With the expanding development of C-ITS services and their field implementation, our driving experience is now occurring under a partially connected environment. Whether through embedded smartphones or onboard equipment, vehicles are getting connected and regularly emit high-frequency safety messages (CAM in Europe or BSM in the USA) regarding their status. In this paper, as an alternative to the usual methods that rely on expensive dedicated cameras, we explore the potential of passive data resources to feed a slow obstacle detection process performed in near-real time. Contrary to the recent literature focusing on the development of dynamic obstacle detection to expand autonomous skills through expensive dedicated sensors, we adopt the road managers’ perspective. We assume the existence of a monitoring and management center, potentially decentralized to Road-Side Units, collecting the data stream continuously, analyzing it, and enabling it to broadcast safety warning messages to connected vehicles located immediately upstream of the identified slow obstacle. The two-step methodology is based on (i) an automatic lane change detection process followed by (ii) a weighting process and statistical analysis of the space–time scatter plots generated by detected lane changes over a sliding time window. Simulation-based results highlight that despite a low share of connected vehicles, a stationary obstacle can be detected on average at 3 min, while longer delays (5 min) are required when the obstacle is moving between 30 km/h and 50 km/h. Furthermore, disseminating warning messages upstream can improve safety and traffic performance by up to 10 percent for low traffic conditions.