Modeling Driver’s Real-Time Confidence in Autonomous Vehicles
Autonomous vehicle technology has developed at an unprecedented rate in recent years. An increasing number of vehicles are equipped with different levels of driving assist systems to reduce the human driver’s burden. However, because of the conservative design of its programming framework, there is...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/7/4099 |
| _version_ | 1827747120409477120 |
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| author | Jiayi Lu Shichun Yang Yuan Ma Runwu Shi Zhaoxia Peng Zhaowen Pang Yuyi Chen Xinjie Feng Rui Wang Rui Cao Yibing Liu Qiuhong Wang Yaoguang Cao |
| author_facet | Jiayi Lu Shichun Yang Yuan Ma Runwu Shi Zhaoxia Peng Zhaowen Pang Yuyi Chen Xinjie Feng Rui Wang Rui Cao Yibing Liu Qiuhong Wang Yaoguang Cao |
| author_sort | Jiayi Lu |
| collection | DOAJ |
| description | Autonomous vehicle technology has developed at an unprecedented rate in recent years. An increasing number of vehicles are equipped with different levels of driving assist systems to reduce the human driver’s burden. However, because of the conservative design of its programming framework, there is still a large gap between the performance of current autonomous driving systems and experienced veteran drivers. This gap can cause drivers to distrust decisions or behaviors made by autonomous vehicles, thus affecting the effectiveness of drivers’ use of auto-driving systems. To further estimate the expected acceptance of autonomous driving systems in real human–machine co-driving situations, a characterization model of driver confidence has to be constructed. This paper conducts a survey of driver confidence in riding autonomous vehicles. Based on the analysis of results, the paper proposes a confidence quantification model called “the Virtual Confidence (VC)” by quantifying three main factors affecting driver confidence in autonomous vehicles, including (1) the intrusive movements of surrounding traffic participants, (2) the abnormal behavior of the ego vehicle, and (3) the complexity of the driving environment. The model culminates in a dynamic confidence bar with values ranging from 0 to 100 to represent the levels of confidence. The validation of the confidence model was verified by doing comparisons between the real-time output of the VC and the real-time feeling of human drivers on an autonomous vehicle simulator. The proposed VC model can potentially identify features that need improvement for auto-driving systems in unmanned tests and provide data reference. |
| first_indexed | 2024-03-11T05:43:39Z |
| format | Article |
| id | doaj.art-3e51969afb7944b182f51ee36f69e326 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:43:39Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-3e51969afb7944b182f51ee36f69e3262023-11-17T16:15:31ZengMDPI AGApplied Sciences2076-34172023-03-01137409910.3390/app13074099Modeling Driver’s Real-Time Confidence in Autonomous VehiclesJiayi Lu0Shichun Yang1Yuan Ma2Runwu Shi3Zhaoxia Peng4Zhaowen Pang5Yuyi Chen6Xinjie Feng7Rui Wang8Rui Cao9Yibing Liu10Qiuhong Wang11Yaoguang Cao12School of Transportation Science and Engineering, Beihang University, Beijing 100191, ChinaSchool of Transportation Science and Engineering, Beihang University, Beijing 100191, ChinaSchool of Transportation Science and Engineering, Beihang University, Beijing 100191, ChinaSchool of Transportation Science and Engineering, Beihang University, Beijing 100191, ChinaSchool of Transportation Science and Engineering, Beihang University, Beijing 100191, ChinaSchool of Transportation Science and Engineering, Beihang University, Beijing 100191, ChinaSchool of Transportation Science and Engineering, Beihang University, Beijing 100191, ChinaSchool of Transportation Science and Engineering, Beihang University, Beijing 100191, ChinaSchool of Transportation Science and Engineering, Beihang University, Beijing 100191, ChinaSchool of Transportation Science and Engineering, Beihang University, Beijing 100191, ChinaResearch Institute for Road Safety of MPS, Beijing 100062, ChinaResearch Institute for Road Safety of MPS, Beijing 100062, ChinaSchool of Transportation Science and Engineering, Beihang University, Beijing 100191, ChinaAutonomous vehicle technology has developed at an unprecedented rate in recent years. An increasing number of vehicles are equipped with different levels of driving assist systems to reduce the human driver’s burden. However, because of the conservative design of its programming framework, there is still a large gap between the performance of current autonomous driving systems and experienced veteran drivers. This gap can cause drivers to distrust decisions or behaviors made by autonomous vehicles, thus affecting the effectiveness of drivers’ use of auto-driving systems. To further estimate the expected acceptance of autonomous driving systems in real human–machine co-driving situations, a characterization model of driver confidence has to be constructed. This paper conducts a survey of driver confidence in riding autonomous vehicles. Based on the analysis of results, the paper proposes a confidence quantification model called “the Virtual Confidence (VC)” by quantifying three main factors affecting driver confidence in autonomous vehicles, including (1) the intrusive movements of surrounding traffic participants, (2) the abnormal behavior of the ego vehicle, and (3) the complexity of the driving environment. The model culminates in a dynamic confidence bar with values ranging from 0 to 100 to represent the levels of confidence. The validation of the confidence model was verified by doing comparisons between the real-time output of the VC and the real-time feeling of human drivers on an autonomous vehicle simulator. The proposed VC model can potentially identify features that need improvement for auto-driving systems in unmanned tests and provide data reference.https://www.mdpi.com/2076-3417/13/7/4099driver confidencequantitative modelautonomous vehicleshuman driversafety entropycollision analysis |
| spellingShingle | Jiayi Lu Shichun Yang Yuan Ma Runwu Shi Zhaoxia Peng Zhaowen Pang Yuyi Chen Xinjie Feng Rui Wang Rui Cao Yibing Liu Qiuhong Wang Yaoguang Cao Modeling Driver’s Real-Time Confidence in Autonomous Vehicles Applied Sciences driver confidence quantitative model autonomous vehicles human driver safety entropy collision analysis |
| title | Modeling Driver’s Real-Time Confidence in Autonomous Vehicles |
| title_full | Modeling Driver’s Real-Time Confidence in Autonomous Vehicles |
| title_fullStr | Modeling Driver’s Real-Time Confidence in Autonomous Vehicles |
| title_full_unstemmed | Modeling Driver’s Real-Time Confidence in Autonomous Vehicles |
| title_short | Modeling Driver’s Real-Time Confidence in Autonomous Vehicles |
| title_sort | modeling driver s real time confidence in autonomous vehicles |
| topic | driver confidence quantitative model autonomous vehicles human driver safety entropy collision analysis |
| url | https://www.mdpi.com/2076-3417/13/7/4099 |
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