Cognitive Psychology · doi:10.1002/acp.3463 · Cited by 60 · see citing works
Self-citationDrowsiness Mitigation Through Driver State Monitoring Systems: A Scoping Review
Ayas, S., Donmez, B., & Tang, X. (2023) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208231208523 · Cited by 40 · see citing works
“Feedback displays that present the detected driver alertness levels have been implemented alone (Kundinger et al., 2021) or in conjunction with warnings (Aidman et al., 2015; Nishigaki & Shirakata, 2019).”
InfluentialEvaluating driver drowsiness countermeasures
Gaspar, J. G., Brown, T., Schwarz, C., Lee, J. D., Kang, J. J., & Higgins, J. S. (2017) · Traffic Injury Prevention · doi:10.1080/15389588.2017.1303140 · Cited by 39
“It is unclear what type of feedback might work best to promote longer-term behavioral change (though see Aidman et al. 2015).”
BackgroundEffects of Partial Sleep Deprivation on Braking Response of Drivers in Hazard Scenarios
Mahajan, K., & Velaga, N. R. (2020) · Accident Analysis & Prevention · doi:10.1016/j.aap.2020.105545 · Cited by 44 · see citing works
Lenné, M., & Jacobs, E. (2016) · doi:10.1080/1463922x.2016.1155239 · Cited by 34
“…approaches that look to perform well in research settings in the laboratory (Anderson et al. 2013), and have begun to be used in on-road studies (Aidman et al. 2015) the suitability of these and other technologies reviewed by Dawson, Searle, and Paterson (2014) should be explored further as…”
MethodLi, W., Kearney, P., Zhang, J., Hsu, Y., & Braithwaite, G. (2021) · Risk Analysis · doi:10.1111/risa.13594 · Cited by 34 · see citing works
A Systematic Review on Driver Drowsiness Detection Using Eye Activity Measures
Kolus, A. (2024) · IEEE Access · doi:10.1109/access.2024.3424654 · Cited by 33
“Alternatively, IR-OG, or infrared reflection technology, is more suitable for real-life implementation because of its ability to measure a driver’s eye activity under different light conditions, with or without prescription glasses, contact lenses, or sunglasses [86], [158], [159], [160], [161].”
BackgroundRiaz, F., Khadim, S., Rauf, R., Ahmad, M., Jabbar, S., & Chaudhry, J. (2018) · Comput. Networks · doi:10.1016/j.comnet.2018.06.007 · Cited by 23 · see citing works
Caffeine reduces the impact of drowsiness on driving errors
Aidman, E., Johnson, K., Paech, G. M., Vedova, C. B. D., Pajcin, M., Grant, C., Kamimori, G. H., Mitchelson, E., Hoggan, B. L., Fidock, J. J. T., et al. (2018) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2018.01.008 · Cited by 21 · see citing works
Self-citationChwalek, P., Ramsay, D. B., & Paradiso, J. (2021) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3478079 · Cited by 19 · see citing works
“This algorithm not only captures blink timestamps; it captures useful metadata about each blink that has been shown to correlate with alertness [12].”
The Effects of ADAS on Driving Behavior: A Case Study
Bosurgi, G., Pellegrino, O., Ruggeri, A., & Sollazzo, G. (2023) · Italian National Conference on Sensors · doi:10.3390/s23041758 · Cited by 18
Geiger-Brown, J. M., Harlow, A. B., Bagshaw, B., Sagherian, K., & Hinds, P. S. (2021) · Workplace Health & Safety · doi:10.1177/21650799211038003 · Cited by 19 · see citing works
“A better solution may be for nurses or hospitals to purchase in-car drowsy driving detection technology to provide real-time alerting to increase the driver’s own awareness of when drowsiness occurs during the drive (Aidman et al., 2015).”
BackgroundAn evaluation and comparison of commercial driver sleepiness detection technology: a rapid review
Cori, J., Manousakis, J., Koppel, S., Ferguson, S., Sargent, C., Howard, M., & Anderson, C. (2021) · Physiological Measurement · doi:10.1088/1361-6579/abfbb8 · Cited by 18 · see citing works
Zhang, J.-H., Xue, J., Deng, Y., Li, Z., & Li, Y. (2025) · Journal of Organizational Behavior · doi:10.1002/job.2874 · Cited by 20 · see citing works
Learning-Based Instantaneous Drowsiness Detection Using Wired and Wireless Electroencephalography
Choi, H.-S., Min, S., Kim, S., Bae, H., Yoon, J., Hwang, I., Oh, D., Yun, C., & Yoon, S. (2019) · IEEE Access · doi:10.1109/access.2019.2946053 · Cited by 21 · see citing works
“, slow movements, increased closure time, and increased blinking frequency) [19], [24], [26]–[28].”
Choi, H.-S., Kim, S., Oh, J. E., Yoon, J. E., Park, J. A., Yun, C., & Yoon, S. (2018) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3233547.3233567 · Cited by 14 · see citing works
Bi, C., Huang, J., Xing, G., Jiang, L., Liu, X., & Chen, M. (2019) · ACM Transactions on Cyber-Physical Systems · doi:10.1145/3360323 · Cited by 33 · see citing works
“To keep the driver concentrating on the driving, effective methods are alerting the driver to the incoming danger [3] or giving feedback on the driver’s level of drowsiness [1].”
MethodKontaxi, A., Ζιακόπουλος, Α., & Yannis, G. (2025) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2025.05.028 · Cited by 13 · see citing works
Real-time Alarm Monitoring System for Detecting Driver Fatigue in Wireless Areas
Fu, R., Wang, S.-T., & Wang, S. (2017) · PROMET - Traffic&Transportation · doi:10.7307/ptt.v29i2.2058 · Cited by 12 · see citing works
“This is inevitably accompanied by a decrease in alertness and results in performance dec-rements and a higher risk of accidents [8].”
BackgroundWang, Z., Wan, Q., Qin, Y., Fan, S.-Q., & Xiao, Z.-Y. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-019-01609-3 · Cited by 9
A review of cyber vigilance tasks for network defense
Guidetti, O., Speelman, C. P., & Bouhlas, P. (2023) · Frontiers in Neuroergonomics · doi:10.3389/fnrgo.2023.1104873 · Cited by 6 · see citing works
“These measurements all correlated with changes in CDT performance over time, a result which accorded with an abundance of studies on vigilance while driving (Thiffault and Bergeron, 2003a,b; Tan and Zhang, 2006; D’Orazio et al., 2007; Sommer and Golz, 2010; Jo et al., 2014; Aidman et al., 2015; Cabrall et al., 2016; Z…”
ResultKratzel, L., Glos, M., Veauthier, C., Rekow, S., François, C., Fietze, I., & Penzel, T. (2021) · Physiological Measurement · doi:10.1088/1361-6579/abdb7e · Cited by 8 · see citing works
Vehicle and Highway Adaptations to Compensate for Sleepy Drivers.
Howard, M., Cori, J., & Horrey, W. (2019) · Sleep Medicine Clinics · doi:10.1016/j.jsmc.2019.08.005 · Cited by 8
Effects of adopting voice assistants countermeasures on alerting drowsy drivers across age
Zhang, W., Carsten, O., Goodridge, C. M., & Zhang, W. (2025) · Applied Ergonomics · doi:10.1016/j.apergo.2025.104616 · Cited by 10 · see citing works
Zhao, Y., & Yamamoto, T. (2020) · Energies · doi:10.3390/en13071593 · Cited by 6 · see citing works
Kaduk, S. I., Roberts, A. P. J., & Stanton, N. A. (2020) · Theoretical Issues in Ergonomics Science · doi:10.1080/1463922x.2020.1758829 · Cited by 11 · see citing works
Individual Variability in Brain Connectivity Patterns and Driving-Fatigue Dynamics
Giannakopoulou, O., Kakkos, I., Dimitrakopoulos, G. Ν., Tarousi, M., Sun, Y., Bezerianos, A., Koutsouris, D., & Matsopoulos, G. K. (2024) · Sensors · doi:10.3390/s24123894 · Cited by 5 · see citing works
Gianfranchi, E., Tagliabue, M., Megías‐Robles, A., & Maldonado, A. (2021) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2021.10.001 · Cited by 5 · see citing works
A Framework for Lane Prediction on Unstructured Roads
Dhanakshirur, R. R., Pillai, P. J., Tabib, R., Patil, U., & Mudenagudi, U. (2018) · Communications in Computer and Information Science · doi:10.1007/978-981-13-5758-9_16 · Cited by 5
Li, P.-H., Zhu, S., Zhang, N., Gong, J.-G., Li, X., Dong, C., & Yan, X. (2025) · Transportation Research Part F: Traffic Psychology and Behaviour · doi:10.1016/j.trf.2025.07.018 · Cited by 4
Time for sleep science to wake up to drowsy driver monitoring
Howard, M. E., & Cori, J. (2023) · Sleep · doi:10.1093/sleep/zsad324 · Cited by 4
“Limited studies have shown that implementing DMS with in-cabin alerts for drowsiness can reduce drowsiness events by over 60% and improve driving performance in occupational settings [9, 10].”
BackgroundPoliak, M., Beňuš, J., & Nica, E. (2021) · Transportation Research Procedia · doi:10.1016/j.trpro.2021.07.172 · Cited by 4
Toward Wearable EEG-based Alertness Detection System Using SVM with Optimal Minimum Channels
Yang, M., Li, H.-Y., Sun, X., Yang, L., Duan, H.-L., Che, Y., & Han, C.-X. (2018) · doi:10.1051/matecconf/201821403009 · Cited by 4
DUIncoder: Learning to Detect Driving Under the Influence Behaviors from Various Normal Driving Data
Zhou, H., Carballo, A., Yamaoka, M., Yamataka, M., Fujii, K., & Takeda, K. (2025) · Italian National Conference on Sensors · doi:10.3390/s25061699 · Cited by 2 · see citing works
“Driver state monitoring aims to recognize negative driver states at an early stage to minimize the potential for traffic accidents [9,10].”
Predicting Drowsy Driver Break Taking During Long Drives
Gaspar, J. G., Tefft, B., Carney, C., & Horrey, W. J. (2024) · Hum. Factors · doi:10.1177/00187208241293707 · Cited by 2
“Driver monitoring interventions include feedback (e.g., Aidman et al., 2015), alerts (e.g., Fitzharris et al., 2017), and tuning the level of automation (e.g., Hayashi et al., 2021).”
Howard, M. E., & Horrey, W. J. (2024) · Sleep · doi:10.1093/sleep/zsae220 · Cited by 2
“Other advanced driver assistance systems, such as lane departure warnings, lane keeping assist, and automated driving systems that take over vehicle operation are also becoming wide spread.”
Operating a remote autonomous train: degradation of senses and cooperation
Berdal, Q., Michel, E., Richard, P., & Paglia, C. (2023) · Ergonomie Et Informatique Avancée · doi:10.1145/3624323.3624330 · Cited by 3 · see citing works
“Il est important de noter cependant que les travaux pris en référence ([3]) portent sur un contexte différent et se limitent à un indicateur unique.”
BackgroundZahed, K., Smith, A., McDonald, A. D., & Sasangohar, F. (2022) · IISE Transactions on Occupational Ergonomics and Human Factors · doi:10.1080/24725838.2022.2094502 · Cited by 4 · see citing works
“…et al., 2017), caffeine and naps (Horne & Reyner, 1996), increasing arousal levels through auditory signals (Nazari et al., 2017), and technology (Aidman et al., 2015) – have shown promise, but are not prevalent because they pose a significant burden on hospitals and nurses to implement…”
BackgroundShams, Z., Sanij, H. K., Afshani, A., Ramezani-Khansari, E., Nejad, F., & Olazar, M. (2022) · Jurnal Kejuruteraan · doi:10.17576/jkukm-2022-34(2)-16 · Cited by 2
“2015), driver’s drowsiness has been considered one of the most significant facts causing the accidents (Aidman et al. 2015; Zhang & Chan 2014).”
BackgroundModel Predictive Control of Shallow Drowsiness: Improving Productivity of Office Workers
Kogo, T., Tsujikawa, M., Kiuchi, Y., Nishino, A., & Hashimoto, S. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc.2019.8856562 · Cited by 2
“It was also reported that subjects who were less drowsy achieved higher performance when doing basic tasks [11][12].”
Aidman, E., Johnson, K. T., Hoggan, B., Fidock, J., Paech, G., Vedova, C. D. D., Pajcin, M., Grant, C. L., Kamimori, G. H., Mitchelson, E., et al. (2018) · Data in Brief · doi:10.1016/j.dib.2018.06.006 · Cited by 2
Self-citationTowards cooperative management of fatigue and vigilance in railway operations
Berdal, Q., Gadmer, Q., & Richard, P. (2024) · ITM Web of Conferences · doi:10.1051/itmconf/20246903006 · Cited by 2 · see citing works
Hasan, A. S., Jalayer, M., & Das, S. (2026) · Transportation Research Record · doi:10.1177/03611981261425972 · Cited by 1 · see citing works
Shiomi, Y., & Zou, T. (2025) · Accident Analysis & Prevention · doi:10.1016/j.aap.2025.108359 · Cited by 2 · see citing works
Manouchehri, T., Fereidooni, R., Heydari, S., & Lankarani, K. B. (2025) · Shiraz E Medical Journal · doi:10.5812/semj-158678 · Cited by 1
Driver Fatigue Detection Based on Multiple Physiological Signals and an Improved Deep Belief Network
Wang, L., Liu, Y., Yin, X.-W., Li, J., & Gu, Y. (2025) · Information Technology and Control · doi:10.5755/j01.itc.54.1.39833 · Cited by 1
“Wang et al. [ 23] verified that cervical lumbar electromyography electromyography ( Based on above literatures review, in the real-time driver fatigue detection field, the selection of signals, denoising methods, and classification methods are still challenges [1, 24].”
Smith, A., McDonald, A. D., Sasangohar, F., Sosa, L., Lazaro, L., & Ouko, C. (2020) · doi:10.1177/1071181320641380 · Cited by 1
“This interpretation is aligned with other research on drowsy driving feedback that has shown that drivers improve subjective assessments after receiving feedback on their drowsiness (Aidman et al., 2015).”
ResultPsychometric evaluation of the Chinese version of the Toronto Hospital Alertness Test
Li, S., Fong, D., Wong, J., Wilkinson, K., Shapiro, C., Choi, E. P., McPherson, B., Lam, C., & Ip, M. (2020) · Journal of Patient-Reported Outcomes · doi:10.1186/s41687-020-00197-7 · Cited by 1 · see citing works
Stephanidis, C., Duffy, V. G., Streitz, N. A., Konomi, S., & Krömker, H. (2020) · Interacción · doi:10.1007/978-3-030-59987-4 · Cited by 1
“A society’s position on this dimension is reflected in whether people’s self-image is defined in terms of “I” or “we” [10].”
BackgroundInfluentialA Practical View of the Similarity and Differences Among the Impaired Driver States in Legal Driving
Shen, D., Spisak, T., Chen, Y., & Tian, R. (2020) · Interacción · doi:10.1007/978-3-030-59987-4_15 · Cited by 1
ArithHomFA: A toolkit for oblivious online STL monitoring
Waga, M., Matsuoka, K., & Suwa, T. (2026) · Science of Computer Programming · doi:10.1016/j.scico.2026.103539
Chen, M., Wei, J., Du, H.-L., Jia, Y., & Wang, S. (2026) · Displays (Guildford) · doi:10.1016/j.displa.2026.103510
Driver speed dynamics in pedestrian crossing warnings: A multilevel functional regression approach
Huo, Y.-H., Peng, Z., Wang, C., Li, D., Wang, Y.-G., & Easa, S. M. (2026) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1016/j.cacaie.2026.100150
Kontaxi, A., Ziakopoulos, A., & Yannis, G. (2026) · Transportation Research Part F: Traffic Psychology and Behaviour · doi:10.1016/j.trf.2026.103657
Towards management of vigilance in railway operations through drivers state feedback loop
Berdal, Q., Gadmer, Q., & Richard, P. (2025) · Cognition, Technology & Work · doi:10.1007/s10111-025-00828-3
Cori, J. M., Booker, L. A., Sletten, T. L., Rajaratnam, S. M. W., Stanley, D., Francis, L., McMahon, K., Wilson, K., Mulhall, M., & Howard, M. E. (2025) · Journal of Occupational and Environmental Medicine · doi:10.1097/jom.0000000000003350
“21,22 Additionally, these studies demonstrated an improvement in objectively measured harsh braking and self-reported safe-distance keeping, although other self-reported measures of responsiveness or lane keeping did not differ.”
Detecting Drowsy Driving Through Eye Closure and Yawning : Azure Custom Vision System
G., K., G, P., R, D., & Nimalan, P. (2024) · International Conference on Signals and Electronic Systems · doi:10.1109/icses63445.2024.10762996
“Investigating the efficacy of signals at 3100 Hz and 450 Hz, the study found that 92% of individuals awoke from the latter, demonstrating its effectiveness [7].”
Analysis of a Driver's Visual Behaviour and its Relationship with the Smart Road Context
Bosurgi, G., Pellegrino, O., Sollazzo, G., & Ruggeri, A. (2024) · IEEE International Workshop on Measurement and Networking · doi:10.1109/mn60932.2024.10615270
“Performance measures mainly refer to the dynamic characteristics of the drive, such as trajectory, speed, longitudinal and transverse acceleration, steering control [12-15].”
BackgroundGorvenia, J., & Tello, F. (2021) · CIIS Ulima Congreso Internacional de Ingeniería de Sistemas · doi:10.26439/ciis2018.5495
A Simple Framework for Context-Aware Driver Performance
Ali, H., Muhammad, A., & Khan, M. M. (2020) · 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc45102.2020.9294730
“In several studies [4]–[10], researchers used different driving data and have proposed various driving performance metrics.”
BackgroundKhan, F., Sharma, S., & Arulalan, M. R. (2018) · International Conference on Intelligent Systems Design and Applications · doi:10.1007/978-3-030-16657-1_24
“Further, studies such as [7] use driver response feedback monitoring systems.”
An offline autonomous vehicular actuator control system
Ramotsoela, T. D., & le Roux, J. L. (2016) · International Conference on Industrial Informatics · doi:10.1109/indin.2016.7819343
“One of the major contributory factors to fatal car crashes has been identified as driver fatigue [2].”
BackgroundFramework for Process Mining in Cargo Transportation Safety – PM2RCS
Bernardi, J. S. V. D. S., & Santos, E. A. P. (2025) · Gestão & Produção · doi:10.1590/1806-9649-2025v32e1524 · Cited by 2 · see citing works
Sivasankaran, S. K., Bharadwaj, R., & Balasubramanian, V. (2021) · doi:10.1007/978-981-15-9054-2_66
A Novel Approach for Real-Time Drowsiness Detection and Alert to Driver
Pandey, S., Bharti, R., Verma, P., & Ahmad, T. (2020) · doi:10.1007/978-981-15-4692-1_28
Basori, A., & Malebary, S. (2020) · doi:10.1007/978-3-030-46335-9_7
- Authors
- Aidman, E., Chadunow, C., Johnson, K., & Reece, J.
- Affiliations
- The University of Sydney (AU)
- Defence Science and Technology Group (AU)
- ACAP University College (AU)
- Corresponding author
- Aidman, Eugene
- Venue
- Accident Analysis & Prevention
- Publisher
- Elsevier BV
- Published
- 2015-08
- Volume
- 81
- Pages
- 8-13
- ISSN
- 0001-4575 (ISSN-L)
- OpenAlex
- W2047332672
- Cited by
- 92
- References
- 40
- Citations by year
- 2026: 6 · 2025: 11 · 2024: 7 · 2023: 7 · 2022: 5 · 2021: 13 · 2020: 11 · 2019: 10 · 2018: 10 · 2017: 5 · 2016: 7
- FWCI
- 11.06
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Accident Analysis & Prevention (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RMIT Research Repository (RMIT University Library) (submittedVersion, other-oa)
- Figshare (submittedVersion, cc-by)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and Work-Related Fatigue
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Life Sciences › Neuroscience › Cognitive Neuroscience › Mind wandering and attention
- Keywords
- driver drowsiness, real-time feedback, driver alertness, eye movement recording, military vehicle drivers, driving performance, circadian rhythm, fatigue monitoring, road safety
- MeSH terms
- Accidents, Traffic, Adult, Attention, Automobile Driving, Biofeedback, Psychology, Blinking, Electrooculography, Fatigue, Female, Humans, Male, Middle Aged, Signal Processing, Computer-Assisted, Sleep Stages, Young Adult, Self Report
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
