Human-Centric Spatial Cognition Detecting System Based on Drivers’ Electroencephalogram Signals for Autonomous Driving
Existing autonomous driving systems face challenges in accurately capturing drivers’ cognitive states, often resulting in decisions misaligned with drivers’ intentions. To address this limitation, this study introduces a pioneering human-centric spatial cognition detecting system based on drivers’ e...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/397 |
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| author | Yu Cao Bo Zhang Xiaohui Hou Minggang Gan Wei Wu |
| author_facet | Yu Cao Bo Zhang Xiaohui Hou Minggang Gan Wei Wu |
| author_sort | Yu Cao |
| collection | DOAJ |
| description | Existing autonomous driving systems face challenges in accurately capturing drivers’ cognitive states, often resulting in decisions misaligned with drivers’ intentions. To address this limitation, this study introduces a pioneering human-centric spatial cognition detecting system based on drivers’ electroencephalogram (EEG) signals. Unlike conventional EEG-based systems that focus on intention recognition or hazard perception, the proposed system can further extract drivers’ spatial cognition across two dimensions: relative distance and relative orientation. It consists of two components: EEG signal preprocessing and spatial cognition decoding, enabling the autonomous driving system to make more contextually aligned decisions regarding the targets drivers focus on. To enhance the detection accuracy of drivers’ spatial cognition, we designed a novel EEG signal decoding method called a Dual-Time-Feature Network (DTFNet). This approach integrates coarse-grained and fine-grained temporal features of EEG signals across different scales and incorporates a Squeeze-and-Excitation module to evaluate the importance of electrodes. The DTFNet outperforms existing methods, achieving 65.67% and 50.65% accuracy in three-class tasks and 84.46% and 70.50% in binary tasks. Furthermore, we investigated the temporal dynamics of drivers’ spatial cognition and observed that drivers’ perception of relative distance occurs slightly later than their perception of relative orientation, providing valuable insights into the temporal aspects of cognitive processing. |
| format | Article |
| id | doaj-art-9a350a045e4d4452bfa79ef2498642bb |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-9a350a045e4d4452bfa79ef2498642bb2025-01-24T13:48:46ZengMDPI AGSensors1424-82202025-01-0125239710.3390/s25020397Human-Centric Spatial Cognition Detecting System Based on Drivers’ Electroencephalogram Signals for Autonomous DrivingYu Cao0Bo Zhang1Xiaohui Hou2Minggang Gan3Wei Wu4School of Automation, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Automation, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Automation, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Automation, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Automation, Beijing Institute of Technology, Beijing 100081, ChinaExisting autonomous driving systems face challenges in accurately capturing drivers’ cognitive states, often resulting in decisions misaligned with drivers’ intentions. To address this limitation, this study introduces a pioneering human-centric spatial cognition detecting system based on drivers’ electroencephalogram (EEG) signals. Unlike conventional EEG-based systems that focus on intention recognition or hazard perception, the proposed system can further extract drivers’ spatial cognition across two dimensions: relative distance and relative orientation. It consists of two components: EEG signal preprocessing and spatial cognition decoding, enabling the autonomous driving system to make more contextually aligned decisions regarding the targets drivers focus on. To enhance the detection accuracy of drivers’ spatial cognition, we designed a novel EEG signal decoding method called a Dual-Time-Feature Network (DTFNet). This approach integrates coarse-grained and fine-grained temporal features of EEG signals across different scales and incorporates a Squeeze-and-Excitation module to evaluate the importance of electrodes. The DTFNet outperforms existing methods, achieving 65.67% and 50.65% accuracy in three-class tasks and 84.46% and 70.50% in binary tasks. Furthermore, we investigated the temporal dynamics of drivers’ spatial cognition and observed that drivers’ perception of relative distance occurs slightly later than their perception of relative orientation, providing valuable insights into the temporal aspects of cognitive processing.https://www.mdpi.com/1424-8220/25/2/397electroencephalogram (EEG)automatic drivingspatial cognitionhuman–machine cooperation |
| spellingShingle | Yu Cao Bo Zhang Xiaohui Hou Minggang Gan Wei Wu Human-Centric Spatial Cognition Detecting System Based on Drivers’ Electroencephalogram Signals for Autonomous Driving Sensors electroencephalogram (EEG) automatic driving spatial cognition human–machine cooperation |
| title | Human-Centric Spatial Cognition Detecting System Based on Drivers’ Electroencephalogram Signals for Autonomous Driving |
| title_full | Human-Centric Spatial Cognition Detecting System Based on Drivers’ Electroencephalogram Signals for Autonomous Driving |
| title_fullStr | Human-Centric Spatial Cognition Detecting System Based on Drivers’ Electroencephalogram Signals for Autonomous Driving |
| title_full_unstemmed | Human-Centric Spatial Cognition Detecting System Based on Drivers’ Electroencephalogram Signals for Autonomous Driving |
| title_short | Human-Centric Spatial Cognition Detecting System Based on Drivers’ Electroencephalogram Signals for Autonomous Driving |
| title_sort | human centric spatial cognition detecting system based on drivers electroencephalogram signals for autonomous driving |
| topic | electroencephalogram (EEG) automatic driving spatial cognition human–machine cooperation |
| url | https://www.mdpi.com/1424-8220/25/2/397 |
| work_keys_str_mv | AT yucao humancentricspatialcognitiondetectingsystembasedondriverselectroencephalogramsignalsforautonomousdriving AT bozhang humancentricspatialcognitiondetectingsystembasedondriverselectroencephalogramsignalsforautonomousdriving AT xiaohuihou humancentricspatialcognitiondetectingsystembasedondriverselectroencephalogramsignalsforautonomousdriving AT mingganggan humancentricspatialcognitiondetectingsystembasedondriverselectroencephalogramsignalsforautonomousdriving AT weiwu humancentricspatialcognitiondetectingsystembasedondriverselectroencephalogramsignalsforautonomousdriving |