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The algorithm first employs a CNN to extract and identify features from image data of driving scenes. These features are then input into the DDPG model, which performs decision-making and control through an actor\u2013critic network architecture. To address the significant training fluctuations commonly associated with DDPG, a synthetic experience replay mechanism is introduced. Additionally, a more rational reward function is designed to overcome challenges in defining appropriate reward values. The algorithm\u2019s effectiveness and reliability in autonomous driving have been validated through both simulation and real-world driving experiments. The results demonstrate that the proposed CNN-DDPG-based algorithm achieves high obstacle recognition accuracy. Although recognition performance slightly declines as driving speed increases, the algorithm maintains stable overall performance and surpasses other baseline methods. 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