## 5 Conclusions  
In this paper, we presented the experimental design and performance evaluation of the driving environment perception system based on the fusion of multiple on-board sensors and V2X communications. The two test vehicles used for the driving experiments were each equipped with DSRC equipment and a low-cost GNSS receiver for the exchange of BSM data. The host vehicle was additionally equipped with radar and camera sensors that have already been adopted in production vehicles. The performance of the proposed fusion approach in terms of relative positioning accuracy was evaluated at varying longitudinal and lateral distances between the two test vehicles. In the longitudinal direction, the total RMSE of the relative position estimated with the proposed fusion approach was 0.27 m, whereas those estimated with the camera and radar sensors were 1.91 m and 0.57 m, respectively, which correspond to an improvement of 86% and 52%. In the lateral direction, the total RMSE of the relative position estimated with the proposed method was 0.12 m, while those estimated with the camera and radar were 0.16 m and 0.26 m, respectively, corresponding to an improvement of 27% and 56%. The performance of the trajectory prediction and risk assessment was evaluated in different cut-in driving scenarios where the remote vehicle performs a lane-change maneuver in front of the host vehicle. The test results showed that the proposed method provides accurate target localization and facilitates reliable target trajectory prediction and detection of potential collision during the events when a remote vehicle driving in an adjacent lane cuts in front of the host vehicle. Although the proposed cooperative approach for driving environment perception proved to be successful in the driving scenarios considered in this study, there still exists a number of challenges to be addressed. The scope of this paper has been limited to driving scenarios where a single remote target is present in the surroundings of the host vehicle. As part of future work, the benefits of the cooperative environment perception approach will be further investigated in scenarios that involve multiple surrounding objects for more diverse use cases. In addition, various factors that could adversely affect the accuracy and reliability of on-board sensors (e.g., high-curvature roads and adverse weather conditions) and V2X communications (e.g., large separations between vehicles, urban environments) will be examined so that the data quality levels that can be expected for each sensor track and for V2X communications can be more accurately determined in continuously changing driving conditions. It is also important to note that the trajectory prediction performed in the scope of this study was based on the physics-based motion model, which performs well for short-term prediction but degrades in its performance when the prediction horizon is extended. Furthermore, this trajectory prediction approach cannot anticipate the state changes caused by varying road curvature, traffic signals, or future driving maneuver execution. Therefore, future work should investigate interaction-aware motion models and also incorporate the map data and the signal phase and timing message, which can be acquired via V2X communications, for accurate and reliable longer-term trajectory prediction that takes into account inter-vehicle interaction, road configuration, and traffic signal dependencies. Conceptualization, M.B. and S.L. ; methodology, M.B. ; software, M.B., J.M., and W.K. ; validation, M.B., J.M., and J.Y. ; formal analysis, M.B. ; investigation, M.B., J.M., and D.C.; data curation, M.B., J.M., W.K., and J.Y. ; writing--original draft preparation, M.B. ; writing--review and editing, M.B. ; visualization, M.B. ; supervision, S.L. ; project administration, M.B. and D.C.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.