Open-Vocabulary Object Detection with Driving-Aware Multi-Scale Feature Fusion for Autonomous Driving

Open-vocabulary object detection (OVD) is crucial for handling dynamic real-world driving scenarios. Inspired by YOLO-World, we propose OpenVocab-Auto, an open-vocabulary object detection framework with driving-aware multi-scale feature fusion for autonomous driving scenarios. Our system introduces three key innovations: (1) a context-adaptive prompt engine that significantly reduces computational overhead compared to global prompt strategies, (2) hierarchical vision-language alignment for improved small object detection, and (3) real-time optimization achieving 27 FPS on NVIDIA Jetson AGX Orin through TensorRT acceleration. On RTX 3080 (FP16 full model), the framework achieves 0.923 F1 for parking space detection and 0.847 F1 for zero-shot obstacle recognition. On Jetson Orin (TensorRT INT8 model), the corresponding scores are 0.811 and 0.333, respectively, under the same evaluation protocol.