## 2 Literature Review  
After reviewing the literature that evaluated the effect of V2V-based in-vehicle warning information, we can categorize the research into three groups based on the type of data. The first group of studies used simulated data from microscopic simulation, where in-vehicle warning information was evaluated based on average speed, acceleration, volume of traffic, etc. For example, some studies examined simulated data to optimize mobility, safety, and the environmental benefits in a CV environment [8; 9]. Another study simulated automated driving vehicles in CV environments. The researchers calculated the traffic volume, travel times, and speeds to evaluate the CVs based on the in-vehicle information [10; 11]. [PERSON] et al. (2017) evaluated the CV environments with heavy vehicles based on intersection movement assistance (IMA), forward collision warning (FCW), and blind spot warning/lane change warning (BSW/LCW) via VISSIM simulation [12]. [PERSON] et al. (2019) evaluated automated braking and lane-keeping assistance scenarios using time exposed time to collision (TET), time integrated time to collision (TIT), time exposed rear-end crash risk index (TERCRI), lane-changing conflicts (LCC), number of critical jerks (NCJ), and the number of conflicts that are reproduced by simulations [13]. These simulation-based studies concluded that CV environments can simultaneously provide positive effects on mobility and safety. Traffic simulation has been utilized to evaluate the effectiveness of warning information not only for driving safety, but also traffic flow performance [14; 15; 16]. [PERSON] et al. (2021) showed that in mixed traffic conditions, CVs improve the efficiency and safety of traffic flow [17]. [PERSON] et al. (2021) analyzed the performance of mobility according to the market penetration rate (MPR) of CVs. The results showed that as the MPR increases in mixed traffic, the average travel time of all traffic decreases and the average speed increases [18]. The second group of studies included those using driving simulators, where in-vehicle warning information was provided using various types of display methods. [PERSON] et al. (2021) analyzed the impact of the CV environment on the tunnel entrance zone. They calculated the speed, standard deviation of the speed, and acceleration and evaluated drivers' speed control in the warning zone [19]. [PERSON] et al. (2022) combined a driving simulator and VISSIM to evaluate the impact of weather conditions (i.e., foggy conditions) [20]. They used TTC, TET, TIT, and a speed-related index as the measure of effectiveness (MOE). [PERSON] et al. (2020) evaluated CV environments using a driver simulator based on spacing and TTC [21]. [PERSON] et al. (2018) simulated the relationship between various types of traffic accidents and TTC in CV environments [22]. [PERSON] et al. (2019) evaluated the safety effectiveness of providing warning information. The analysis used speed standard deviation, TET, and TIT to evaluate the longitudinal safety effect of warning information in the event of fog. The results showed that fog warning systems are beneficial in reducing speed and improving driver behavior [23]. [PERSON] et al. (2021) evaluated the effectiveness of warning information for curved road sections with slippery road conditions for truck drivers in a driving simulation environment. The results showed that warning information decreased off-road crashes by utilizing a kinematic-based surrogate measure of safety (K-SMos) [24]. [PERSON] et al. (2023) analyzed the effectiveness of an audible warning system based on a driving simulator environment to improve bottlenecks in highway construction areas. The analysis showed that warning information can effectively reduce the risk of bottlenecks, but the effectiveness decreases with the delay in providing warning information [25]. In addition, several studies have evaluated the effectiveness of variable speed limit systems (VLSs) in CV environments.