## 5 Discussion & Conclusions  
We propose a simple and real-time approach to estimate a target vehicle's intent at urban intersections. It is based on a driver model for representing the real driver's intersection maneuvering and use of an IMM to classify the target intent. It is demonstrated that the proposed method allows us to achieve good classification performance via simulation study.  
This paper focused on the implementation of the proposed algorithm and a real road test. By applying the proposed algorithm to an emergency driver support system, or an automated driving technology, it is expected that intersection safety can be significantly improved.  
Since the IMM approach using extended Kalman filters (EKFs) for multi-target state estimation of intelligent vehicle has already been verified by Figure 4, and in the work of [PERSON] et al. [28], the proposed algorithm will be improved in the urban intersection, which is a more complex driving situation using the perception of a multi-target vehicle. Furthermore, it is expected to be equipped as a generalized perception module for various applications of advanced driver assistance systems (ADAS) which use the lane change assistance system, intersection driving assistance system, steering assist system, side-crash prevention system, and autonomous emergency braking system. Using our proposed algorithm, it is possible to make generic assessments as well as an overall assessment of collision risks with multi-target vehicles in complex urban intersections.  
Figure 4: Multi-target state estimation test results for complex urban road.  
Figure 3: Mode probability update results for left turning case.  
Considering a mixed traffic situation, which would include many other cars and more lanes with different types of intersection shapes in urban roads, a deep learning approach should be taken into account in future research.  
The deep-learning approach is also useful to streamline the method stated in this paper to some degree of complex urban road. Since urban traffic is quite complicated and hard to predict, the algorithm needs to be improved using both a rule-based and deep-learning approach. The rule-based EKF approach, as previously demonstrated in this paper, is still beneficial to guarantee the safety of a vehicle in any driving situation.  
Conceptualization, D.S., M.P. and S.Y.; Methodology, D.S., K.-m.P. and S.Y.; Software, D.S. and K.-m.P.; Validation, D.S.; Formal Analysis, D.S.; Investigation, D.S. and K.-m.P.; Resources, M.P. and S.Y.; Data curation, K.-m.P.; Writing--Original Draft Preparation, D.S and S.Y.; Writing--Review and Editing, K.-m.P. and M.P.; Visualization, K.-m.P.; Supervision, M.P.; Project Administration, D.S.; Funding Acquisition, M.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant (code 20 PQOW-B152473-02) from R&D Program funded by Ministry of Land, Infrastructure and Transport of Korean government, by the Road Traffic Authority grant funded by the Korea government (KNPA) (No. POLICE-L-00001-02-101, Development of control system and AI driving capability test for autonomous driving), and by the Sookmyung Women's University Research Grants (1-1903-2022).  
The authors declare no conflict of interest.