Intelligent vehicles rely on deep learning-based image recognition models for accurate environmental perception, which is critical for driving safety. However, recent research has identified the significant threat from adversarial examples, which can mislead models by adding subtle perturbations to input images, thereby endangering intelligent vehicles. Existing defenses typically use image preprocessing to remove perturbations, however, most works overlook the diversity in adversarial perturbation amplitudes, leading to performance degradation in varying conditions. To address this limitation, this paper investigates the relationship between changes in image spatial frequency and varying adversarial perturbation amplitudes, revealing that increasing perturbation amplitudes lead to heightened high-frequency variations in the spectrum. Based on this finding, we propose an adaptive quantization table-guided image compression method that modifies quantization tables based on the image's frequency spectrum, which enhances the defense performance compared to existing methods that rely on fixed quantization tables. Experimental results demonstrate that our adaptive method effectively responds to the dynamic nature of adversarial examples, significantly outperforming state-of-the-art defenses against common adversarial attacks, such as FGSM, PGD, Deepfool, CW, AutoAttack, and BPDA. The proposed method shows potential for application in intelligent vehicles, as it offers robust defense capabilities across a range of perturbation amplitudes.