3D object detection for self-driving cars has emerged as a critical challenge, largely due to the constraints of computing resources and the requirement for real-time processing. For evaluation, we utilize the KITTI benchmark dataset, which is widely used for self-driving cars and 3D object detection research. In this paper, an adaptive Weighted knowledge distillation approach is proposed to enhance detection accuracy while improving computational efficiency. Based on the performance of the teacher model, point clouds are divided into TPS (Teacher Performs Strongly) and TPW (Teacher Performs Weakly). The student model adapts its learning strategy dynamically: for TPS point clouds, it closely imitates the teacher by increasing the distillation weight, whereas for TPW point clouds, it prioritizes learning from raw data, reducing reliance on the teacher’s guidance. Additionally, a data pruning mechanism creates a smaller dataset from the KITTI benchmark based on the teacher model’s performance, while maintaining the TPS-TPW ratio. Experimental results indicate that the student model achieves comparable, and in some cases superior, performance to the teacher model. Specifically, it enhances recall by up to 1.5% and precision by up to 2.2% in complex scenarios. The student model is a lightweight network that learns from the teacher through knowledge distillation [28] and is solely used during execution. This design reduces execution time by nearly 50% while maintaining high detection accuracy. These findings emphasize the effectiveness of the proposed framework in real-time 3D object detection, making it well-suited for deployment in resource-constrained environments such as self-driving cars.