Research in current autonomous driving domain mainly focused on the problems of perception, decision-making and control based on single autonomous vehicle, but the interactions and games among different vehicles are usually ignored. That makes exiting techniques inapplicable to reduce the accident rate and to improve the traffic efficiency of the transportation system. To solve this problem, a decision-making emergence method is proposed for the large-scale autonomous driving system based on the principle of coevolutionary games. We have established a grid road model and a vehicle kinematics model in which each vehicle interacts by indirect interaction. Benefited from the distributed algorithms and the communication method between vehicles, the computational complexity can be kept linear with the simulated vehicle volume. By designing a multi-objectives reward function, and making the co-evolution process in a simulated environment, the emergence of dominant driving strategies can be observed efficiently. Experimental results showed that the accidents rate and the average computation speed can be greatly improved compared with conventional approach. In details, the accident rate can be reduced by 90% and the average speed can be increased by 30%. The proposed method have great potentials to explore the optimal driving strategy for urban traffic up to millions of autonomous vehicles.