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.

In the early stages of the development of autonomous driving technology, autonomous vehicles (AVs) and manually driven vehicles (MVs) will both be present on the roads, and the interaction of AVs and MVs will affect driving safety. This study aims to evaluate driving safety for car-following scenarios involving AVs and MVs on urban roads and to identify the driving safety vulnerability sections for each road. In this study, the AV behavior control algorithm and the urban interrupted road were implemented using SCANeR ^TM , and longitudinal, lateral, and inter-vehicle driving safety indicators were derived. As a result of the analysis, the driving safety of the AV-AV pair was the highest in all safety indicators, and the mixed pair (AV-MV) was safer than the MV-MV pair. The driving safety evaluation index for each analysis section was analyzed by the rate of change. As a result of analysis of variance, the null hypothesis that the section was the same in all mixed pairs of evaluation indicators was rejected. Post hoc analysis shows that the section with the greatest difference from the straight line was selected as a vulnerable area. As a result of the post hoc analysis, the non-signal intersection was analyzed as the most vulnerable area in the case of the mixed pair. Using this, it is possible to select a driving safety vulnerability section when AVs and MVs are mixed on actual urban roads.

Vehicle interactions with different driving behaviors in mixed traffic conditions, in which autonomous vehicles (AVs) and manual vehicles (MVs) coexist, would result in unstable traffic flow leading to a potential crash risk. A proactive traffic management strategy is required to enhance both safety and mobility by preventing hazardous events in connected environments. The purpose of this study is to develop a Proactive Lane‐changE Assistant Strategy for Automated iNnovative Transportation (PLEASANT) to enhance traffic safety. PLEASANT is a strategy for providing lane change assistance information to vehicles approaching risky situations such as crashes, broken vehicles, and upcoming hazardous obstacles. In addition, this study proposed a comprehensive simulation framework that incorporates driving simulation and traffic simulation to evaluate the performance of PLEASANT when dealing with mixed traffic. To characterize vehicle interactions between AVs and MVs, this study analyzes driving behavior in mixed car‐following situations based on multiagent driving simulation (MADS), which is able to synchronize the space and time domains on the road by connecting two driving simulators. The characteristics of vehicle interactions between AVs and MVs were incorporated into microscopic traffic simulations. The effectiveness of PLEASANT was evaluated based on the crash potential index from the perspective of safety. The results showed that PLEASANT was capable of enhancing traffic safety by approximately 21%. PLEASANT is expected to be useful as a novel management strategy for enhancing traffic safety in mixed‐traffic environments.