# Effects of Vehicular Communication on Risk Assessment in Automated Driving Vehicles
## 5 Conclusions  
This paper describes a human-centered risk assessment algorithm with two control functions for automated driving vehicles. The proposed algorithm shows effectiveness of vehicle-to-vehicle communication which significantly improves the safety of automated driving vehicle with faster intervention moment of active safety control and drive mode decision. Since a 5G network-based vehicle-to-vehicle communication overcomes two issues of conventional 4G network-based system; a latency and a package deliver rate (PDR). This provides a host vehicle with more accurate remote vehicle information, and allows predicting the motion of the remote vehicle more accurately. To decide proper intervention moment of active safety control and drive mode, a collision risk and two human reaction times are computed. By incorporating two human factors, the risk assessment system of automated vehicle has been extended to fully automated driving, incorporating human factors. The simulation results show that the proposed algorithm with the V2V communication allows a faster drive mode decision and active safety control for application to automated driving. The effects of V2V communication have been investigated through safe triangle analysis and this shows enlarged area of triangle by 13.65%. This results in proper intervention timing of two control functions with more enough time to react hazardous situation. The proper intervention timing improves driver acceptance since the human-centered algorithm considers human reaction time which reflects the capability of most of the human driver while making drivers understand the situation of the autonomous vehicle better than conventional ADAS. This paper considers full speed range of risk assessment method and drive mode decision with the active safety function is also considered. These two functions are computed with two kinds of reaction time from stopping sight distance and decision sight distance.  
In the future, coordination between edge computing and in-vehicle local computing will be implemented and this will be our future research topic. By using 5G-based C-V2X and edge computing, the computation resource is more enriched. In addition, it is expected that real time implementation of particle filter on the test vehicle is possible with the guaranteed performance. For normal driving mode, edge computing-based automated driving is used. When the vehicle encounters emergency situation, the vehicle is controlled by the local computer using vehicle local sensors such as radar and LiDAR and vision. At the same time, vehicle tests will be performed to validate communication performance and the vehicle safety control performance. Communication and sensor information fusion performance is another research topic in the near future  
Conceptualization, D.S. and K.P.; Methodology, D.S.; Software, D.S.; Validation, D.S., K.P. and M.P.; Formal Analysis, K.P.; Investigation, D.S.; Resources, K.P.; Data Curation, D.S.; Writing--Original Draft Preparation, D.S.; Writing--Review and Editing, M.P.; Visualization, D.S.; Supervision, M.P.; Project Administration, D.S.; Funding Acquisition, M.P.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2017-2013-0-00680) supervised by the IITP(Institute for Information &communications Technology Promotion). It was also supported by the 2018 National Research Projects of Naval Research Center, Republic of Korea Naval Academy.  
The authors declare no conflicts of interest.