Simultaneous localization and mapping (SLAM) is one of the basic requirements of autonomous driving. Furthermore, multisensor fusion, more particularly, the fusion of lidar and camera, is essential for autonomous driving, and understanding how to adjust the weights of different sensors for various scenarios is a critical challenge. Therefore, an adaptive tightly coupled lidar-visual SLAM (AVLS) algorithm is proposed. First, AVLS is built on a factor graph based on sliding windows, including modules such as flexible depth correlation and elastic initialization that improve the accuracy and robustness of the overall algorithm. Second, in order to fully explore the performance of lidars and cameras in different environments, a dynamic weighting scheme based on prior knowledge is adopted. Finally, comprehensive experiments are conducted on the proposed AVLS algorithm on two publicly available large-scale autonomous driving datasets, including comparisons with classical algorithms and ablation experiments. The experimental results show that the robustness and accuracy of the AVLS achieves state-of-the-art performance.