Autonomous driving navigates the complex environment safely through a suitable object detection process. Since the existing object detection systems are facing challenges in achieving the required speed and accuracy for the real-time applications, the existing models focus on either the high accuracy or the fast-processing times, which creates a gap in realistic solutions for the autonomous vehicles. This paper presents a new hybrid model that combines Cross-Stage Partially Deformable Network (CSPDNet) with the Single Shot MultiBox Detector (SSD) to overcome these issues and help the models to maintain the detection accuracy and processing speed. The CSPDNet improves the feature extraction and SSD framework with quick object localization and classification. The proposed method shows an improved performance over the traditional approaches. The proposed work has achieved improved precision and recall metrics on the benchmark datasets. The experimental results shows that the CSPDNet-SSD hybrid model has effectively balanced the trade-offs between speed and accuracy. This research has advanced the object detection techniques and provides a strong solution to enhance the reliability and safety of the autonomous vehicles in dynamic driving conditions.