## 5 Conclusions  
This paper introduces a novel approach for determining the decision sight distance for right-turn ramps on the left within an interchange. The methodology encompasses several key steps: Firstly, utilizing UAV footage, this study captures and analyzes vehicle operational videos within the diversion area of two left-placed right-turn ramps. It delves into examining vehicle operating velocities, headway distances, and microscopic lane-change trajectory data. Secondly, a comprehensive driver behavior simulation test is conducted to explore various aspects such as vehicle speed fluctuations, visual characteristics, and psychological reactions within the right-turn ramp on the left section. Subsequently, the findings from the collected empirical data are applied to parameterize a decision sight distance calculation model, resulting in the recommended values for the decision sight distance in the context of the right-turn ramp on the left at freeway interchanges. This study presents the following key findings:  
Utilizing UAV aerial photography combined with the YOLOv3 target detection algorithm, Kalman filtering, and the Frenet coordinate transformation method, microscopic lane-changing trajectory data is obtained. This data analysis sheds light on the travel speed, headway, and lane-changing behaviors within the left exit section of the right-turn ramp. Employing the modified hyperbolic tangent function lane-change trajectory model, Python calculations yield a 97.18% goodness of fit for the right lane change, affirming the accuracy of this model in depicting vehicle driving characteristics. Furthermore, the vehicle's headway adheres to the 2-stage Erlang distribution, determining the waiting time. Simulated driving tests conducted using UC-win/Road simulation software, Forum8 driving simulation platform, and SMI ETGTM spectacle-type eye-tracking device analyze driver accuracy, workload, and traffic and visual behavior characteristics within the left-handed section of the right-turn on-ramp at the interchange. A formulated model for calculating the decision sight distance within the left-placed right-turn ramp section of the interchange is presented. This model segments the decision distance, queuing distance, and execution lane-changing distance, elucidating the underlying principles for calculating the decision sight distance. Sequential calibration of the relevant model parameters based on the measured data and driver behavior simulation experiments yields recommended decision sight distance values corresponding to different mainline design velocities. Figure 15: The comparison of recommended value and standard value for decision sight distance. * Chinese specified sight distance values appear smaller than the recommended value, raising concerns regarding the potential insufficiency of the decision sight distance when adopting the Route Specification's special value due to the distinctive nature of the right-turn ramp left placement. Due to space constraints, this paper primarily investigates traffic data at design velocities of 120 km/h and 100 km/h, limiting the investigation area and sample. Future research directions involve expanding the dataset to include 80 km/h traffic data, augmenting the sample sizes, and further exploring the traffic characteristics of trucks. Conceptualization, Z.F. and J.Z. ; methodology, J.Z. and B.P. ; validation, S.W. and B.P. ; formal analysis, S.W. ; investigation, H.Y. and J.Z. ; resources, B.P. and J.Z. ; data curation, S.W. and H.Y. ; writing--original draft preparation, Z.F. ; writing--review and editing, B.P. and J.Z. ; visualization, H.Y. ; supervision, B.P. ; project administration, S.W. ; funding acquisition, B.P. All authors have read and agreed to the published version of the manuscript. This research was funded by the Scientific Research Program of CCCC First Highway Consultants Co., Ltd. (Study on Highway Parking Sight Distance No. KCJJ2022-23). Not applicable.