How does scene complexity influence the detection of expected and appropriate objects within the scene? Traffic research has indicated that vulnerable road users (VRUs: pedestrians, bicyclists, and motorcyclists) are sometimes not perceived, despite being expected. Models of scene perception emphasize competition for limited neural resources in early perception, predicting that an object can be missed during quick glances because other objects win the competition to be individuated and consciously perceived. We used pictures of traffic scenes and manipulated complexity by inserting or removing vehicles near a to-be-detected VRU (crowding). The observers' sole task was to detect a VRU in the laterally presented pictures. Strong bias effects occurred, especially when the VRU was crowded by other nearby vehicles: Observers failed to detect the VRU (high miss rates), while making relatively few false alarm errors. Miss rates were as high as 65% for pedestrians. The results indicated that scene context can interfere with the perception of expected objects when scene complexity is high. Because urbanization has greatly increased scene complexity, these results have important implications for public safety.