Abstract:  Aiming at the high real-time network transmission application scenarios required in the remote driving field, an adaptive BBRv2 congestion control algorithm is proposed, which aims to optimize the fairness of the BBRv2 congestion control algorithm in sharing bottleneck links between different RTT flows in the remote driving network, make its working point approach the optimal working point, and reduce the high transmission delay caused by deviation from the optimal work point. The proposed algorithm improves the competitiveness of shorter RTT flows by adding a factor dynamic with RTT as the minus function, and improves the response sensitivity of longer RTT flows and shorter RTT flows by setting the queuing delay threshold to achieve relatively fair bandwidth allocation and low delay transmission. The effectiveness of the adaptive BBRV2 congestion control algorithm is verified through the Network Simulator (NS3) platform. The results show that, compared with BBRv2, the adaptive BBRv2 algorithm under the depth buffer of the remote driving network improves the fairness by 39.4% and significantly reduces the delay.