# Effects of Vehicular Communication on Risk Assessment in Automated Driving Vehicles
## 1 Introduction  
With a significant development in sensors, actuators and other technologies related to vehicle-to-vehicle (V2V) communication technologies, numerous developments in safety systems have been achieved during the last decade. Intelligent systems based on on-board perception/detection devices have contributed to improve road safety [1]. The next step in the development of vehicle safety technology points toward vehicle-to-vehicle (V2V) communications to obtain more extensive and reliable information about vehicles in the surrounding area, representing cooperative intelligent transportation systems (C-ITS). Research projects have been conducted throughout the world to define the requirements for an appropriate vehicular communication system and its possible applications [2]. While the in-vehicle sensors can enable a limited level of safety functionalities without the use of inter-vehicle communication, the benefits that V2X communication can bring are widely known by many automobile manufacturers, and they are the result of collaborations with the mobile industry. Toyota and Lexus have commercialized vehicles equipped with V2X communication technology from 2015 on the road in Japan. This technology provides drivers with useful and detailed surrounding vehicle and traffic signal information. In addition, they aim to deploy the technology with Dedicated Short-Range Communications (DSRC) systems on vehicles sold in the United States starting in 2021 [3]. Audi, Ford and Qualcomm announced cellular-based vehicular communication technology (C-V2X) operating across vehicles from different manufacturers. A demonstration has been performed to show the effectiveness of using C-V2X technology on the ITS spectrum in order to avoid vehicle-to-vehicle collision and improved road safety [4]. Effects of V2X communication on the automated vehicle safety have been investigated through information fusion and risk assessment. [PERSON] et al. [5] proposed a human-centered risk assessment algorithm using V2V communication in order to determine a proper intervention timing of active safety system. A host vehicle reduces the chances of perception error and uncertainty during a sudden deceleration due to the fact that V2V communication allows a host vehicle to receive the internal sensor information of a remote vehicle and, to compute more accurate prediction. It overcomes the estimation error of a convectional radar-based advanced driver assistant system (ADAS). For fully automated driving, however, a drive mode decision function with guaranteed safety has to be integrated to threat assessment algorithm. For a safety-critical cyber-physical system of an automated electric vehicle, in [6], an artificial-neural-network (ANN)-based estimation method is announced for an accurate observation of the brake pressure of the vehicle. The ANN-based machine learning framework is used to quantitatively estimate the brake pressure of the vehicle, and it has great potential to achieve a sensorless design of the braking control system. The guaranteed safety is also crucial for a lane change decision in fully automated driving. Previous researches are analyzed a pattern of lane change of human driver data to determine lane change timing [7]. The learning-based approach for lane change using neural network to predict the maneuver trajectory [8], and a fuzzy logic for solving the modeling lane change decision making problems [9] were proposed. Since lane changing for an automated driving system requires predicting traffic by the host vehicle, a probabilistic threat assessment approach was proposed by successfully implementing a stopping sight distance [5]. To decide on the active safety control intervention moment, collision risk and human reaction time, which is inspired by stopping sight distance, are determined. Active safety systems of automated vehicle have become more intelligent incorporating human-sense, and this enhances driver acceptance.