Abstract: Heavy mining trucks are key equipment in open-pit haulage systems, where the available roadway space is often narrow in relation to the vehicle's size, resulting in high driving difficulty. With the rapid advancement of mining intelligence, autonomous-driving technology has become an essential means of improving production efficiency, ensuring operational safety, and reducing operating costs. As a core component of autonomous-driving systems, path tracking control plays a decisive role in ensuring stable vehicle motion along a reference path. However, heavy-duty mining trucks exhibit pronounced steering-mechanism constraints and significant signal transmission delays. Under the combined influence of sharp curves and long delays, the path-tracking system tends to suffer sluggish responses, rapidly increasing tracking errors, and even instability. Existing control methods struggle to simultaneously handle the compound effects of steering constraints and time delay, limiting their engineering applicability. To address the response lag caused by front-wheel steering-rate constraints in sharp-curve environments, a preview-correct control (PCC) algorithm is developed by introducing the future heading of the reference path as preview information and incorporating the key-point displacement error. The preview component improves steering proactiveness, while the correction component enhances responsiveness to current deviations, enabling stable posture adjustments during curve entry, mid-curve, and exit. PCC does not rely on complex models or high-performance computing platforms, making it suitable for real-time operation on low-power onboard controllers. To handle the widespread presence of signal transmission delay in autonomous-driving systems, a virtual-constraint treatment method is established by analyzing the PCC output structure and the characteristics of the front-wheel steering-rate constraint. On this basis, a multi-step motion–compensation delay compensator is constructed to predict the vehicle’s posture evolution during the delay interval and generate new control inputs that counteract the delay effect. By integrating PCC with the delay compensator, a path-tracking control system capable of simultaneously handling steering-mechanism constraints and long delays is achieved for heavy-duty mining trucks. Simulations were conducted under both unloaded and fully-loaded conditions, followed by full-load field experiments. In unloaded simulations at 20 km/h on a U-shaped curve with a radius of 35 m, PCC achieved a maximum displacement error of 0.1118 m, which is significantly more accurate than P-PID and PID, and close to NMPC, while its average computation time was only 0.2193 ms, far outperforming NMPC in real-time capability. Under fully-loaded conditions with a 0.4 s signal delay, PCC combined with the delay compensator maintained the maximum displacement error within 0.0949 m, whereas uncompensated PCC and NMPC both exhibited error divergence in sharp-curve sections. This demonstrates the critical role of the proposed compensation strategy in ensuring system stability under long-delay conditions. The compensator increased the average computation time by only 0.0498 ms, imposing a negligible impact on real-time performance. Two sets of full-load field tests were conducted with an actual signal delay of approximately 0.4 s, using the same U-shaped curve. The maximum displacement errors were 0.2078 m and 0.1768 m, respectively. In both tests, the vehicle navigated the sharp curve stably without loss of control or noticeable yaw deviations. Overall, simulation and experimental results demonstrate that the proposed control system can maintain stable and reliable path-tracking performance under significant steering-mechanism constraints and long signal delays, achieving a favorable balance among accuracy, real-time capability, and engineering deployability. It is therefore well-suited for practical autonomous-driving applications in heavy-duty mining trucks.