Multidimensional Assessment of Takeover Performance in Conditionally Automated Driving
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arXiv
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| Format: | Preprint |
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2025
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| author | Liang, Kexin Kästle, Jan Luca Anvari, Bani Calvert, Simeon C. van Lint, J. W. C. |
| author_facet | Liang, Kexin Kästle, Jan Luca Anvari, Bani Calvert, Simeon C. van Lint, J. W. C. |
| contents | When automated driving systems encounter complex situations beyond their operational capabilities, they issue takeover requests, prompting drivers to resume vehicle control and return to the driving loop as a critical safety backup. However, this control transition places significant demands on drivers, requiring them to promptly respond to takeover requests while executing high-quality interventions. To ensure safe and comfortable control transitions, it is essential to develop a deep understanding of the key factors influencing various takeover performance aspects. This study evaluates drivers' takeover performance across three dimensions: response efficiency, user experience, and driving safety - using a driving simulator experiment. EXtreme Gradient Boosting (XGBoost) models are used to investigate the contributions of two critical factors, i.e., Situational Awareness (SA) and Spare Capacity (SC), in predicting various takeover performance metrics by comparing the predictive results to the baseline models that rely solely on basic Driver Characteristics (DC). The results reveal that (i) higher SA enables drivers to respond to takeover requests more quickly, particularly for reflexive responses; and (ii) SC shows a greater overall impact on takeover quality than SA, where higher SC generally leads to enhanced subjective rating scores and objective execution trajectories. These findings highlight the distinct yet complementary roles of SA and SC in shaping performance components, offering valuable insights for optimizing human-vehicle interactions and enhancing automated driving system design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_22252 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multidimensional Assessment of Takeover Performance in Conditionally Automated Driving Liang, Kexin Kästle, Jan Luca Anvari, Bani Calvert, Simeon C. van Lint, J. W. C. Human-Computer Interaction When automated driving systems encounter complex situations beyond their operational capabilities, they issue takeover requests, prompting drivers to resume vehicle control and return to the driving loop as a critical safety backup. However, this control transition places significant demands on drivers, requiring them to promptly respond to takeover requests while executing high-quality interventions. To ensure safe and comfortable control transitions, it is essential to develop a deep understanding of the key factors influencing various takeover performance aspects. This study evaluates drivers' takeover performance across three dimensions: response efficiency, user experience, and driving safety - using a driving simulator experiment. EXtreme Gradient Boosting (XGBoost) models are used to investigate the contributions of two critical factors, i.e., Situational Awareness (SA) and Spare Capacity (SC), in predicting various takeover performance metrics by comparing the predictive results to the baseline models that rely solely on basic Driver Characteristics (DC). The results reveal that (i) higher SA enables drivers to respond to takeover requests more quickly, particularly for reflexive responses; and (ii) SC shows a greater overall impact on takeover quality than SA, where higher SC generally leads to enhanced subjective rating scores and objective execution trajectories. These findings highlight the distinct yet complementary roles of SA and SC in shaping performance components, offering valuable insights for optimizing human-vehicle interactions and enhancing automated driving system design. |
| title | Multidimensional Assessment of Takeover Performance in Conditionally Automated Driving |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2507.22252 |