Current automated vehicles depend on takeover requests to notify drivers when system limitations occur, and informative takeover requests that communicate contextual environmental information, such as potential road hazards, have been found effective in controlled driving experiments. However, the robustness of informative takeover requests across diverse traffic scenarios and various driver mental states remains unclear. Therefore, this study investigated how mental states (anger, sadness, happiness, mind-wandering, external distraction, fatigue), scenarios (lane changing and lane keeping), and modalities (visual and tactile signals) affect takeover performance through reaction time, decision-making time, and decision-making accuracy while adopting informative takeover requests. The results showed lower decision-making accuracy in lane-keeping than in lane-changing scenarios, with tactile signals providing marginally greater accuracy than visual signals. No main effects of mental states were found on takeover performance. Overall, the findings may inform the development of advanced human-machine interfaces across different mental states in automated systems.
AbeG.ItohM.YamamuraT. (2011). Visual distraction when driving and its influence on driver response to a forward collision warning system. SICE Annual Conference, 2011, 179–183.
2.
ChuA.LoW.-H.HuangG. (2024). Multimodal feedback for effective takeover in automated vehicles for hearing impairment [Conference session]. Adjunct proceedings of the 16th international conference on automotive user interfaces and interactive vehicular applications, Stanford, CA, USA, pp. 304–306.
3.
ChuD.WangR.DengY.LuL.WuC. (2020). Vibrotactile take-over requests in highly automated driving [Conference session]. 2020 4th CAA international conference on vehicular control and intelligence (CVCI), Hangzhou, China, 695–700. https://doi.org/10.1109/CVCI51460.2020.9338667
4.
DuN.ZhouF.PulverE. M.TilburyD. M.RobertL. P.PradhanA. K.YangX. J. (2020). Examining the effects of emotional valence and arousal on takeover performance in conditionally automated driving. Transportation Research Part C: Emerging Technologies, 112, 78–87. https://doi.org/10.1016/j.trc.2020.01.006
5.
DuN.ZhouF.TilburyD. M.RobertL. P.Jessie YangX. (2024). Behavioral and physiological responses to takeovers in different scenarios during conditionally automated driving. Transportation Research Part F: Traffic Psychology and Behaviour, 101, 320–331. https://doi.org/10.1016/j.trf.2024.01.008
6.
ErikssonA.StantonN. A. (2017). Takeover time in highly automated vehicles: Noncritical transitions to and from manual control. Human Factors, 59(4), 689–705. https://doi.org/10.1177/0018720816685832
7.
GrahamR. (1999). Use of auditory icons as emergency warnings: Evaluation within a vehicle collision avoidance application. Ergonomics, 42(9), 1233–1248. https://doi.org/10.1080/001401399185108
8.
HameedS.SarterN. (2009). Context-sensitive information presentation: Integrating adaptive and adaptable approaches to display design. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 53(22), 1694–1698. https://doi.org/10.1177/154193120905302208
9.
HeenanA.HerdmanC. M.BrownM. S.RobertN. (2014). Effects of conversation on situation awareness and working memory in simulated driving. Human Factors, 56(6), 1077–1092. https://doi.org/10.1177/0018720813519265
10.
HuangG.LiangN.WuC.PittsB. J. (2019). The impact of mind wandering on signal detection, semi-autonomous driving performance, and physiological responses. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 63(1), 2051–2055. https://doi.org/10.1177/1071181319631015
11.
HuangG.PittsB. J. (2022). Takeover requests for automated driving: The effects of signal direction, lead time, and modality on takeover performance. Accident Analysis & Prevention, 165, Article 106534. https://doi.org/10.1016/j.aap.2021.106534
12.
HuangG.PittsB. J. (2023). To inform or to instruct? An evaluation of meaningful vibrotactile patterns to support automated vehicle takeover performance. IEEE Transactions on Human-Machine Systems, 53(4), 678–687. https://doi.org/10.1109/THMS.2022.3205880
13.
JafarpourS.Rahimi-MovagharV. (2014). Determinants of risky driving behavior: A narrative review. Medical Journal of the Islamic Republic of Iran, 28, 142.
14.
JaroschO.BellemH.BenglerK. (2019). Effects of task-induced fatigue in prolonged conditional automated driving. Human Factors, 61(7), 1186–1199. https://doi.org/10.1177/0018720818816226
15.
LoW.-H.HuangG. (2025). Directional vibrotactile takeover requests on a wrist-worn device: Effects of age, pattern type, and urgency in automated driving. Accident Analysis & Prevention, 220, Article 108093. https://doi.org/10.1016/j.aap.2025.108093
16.
MaciantowiczO.ZajenkowskiM. (2021). Emotional experiences in vulnerable and grandiose narcissism: Anger and mood in neutral and anger evoking situations. Self and Identity, 20(5), 688–713. https://doi.org/10.1080/15298868.2020.1751694
17.
MartinezK. D.HuangG. (2022). The effects of tactile display on automated vehicle takeover: A literature review. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 66(1), 1305–1309. https://doi.org/10.1177/1071181322661391
18.
MartinezK. D.HuangG. (2024). From young to old: The effects of information presentation type, multimodal display, and age on situation awareness and processing time in automated vehicles. Transportation Research Part F: Traffic Psychology and Behaviour, 103, 128–140. https://doi.org/10.1016/j.trf.2024.04.002
19.
McDonaldA. D.AlambeigiH.EngströmJ.MarkkulaG.VogelpohlT.DunneJ.YumaN. (2019). Toward computational simulations of behavior during automated driving takeovers: A review of the empirical and modeling literatures. Human Factors, 61(4), 642–688. https://doi.org/10.1177/0018720819829572
20.
MengF.HoC.GrayR.SpenceC. (2015). Dynamic vibrotactile warning signals for frontal collision avoidance: Towards the torso versus towards the head. Ergonomics, 58(3), 411–425. https://doi.org/10.1080/00140139.2014.976278
21.
NaujoksF.MaiC.NeukumA. (2021). The effect of urgency of take-over requests during highly automated driving under distraction conditions. Advances in Human Aspects of Transportation: Part I). Advance online publication. https://doi.org/10.54941/ahfe100646
22.
Oviedo-TrespalaciosO.HaqueM. M.KingM.WashingtonS. (2016). Understanding the impacts of mobile phone distraction on driving performance: A systematic review. Transportation Research Part C: Emerging Technologies, 72, 360–380. https://doi.org/10.1016/j.trc.2016.10.006
23.
ParkK. S.HassC. J.FawverB.LeeH.JanelleC. M. (2019). Emotional states influence forward gait during music listening based on familiarity with music selections. Human Movement Science, 66, 53–62. https://doi.org/10.1016/j.humov.2019.03.004
24.
PetermeijerS.DoubekF.de WinterJ. (2017). Driver response times to auditory, visual, and tactile take-over requests: A simulator study with 101 participants [Conference session]. 2017 IEEE international conference on systems, man, and cybernetics (SMC), Banff, AB, Canada, pp. 1505–1510.
25.
PetermeijerS. M.CielerS.de WinterJ. C. F. (2017). Comparing spatially static and dynamic vibrotactile take-over requests in the driver seat. Accident Analysis & Prevention, 99, 218–227. https://doi.org/10.1016/j.aap.2016.12.001
26.
PoonM.SchutzM. (2015). Cueing musical emotions: An empirical analysis of 24-piece sets by Bach and Chopin documents parallels with emotional speech. Frontiers in Psychology, 6, Article 1419. https://doi.org/10.3389/fpsyg.2015.01419
27.
ThiffaultP.BergeronJ. (2003). Monotony of road environment and driver fatigue: A simulator study. Accident Analysis & Prevention, 35(3), 381–391. https://doi.org/10.1016/S0001-4575(02)00014-3
28.
YankoM. R.SpalekT. M. (2014). Driving with the wandering mind: The effect that mind-wandering has on driving performance. Human Factors, 56(2), 260–269. https://doi.org/10.1177/0018720813495280
29.
YunH.YangJ. H. (2020). Multimodal warning design for take-over request in conditionally automated driving. European Transport Research Review, 12(1), 34. https://doi.org/10.1186/s12544-020-00427-5
