The technology of Connected and Autonomous Vehicles (CAV) is a hot topic of transportation systems, especially regarding platooning and the interaction with other road users. Considering traffic safety, many studies have been devoted to the exchange of information among various road users, such as CAVs and pedestrians. In a platooning scenario, when a pedestrian is detected by a CAV, the leader CAV shares the information with its followers to provide a safe and courteous environment thanks to its connectivity. However, the possibility to improve traffic efficiency while meeting the safety requirements has rarely been addressed in current research. Yet, in industrial areas, where automated vehicles and pedestrians frequently interact, combining safety and efficiency is crucial. The present paper addresses this challenge by first analyzing the intersection of CAVs and pedestrians in no-traffic-signal scenarios. The optimal state is proposed to reduce the time loss. Then, the paper uses a reinforcement learning-based method to make CAVs arrive at the optimal state, to improve traffic efficiency. The experimental results based on virtual reality show that the proposed method increases traffic efficiency while ensuring traffic safety.