In this paper, a highway speed planning algorithm for autonomous trucks. A quintic polynomial candidate speed generation algorithm is designed based on a road adaptive sampling strategy. Considering scenarios such as highway cruising, following, stopping, and merging, the algorithm utilizes autonomous traffic flow state information to balance the needs for comfort and safety based on dynamic intervals of different speeds and time headways, forming flexible safety thresholds to ensure smooth deceleration transitions in different areas, thereby enhancing comfort and economy while ensuring safety. Then, taking into account various road scenarios and truck kinematics factors, a cost function model is established to optimize speeds and identify the best driving speed. Simulation and road test results show that the autonomous truck can adaptively follow the front vehicle, verifying the feasibility, effectiveness, and accuracy of the algorithm.