Pedestrian detection is a key component of vision-based systems in autonomous driving, critical for ensuring traffic safety. Recent research has shown that adversarial examples can mislead these systems, highlighting the need to evaluate and improve their robustness. However, most prior work assumes that adversarial examples can be clearly captured, overlooking a common real-world scenario: adversarial examples can be blurred by vehicle motion. This motion blur, frequently encountered in dynamic driving scenarios, can obscure adversarial examples and reduce the effectiveness of attacks. To address this issue, this paper proposes an adversarial example generation method based on an image motion blur model for driving scenarios. First, we develop the model using vehicle kinematics, revealing the non-uniform distribution of motion blur parameters for pedestrians at different positions. Based on this, we propose a motion blur model-guided adversarial example generation method that adapts to both the driving scenario and the pedestrian’s position, ensuring that adversarial examples remain effective despite motion blur. Finally, we validate the proposed method through real-world vehicle experiments, demonstrating a 19.67% improvement in the average attack success rate across various vehicle speeds compared to existing methods. This method provides a practical tool for testing pedestrian detection robustness in autonomous driving.