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.

Advanced Driver Assistance Systems (ADAS) can exhibit perception limitations involving large or stationary vehicles, occluded downstream hazards, and the finite detection range of onboard radar. This study proposes a microwave signal conversion based support framework comprising a vehicle-mounted V2V–V2V system and a roadside I2I–I2V system. The algorithms convert and retransmit radar signals according to vehicle speed and spacing states or downstream congestion, while longitudinal responses were behaviorally represented in VISSIM-COM through conditional Desired Speed modification for equipped trucks and buses. A calibrated 12-km Seoul Tollgate corridor was evaluated using V2V–V2V market penetration rates of 0–100%, a binary I2I–I2V condition, and a combined full-deployment scenario. Run-level TTC and conflict frequency were summarized using means, standard deviations, and 95% confidence intervals, and both measures were compared using two-sided paired t-tests. Increasing V2V–V2V penetration was associated with higher TTC and fewer conflicts. Within the tollgate influence area, combined deployment increased mean TTC from 0.72 to 1.27 s and reduced mean conflict frequency from 1486 to 764; both paired comparisons were statistically significant (p < 0.001). These findings suggest potential surrogate-safety benefits from coordinating vehicle- and infrastructure-based recognition support and advance speed adjustment.

Autonomous vehicles (AVs) rely on multiple sensing technologies, including cameras, Light Detection and Ranging (LiDAR), and radar, to perceive their surroundings and support automated driving func...