Learning Effective Multi-Vehicle Cooperation a Unsignalized Intersection via Bandwidth-Constrained Communication

As artificial intelligence and the internet of vehicles are becoming mature, multi-agent reinforcement learning is utilized as an efficient way to coordinate vehicles to achieve safer and more efficient transportation. Communication between vehicles is essential for multi-vehicle cooperation to enhance the understanding of the environment state and the intentions of other vehicles. However, with limited communication resources, how to compress the message and reduce the number of messages that need to be transmitted while ensuring the coordination performance is an urgent problem to be solved. And evaluating whether the message is useful and identifying the valuable information from the received messages are huge challenges for the vehicle communication system. To this end, we propose an efficient communication method that can guarantee coordination performance with limited communication resources. In particular, the efficient communication method using the algorithm in variational auto-encoder to compress the message while guaranteeing the valuable information of the message is preserved in the compressed message. Additionally, the multi-head attention mechanism is utilized to extract valuable information from the received messages and help the vehicle to make the driving decision. To avoid contention of communication resources, the message will be scored before transmitted to other vehicles. So that communication resources can be reserved for valuable messages. The efficient communication method is evaluated in an unsignalized intersections scenario. Experimental results show that the efficient communication method achieves better performance under the bandwidth-constrained environment than the baselines.