Strategic naps in automated driving − Sleep architecture predicts sleep inertia better than nap duration
•Positive effects of napping in automated driving emerge only after a phase of negative effects.•Stable sleep, deep sleep and, to a lesser extent, light sleep result in considerable sleep inertia.•Sleep architecture predicts sleep inertia better than nap duration.•Transferring the strategic NASA nap...
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description | •Positive effects of napping in automated driving emerge only after a phase of negative effects.•Stable sleep, deep sleep and, to a lesser extent, light sleep result in considerable sleep inertia.•Sleep architecture predicts sleep inertia better than nap duration.•Transferring the strategic NASA nap paradigm to automated driving is not the ideal solution.
At higher levels of driving automation, drivers can nap during parts of the trip but must take over control in others. Awakening from a nap is marked by sleep inertia which is tackled by the NASA nap paradigm in aviation: Strategic on-flight naps are restricted to 40 min to avoid deep sleep and therefore sleep inertia. For future automated driving, there are currently no such strategies for addressing sleep inertia. Given the disparate requirements, it is uncertain whether the strategies derived from aviation can be readily applied to automated driving. Therefore, our study aimed to compare the effects of restricting the duration of nap opportunities following the NASA nap paradigm to the effects of sleep architecture on sleep inertia in takeover scenarios in automated driving.
In our driving simulator study, 24 participants were invited to sleep during three automated drives. They were awakened after 20, 40, or 60 min and asked to manually complete an urban drive. We assessed how napping duration, last sleep stage before takeover, and varying proportions of light, stable, and deep sleep influenced self-reported sleepiness, takeover times, and the number of driving errors.
Takeover times increased with nap duration, but sleepiness and driving errors did not. Instead, all measures were significantly influenced by sleep architecture. Sleepiness increased after awakening from light and stable sleep, and takeover times after awakening from light sleep. Takeover times also increased with higher proportions of stable sleep. The number of driving errors was significantly increased with the proportion of deep sleep and after awakenings from stable and deep sleep.
These results suggest that sleep architecture, not nap duration, is crucial for predicting sleep inertia. Therefore, the NASA nap paradigm is not suitable for driving contexts. Future driver monitoring systems should assess the sleep architecture to predict and prevent sleep inertia. |
doi_str_mv | 10.1016/j.aap.2024.107811 |
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At higher levels of driving automation, drivers can nap during parts of the trip but must take over control in others. Awakening from a nap is marked by sleep inertia which is tackled by the NASA nap paradigm in aviation: Strategic on-flight naps are restricted to 40 min to avoid deep sleep and therefore sleep inertia. For future automated driving, there are currently no such strategies for addressing sleep inertia. Given the disparate requirements, it is uncertain whether the strategies derived from aviation can be readily applied to automated driving. Therefore, our study aimed to compare the effects of restricting the duration of nap opportunities following the NASA nap paradigm to the effects of sleep architecture on sleep inertia in takeover scenarios in automated driving.
In our driving simulator study, 24 participants were invited to sleep during three automated drives. They were awakened after 20, 40, or 60 min and asked to manually complete an urban drive. We assessed how napping duration, last sleep stage before takeover, and varying proportions of light, stable, and deep sleep influenced self-reported sleepiness, takeover times, and the number of driving errors.
Takeover times increased with nap duration, but sleepiness and driving errors did not. Instead, all measures were significantly influenced by sleep architecture. Sleepiness increased after awakening from light and stable sleep, and takeover times after awakening from light sleep. Takeover times also increased with higher proportions of stable sleep. The number of driving errors was significantly increased with the proportion of deep sleep and after awakenings from stable and deep sleep.
These results suggest that sleep architecture, not nap duration, is crucial for predicting sleep inertia. Therefore, the NASA nap paradigm is not suitable for driving contexts. Future driver monitoring systems should assess the sleep architecture to predict and prevent sleep inertia.</description><identifier>ISSN: 0001-4575</identifier><identifier>ISSN: 1879-2057</identifier><identifier>EISSN: 1879-2057</identifier><identifier>DOI: 10.1016/j.aap.2024.107811</identifier><identifier>PMID: 39427445</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Adult ; Automated driving ; Automation ; Automobile Driving ; Computer Simulation ; Female ; Humans ; Male ; Napping duration ; Sleep ; Sleep - physiology ; Sleep architecture ; Sleep inertia ; Sleep Stages - physiology ; Sleepiness ; Takeover ; Time Factors ; Wakefulness - physiology ; Young Adult</subject><ispartof>Accident analysis and prevention, 2025-01, Vol.209, p.107811, Article 107811</ispartof><rights>2024 The Author(s)</rights><rights>Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c278t-3d14d344290aec869ad1ee3fc1fd385f39d19ebd7740c257dcebbc3f1cdd62c83</cites><orcidid>0000-0001-7184-2326 ; 0000-0001-9800-5456 ; 0009-0005-5396-9039 ; 0000-0002-9796-1136</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.aap.2024.107811$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,777,781,3537,27905,27906,45976</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39427445$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Tomzig, Markus</creatorcontrib><creatorcontrib>Wörle, Johanna</creatorcontrib><creatorcontrib>Gary, Sebastian</creatorcontrib><creatorcontrib>Baumann, Martin</creatorcontrib><creatorcontrib>Neukum, Alexandra</creatorcontrib><title>Strategic naps in automated driving − Sleep architecture predicts sleep inertia better than nap duration</title><title>Accident analysis and prevention</title><addtitle>Accid Anal Prev</addtitle><description>•Positive effects of napping in automated driving emerge only after a phase of negative effects.•Stable sleep, deep sleep and, to a lesser extent, light sleep result in considerable sleep inertia.•Sleep architecture predicts sleep inertia better than nap duration.•Transferring the strategic NASA nap paradigm to automated driving is not the ideal solution.
At higher levels of driving automation, drivers can nap during parts of the trip but must take over control in others. Awakening from a nap is marked by sleep inertia which is tackled by the NASA nap paradigm in aviation: Strategic on-flight naps are restricted to 40 min to avoid deep sleep and therefore sleep inertia. For future automated driving, there are currently no such strategies for addressing sleep inertia. Given the disparate requirements, it is uncertain whether the strategies derived from aviation can be readily applied to automated driving. Therefore, our study aimed to compare the effects of restricting the duration of nap opportunities following the NASA nap paradigm to the effects of sleep architecture on sleep inertia in takeover scenarios in automated driving.
In our driving simulator study, 24 participants were invited to sleep during three automated drives. They were awakened after 20, 40, or 60 min and asked to manually complete an urban drive. We assessed how napping duration, last sleep stage before takeover, and varying proportions of light, stable, and deep sleep influenced self-reported sleepiness, takeover times, and the number of driving errors.
Takeover times increased with nap duration, but sleepiness and driving errors did not. Instead, all measures were significantly influenced by sleep architecture. Sleepiness increased after awakening from light and stable sleep, and takeover times after awakening from light sleep. Takeover times also increased with higher proportions of stable sleep. The number of driving errors was significantly increased with the proportion of deep sleep and after awakenings from stable and deep sleep.
These results suggest that sleep architecture, not nap duration, is crucial for predicting sleep inertia. Therefore, the NASA nap paradigm is not suitable for driving contexts. Future driver monitoring systems should assess the sleep architecture to predict and prevent sleep inertia.</description><subject>Adult</subject><subject>Automated driving</subject><subject>Automation</subject><subject>Automobile Driving</subject><subject>Computer Simulation</subject><subject>Female</subject><subject>Humans</subject><subject>Male</subject><subject>Napping duration</subject><subject>Sleep</subject><subject>Sleep - physiology</subject><subject>Sleep architecture</subject><subject>Sleep inertia</subject><subject>Sleep Stages - physiology</subject><subject>Sleepiness</subject><subject>Takeover</subject><subject>Time Factors</subject><subject>Wakefulness - physiology</subject><subject>Young Adult</subject><issn>0001-4575</issn><issn>1879-2057</issn><issn>1879-2057</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2025</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kE1OwzAQhS0EoqVwADbISzYpduzUjlihij-pEovC2nLsSeuqTYLtVOIGrDkiJ8GlwJLV6M28eaP5EDqnZEwJnVytxlp345zkPGkhKT1AQypFmeWkEIdoSAihGS9EMUAnIaySFFIUx2jASp4LzoshWs2j1xEWzuBGdwG7Bus-tpvUs9h6t3XNAn--f-D5GqDD2puli2Bi7wF3HqwzMeDwPXMN-Og0riBG8DgudbPLxLZPF1zbnKKjWq8DnP3UEXq5u32ePmSzp_vH6c0sM7mQMWOWcss4z0uiwchJqS0FYLWhtWWyqFlpaQmVFYITkxfCGqgqw2pqrJ3kRrIRutzndr597SFEtXHBwHqtG2j7oBilUjJesEmy0r3V-DYED7XqvNto_6YoUTvEaqUSYrVDrPaI087FT3xfbcD-bfwyTYbrvQHSk1sHXgXjoDEJlk_klG3dP_FffHiPJQ</recordid><startdate>202501</startdate><enddate>202501</enddate><creator>Tomzig, Markus</creator><creator>Wörle, Johanna</creator><creator>Gary, Sebastian</creator><creator>Baumann, Martin</creator><creator>Neukum, Alexandra</creator><general>Elsevier Ltd</general><scope>6I.</scope><scope>AAFTH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-7184-2326</orcidid><orcidid>https://orcid.org/0000-0001-9800-5456</orcidid><orcidid>https://orcid.org/0009-0005-5396-9039</orcidid><orcidid>https://orcid.org/0000-0002-9796-1136</orcidid></search><sort><creationdate>202501</creationdate><title>Strategic naps in automated driving − Sleep architecture predicts sleep inertia better than nap duration</title><author>Tomzig, Markus ; Wörle, Johanna ; Gary, Sebastian ; Baumann, Martin ; Neukum, Alexandra</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c278t-3d14d344290aec869ad1ee3fc1fd385f39d19ebd7740c257dcebbc3f1cdd62c83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2025</creationdate><topic>Adult</topic><topic>Automated driving</topic><topic>Automation</topic><topic>Automobile Driving</topic><topic>Computer Simulation</topic><topic>Female</topic><topic>Humans</topic><topic>Male</topic><topic>Napping duration</topic><topic>Sleep</topic><topic>Sleep - physiology</topic><topic>Sleep architecture</topic><topic>Sleep inertia</topic><topic>Sleep Stages - physiology</topic><topic>Sleepiness</topic><topic>Takeover</topic><topic>Time Factors</topic><topic>Wakefulness - physiology</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tomzig, Markus</creatorcontrib><creatorcontrib>Wörle, Johanna</creatorcontrib><creatorcontrib>Gary, Sebastian</creatorcontrib><creatorcontrib>Baumann, Martin</creatorcontrib><creatorcontrib>Neukum, Alexandra</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Accident analysis and prevention</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tomzig, Markus</au><au>Wörle, Johanna</au><au>Gary, Sebastian</au><au>Baumann, Martin</au><au>Neukum, Alexandra</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Strategic naps in automated driving − Sleep architecture predicts sleep inertia better than nap duration</atitle><jtitle>Accident analysis and prevention</jtitle><addtitle>Accid Anal Prev</addtitle><date>2025-01</date><risdate>2025</risdate><volume>209</volume><spage>107811</spage><pages>107811-</pages><artnum>107811</artnum><issn>0001-4575</issn><issn>1879-2057</issn><eissn>1879-2057</eissn><abstract>•Positive effects of napping in automated driving emerge only after a phase of negative effects.•Stable sleep, deep sleep and, to a lesser extent, light sleep result in considerable sleep inertia.•Sleep architecture predicts sleep inertia better than nap duration.•Transferring the strategic NASA nap paradigm to automated driving is not the ideal solution.
At higher levels of driving automation, drivers can nap during parts of the trip but must take over control in others. Awakening from a nap is marked by sleep inertia which is tackled by the NASA nap paradigm in aviation: Strategic on-flight naps are restricted to 40 min to avoid deep sleep and therefore sleep inertia. For future automated driving, there are currently no such strategies for addressing sleep inertia. Given the disparate requirements, it is uncertain whether the strategies derived from aviation can be readily applied to automated driving. Therefore, our study aimed to compare the effects of restricting the duration of nap opportunities following the NASA nap paradigm to the effects of sleep architecture on sleep inertia in takeover scenarios in automated driving.
In our driving simulator study, 24 participants were invited to sleep during three automated drives. They were awakened after 20, 40, or 60 min and asked to manually complete an urban drive. We assessed how napping duration, last sleep stage before takeover, and varying proportions of light, stable, and deep sleep influenced self-reported sleepiness, takeover times, and the number of driving errors.
Takeover times increased with nap duration, but sleepiness and driving errors did not. Instead, all measures were significantly influenced by sleep architecture. Sleepiness increased after awakening from light and stable sleep, and takeover times after awakening from light sleep. Takeover times also increased with higher proportions of stable sleep. The number of driving errors was significantly increased with the proportion of deep sleep and after awakenings from stable and deep sleep.
These results suggest that sleep architecture, not nap duration, is crucial for predicting sleep inertia. Therefore, the NASA nap paradigm is not suitable for driving contexts. Future driver monitoring systems should assess the sleep architecture to predict and prevent sleep inertia.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>39427445</pmid><doi>10.1016/j.aap.2024.107811</doi><orcidid>https://orcid.org/0000-0001-7184-2326</orcidid><orcidid>https://orcid.org/0000-0001-9800-5456</orcidid><orcidid>https://orcid.org/0009-0005-5396-9039</orcidid><orcidid>https://orcid.org/0000-0002-9796-1136</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Adult Automated driving Automation Automobile Driving Computer Simulation Female Humans Male Napping duration Sleep Sleep - physiology Sleep architecture Sleep inertia Sleep Stages - physiology Sleepiness Takeover Time Factors Wakefulness - physiology Young Adult |
title | Strategic naps in automated driving − Sleep architecture predicts sleep inertia better than nap duration |
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