In this paper, we study the global traffic density and emergent traffic behavior of several hundreds of intelligent vehicles, as a function of V2V communication (for the ego vehicle to perceive traffic) and path-finding heuristics (for the ego vehicle to reach its destination), in urban environments. Ideal/realistic/no V2V communication modes are crossed with straight-line/towards-most-crowded/towards-least-crowded pathfinding heuristics to measure the average trip speed of each vehicle. The behaviours of intelligent vehicles are modelled by a finite state automaton. The V2V communication model is also built based on signal propagation models in an intersection scenario and a Markov-chain based MAC model. Our experiments in simulation over up to 400 vehicles exhibit attractive insights: 1) communication's impact is positive for the performance of the emergent vehicles' behaviour, however, 2) the path-finding heuristics may not obtain their expected collective behaviour due to the communications errors in realistic road environment.