Evan Gallouin, Xuguang Wang, P. Beillas, T. Bellet
{"title":"根据自动驾驶过程中的脱离程度接管性能","authors":"Evan Gallouin, Xuguang Wang, P. Beillas, T. Bellet","doi":"10.17077/dhm.31754","DOIUrl":null,"url":null,"abstract":"Taking over the manual control of a car after Automated Driving (AD) is a key issue for future road safety. However, performance to resume this manual control may be dependant of the driver’s level of engagement in driving during AD. Indeed, according to the level of automation (from L2 to L3 of the SAE), drivers will be in charge of monitoring the driving situation, or will be allowed to perform non-driving related tasks (NDRT) and thus, to be fully disengaged of the driving task. In this context, the present study aims to investigate the influence of the driver’s level of engagement/disengagement during AD on takeover performance using a driving simulator. Four levels of engagement/disengagement were studied: (C1) being engaged in driving situation monitoring without TakeOver Request (TOR) to resume the manual control, (C2) being engaged in driving situation monitoring with a TOR to resume the manual control, (C3) being disengaged of the driving monitoring by performing a cognitively demanding secondary task with a TOR to resume the manual control, and (C4) being disengaged of the driving monitoring in a relaxed position situation with eyes closed and with a TOR to resume the manual control. Forty participants were performed sixteen critical takeover scenarios involving different critical takeover situations. Drivers reaction times and collision risks were measured to assess their takeover performances and to investigate the safety of automation levels 2 and 3. Driving situation monitoring with a TOR (C2) induce shortest reaction times and a lower number of collisions. For the relaxed posture (C4), drivers took longer time to react than the other three conditions. Driving situation monitoring without TOR (C1), had the highest number of collisions. This suggests that the engagement in driving is not always effective and efficient without TOR. Moreover, being in a relaxed position during automated driving decreases takeover performance.","PeriodicalId":111717,"journal":{"name":"Proceedings of the 7th International Digital Human Modeling Symposium (DHM 2022) and Iowa Virtual Human Summit 2022 -","volume":"64 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Takeover performance according to the level of disengagement during automated driving\",\"authors\":\"Evan Gallouin, Xuguang Wang, P. Beillas, T. Bellet\",\"doi\":\"10.17077/dhm.31754\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Taking over the manual control of a car after Automated Driving (AD) is a key issue for future road safety. However, performance to resume this manual control may be dependant of the driver’s level of engagement in driving during AD. Indeed, according to the level of automation (from L2 to L3 of the SAE), drivers will be in charge of monitoring the driving situation, or will be allowed to perform non-driving related tasks (NDRT) and thus, to be fully disengaged of the driving task. In this context, the present study aims to investigate the influence of the driver’s level of engagement/disengagement during AD on takeover performance using a driving simulator. Four levels of engagement/disengagement were studied: (C1) being engaged in driving situation monitoring without TakeOver Request (TOR) to resume the manual control, (C2) being engaged in driving situation monitoring with a TOR to resume the manual control, (C3) being disengaged of the driving monitoring by performing a cognitively demanding secondary task with a TOR to resume the manual control, and (C4) being disengaged of the driving monitoring in a relaxed position situation with eyes closed and with a TOR to resume the manual control. Forty participants were performed sixteen critical takeover scenarios involving different critical takeover situations. Drivers reaction times and collision risks were measured to assess their takeover performances and to investigate the safety of automation levels 2 and 3. Driving situation monitoring with a TOR (C2) induce shortest reaction times and a lower number of collisions. For the relaxed posture (C4), drivers took longer time to react than the other three conditions. Driving situation monitoring without TOR (C1), had the highest number of collisions. This suggests that the engagement in driving is not always effective and efficient without TOR. Moreover, being in a relaxed position during automated driving decreases takeover performance.\",\"PeriodicalId\":111717,\"journal\":{\"name\":\"Proceedings of the 7th International Digital Human Modeling Symposium (DHM 2022) and Iowa Virtual Human Summit 2022 -\",\"volume\":\"64 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 7th International Digital Human Modeling Symposium (DHM 2022) and Iowa Virtual Human Summit 2022 -\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.17077/dhm.31754\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 7th International Digital Human Modeling Symposium (DHM 2022) and Iowa Virtual Human Summit 2022 -","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17077/dhm.31754","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Takeover performance according to the level of disengagement during automated driving
Taking over the manual control of a car after Automated Driving (AD) is a key issue for future road safety. However, performance to resume this manual control may be dependant of the driver’s level of engagement in driving during AD. Indeed, according to the level of automation (from L2 to L3 of the SAE), drivers will be in charge of monitoring the driving situation, or will be allowed to perform non-driving related tasks (NDRT) and thus, to be fully disengaged of the driving task. In this context, the present study aims to investigate the influence of the driver’s level of engagement/disengagement during AD on takeover performance using a driving simulator. Four levels of engagement/disengagement were studied: (C1) being engaged in driving situation monitoring without TakeOver Request (TOR) to resume the manual control, (C2) being engaged in driving situation monitoring with a TOR to resume the manual control, (C3) being disengaged of the driving monitoring by performing a cognitively demanding secondary task with a TOR to resume the manual control, and (C4) being disengaged of the driving monitoring in a relaxed position situation with eyes closed and with a TOR to resume the manual control. Forty participants were performed sixteen critical takeover scenarios involving different critical takeover situations. Drivers reaction times and collision risks were measured to assess their takeover performances and to investigate the safety of automation levels 2 and 3. Driving situation monitoring with a TOR (C2) induce shortest reaction times and a lower number of collisions. For the relaxed posture (C4), drivers took longer time to react than the other three conditions. Driving situation monitoring without TOR (C1), had the highest number of collisions. This suggests that the engagement in driving is not always effective and efficient without TOR. Moreover, being in a relaxed position during automated driving decreases takeover performance.