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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.08640 |
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| _version_ | 1866910299241578496 |
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| author | Igneczi, Gergo Dobay, Tamas |
| author_facet | Igneczi, Gergo Dobay, Tamas |
| contents | The rapid development of automated driving systems in recent years has led to improvements in road safety and travel comfort. One typical function of these systems is Lane Keep Assist, which generally does not take human driving preferences into account. In our previous work, we have demonstrated that it is possible to implement a Lane Keep Assist function that is appropriate to human preferences using a trajectory planning algorithm based on a linear driving model. In our current work, we investigated how to separate the driving styles of individual drivers. We assumed that there are three driving styles: sporty, neutral and defensive. To prove these relations, clustering methods were applied to previously recorded measurements . Simulations with parameters describing the average behaviour of the classes (re-simulated with clustered types) showed that the resulting paths successfully classified drivers, that the 3 classes are distinct in their behaviour and that our model reproduces these behaviours. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_08640 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Typification of Driver Models Using Clustering Methods Igneczi, Gergo Dobay, Tamas Robotics Systems and Control The rapid development of automated driving systems in recent years has led to improvements in road safety and travel comfort. One typical function of these systems is Lane Keep Assist, which generally does not take human driving preferences into account. In our previous work, we have demonstrated that it is possible to implement a Lane Keep Assist function that is appropriate to human preferences using a trajectory planning algorithm based on a linear driving model. In our current work, we investigated how to separate the driving styles of individual drivers. We assumed that there are three driving styles: sporty, neutral and defensive. To prove these relations, clustering methods were applied to previously recorded measurements . Simulations with parameters describing the average behaviour of the classes (re-simulated with clustered types) showed that the resulting paths successfully classified drivers, that the 3 classes are distinct in their behaviour and that our model reproduces these behaviours. |
| title | Typification of Driver Models Using Clustering Methods |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2401.08640 |