# Driver Clustering Based on Individual Curve Path Selection Preference
## 4 Conclusions  
In this paper, we have collected existing solutions in the field of driver modeling, clustering and parameter identification to formulate a solution that can be used to make lane centering systems more human like. The following conclusions are drawn:  
* Linear Driver Model (LDM), a simple, static, parametric model can be used to efficiently characterize drivers by their curve path preferences. The model parameters can be fitted on measured data and can be used to cluster drivers into a dynamic and a cautious driving style group.
* The curve driving groups identified by the LDM parameters are also separated by vehicle kinematics, therefore they are indeed dynamic and cautious drivers, not only from curve driving perspective but from motion perspective, too.
* Extended Kalman Filter can be used to learn the LDM parameters in an environment where memory consumption shall be low (e.g., in an embedded environment). With this, the real-time application of the proposed driver classification workflow is possible.  
Our results prove that using simple solutions, personalization of lane centering systems is possible.  
However, there are certain limitations of the work. The results were generated using a sample dataset of 30 drivers. Even though such an amount of data can be used to prove that the concept is right, further analysis could also show the robustness and applicability of the method better. Also, the planned path can be different from the actual path of the vehicle, if there is a real tracking control in the loop. The effect of both components must be studied together to show that the proposed method can be applied in a real automated driving chain. This is going to be in the focus of our next research step.  
Figure 8: Proposed data flow for driver type learning, based on preliminary defined reference groups, and utilization by the path planning component.  
**Author Contributions:** Conceptualization, G.I., T.D., E.H. and K.N.; methodology, G.I. and T.D.; software, G.I.; validation, G.I. and T.D.; investigation, K.N. and G.I.; resources, E.H.; data curation, G.I. and K.N.; writing--original draft preparation, G.I. and T.D.; writing--review and editing, G.I.; supervision, E.H. and K.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the European Union within the framework of the National Laboratory for Autonomous Systems (RRF-2.3.1-21-2022-00002).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data description is found online: [[https://ieeexplore.ieee.org/document/10737549](https://ieeexplore.ieee.org/document/10737549)]([https://ieeexplore.ieee.org/document/10737549](https://ieeexplore.ieee.org/document/10737549)), accessed on 10 May 2025.  
**Conflicts of Interest:** Authors [PERSON] and [PERSON] were employed by the company Robert Bosch Kft. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.