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To address the issue of high miss rates for distant small objects and the diminished system detection performance due to the influence of hazy when autonomous vehicles operate on mountain highways. We propose a framework for small object vehicle detection in hazy traffic environments (SHTDet). This framework aims to enhance small object detection for autonomous driving under hazy conditions on mountainous motorways. Specifically, to restore the clarity of hazy images, we designed an image enhancement (IE), and its parameters are predicted by a convolutional neural network [filter parameter estimation (FPE)]. In addition, to enhance the detection accuracy of small objects, we introduce a cascaded sparse query (CSQ) mechanism, which effectively utilizes high-resolution features while maintaining fast detection speed. We jointly optimize the IE and the detection network (CSQ-FCOS) in an end-to-end manner, ensuring that FPE module can learn a suitable IE. Our proposed SHTDet method is adept at adaptively handling sunny and hazy conditions. Extensive experiments demonstrate the efficacy of the SHTDet method in detecting small objects on hazy sections of mountain highways. [ABSTRACT FROM AUTHOR]