The in-vehicle warning information provided in the CVs environment allow the driver to respond rapidly to upcoming hazardous situations. The main purpose of this study is to evaluate the safety benefits due to the provision of warning information by analyzing vehicle interactions that are defined as the behavior change of the subject vehicle and the preceding vehicle. A notable feature of this study is the use of a multi-agent driving simulation (MADS) method to analyze the vehicle interaction with various vehicle pairs that are composed of the connected vehicle (CV) capable of receiving warning information and the regular vehicle (RV) that does not receive warning information. A total of four scenarios representing different vehicle pairs, which include CV-RV, RV-CV, CV-CV, and RV-RV, are evaluated in this study. The proposed analysis consists of four parts: the characteristics of subject vehicle maneuvering, variation of relative speed, evasive maneuvering by lane change, and overall crash potential. As an example, the result of analyzing crash potential index (CPI) showed that the greatest safety benefits were obtained with the CV-CV case among aforementioned four vehicle pairs. Approximately 45% of CPI reduction was achievable with the CV-CV case, compared to the RV-RV case. In addition to the CPI, useful findings obtained by investigating safety indicators for each vehicle pair are discussed in terms of safety benefits. The results of this study are expected to be used for both deriving valuable policies and developing more effective in-vehicle warning technologies to fully exploit the benefits of CVs.

The availability of vehicle interaction data, which is obtained by an in-vehicle forward collision warning system, including spacing between the leading and the following vehicle and time-to-collision, provides a valuable opportunity to predict crash risks in real time. When this opportunity is combined with connected vehicle technologies including vehicle-to-vehicle wireless communications, it is expected that more effective crash prevention would be achievable by providing predictive warning information as a part of proactive traffic safety management (PTSM). The purpose of this study is to develop a more reliable in-vehicle warning information provision strategy based on the prediction of crash risks using vehicle interaction data. A crash risk prediction model based on a long short-term memory was able to predict the crash risk after 3 seconds with a mean absolute percentage error of 8% using the data for the past 5 seconds. The predicted crash risk data were applied to derive the optimal threshold for triggering in-vehicle warning information, which is the essence of the proposed warning provision strategy. This study defined three indicators to evaluate the reliability of warning information: correct detection rate (CDR), detection failure rate (DFR), and information provision rate (IPR). An exemplar analysis result showed that the optimal threshold to minimize IPR in a situation where CDR and DFR are 100% and 0%, respectively, was identified as 0.69. The proposed methodology that predicts crash risks in real time and provides V2V-based warning information in a more proactive manner is expected to mitigate the crash risk significantly.

This study derived effective driving behavior indicators to assess the driving safety of autonomous vehicles (AV). A variety of operation design domains (ODD) in urban road networks, which include intersections, illegal parking, bus stop, bicycle lanes, and pedestrian crossings, were taken into consideration in traffic simulation analyses. Both longitudinal and interaction driving indicators were investigated to identify the driving performance of AVs in terms of traffic safety in mixed traffic stream based on simulation experiments. As a result of identifying the appropriate evaluation indicator, time-varying stochastic volatility (VF) headway time was selected as a representative evaluation indicator for left turn and straight through signalized intersections among ODDs related to intersection types. VF headway time is suitable for evaluating driving ability by measuring the variation in driving safety in terms of interaction with the leading vehicle. In addition to ODDs associated with intersection type, U-turns, additional lane segments, illegal parking, bus stops, and merging lane have common characteristics that increase the likelihood of interactions with neighboring vehicles. The VF headway time for these ODDs was derived as driving safety in terms of interaction between vehicles. The results of this study would be valuable in establishing a guideline for driving performance evaluation of AVs. The study found that unsignalized left turns, signalized right turns, and roundabouts had the highest risk scores of 0.554, 0.525, and 0.501, respectively, indicating these as the most vulnerable ODDs for AVs. Additionally, intersection and mid-block crosswalks, as well as bicycle lanes, showed high risk scores due to frequent interactions with pedestrians and cyclists. These areas are particularly risky because they involve unpredictable movements from non-vehicular road users, which require AVs to make rapid adjustments in speed and trajectory. These findings provide a foundation for improving AV algorithms to enhance safety and establishing objective criteria for AV policy-making.

Vehicle-to-vehicle (V2V)-based forward collision warning is a core connected vehicle (CV) service for preventing traffic crashes. Since the driver’s reaction affects the surrounding traffic conditions, an analysis of the effects of the CV application is required in terms of traffic stream performance. This study compared the differences in driving behaviors of CVs depending on whether V2V-based forward collision warning information was provided. The driving characteristics of CVs based on the analysis of probe vehicle data (PVD) were defined, and a methodology was proposed to simulate the driving behaviors of CVs in VISSIM environments. Additionally, the effectiveness of improving mobility and traffic safety by various market penetration rates (MPRs) of CVs in an accident situation was identified by evaluating the average travel speed and time-to-collision (TTC)-based crash risk. The simulation results for the two-lane blocking accident scenario indicated that the average speed increased by 23.18% with an MPR of 100%, and the TTC-based crash risk decreased by 18.34% compared with an MPR of 0%. It has been demonstrated that V2V-based warning information is useful for not only safety benefits but also mobility improvement. The results of this study could be employed as fundamentals to establish various policies to expedite the implementation of CV systems in practice. In addition, it is suggested that the outcome of this study will be useful for developing traffic flow management strategies technology to improve traffic safety.