Autonomous vehicles need fast, reliable perception to detect vehicles, pedestrians, cyclists, traffic signs, and other road objects in real time. This paper surveys camera-based one-stage object detectors for autonomous driving. It traces 24 one-stage detectors, from YOLOv1 (2016) through SSD, RetinaNet, anchor-free designs such as FCOS and CenterNet, and efficiency-oriented models such as EfficientDet and RTMDet, to YOLOv12 (2025), with the real-time transformer RT-DETR as an end-to-end reference point. The detectors are compared by design choices, feature fusion, loss functions, and deployment trade-offs. Reported results are synthesized on general benchmarks (COCO, PASCAL VOC) and driving datasets (Waymo Open, KITTI, BDD100K, Argoverse-HD). The driving-dataset evidence, drawn mainly from single studies, suggests that accuracy and speed margins measured on general benchmarks may not carry over to road scenes. At high-input resolution on Waymo, ResNet-based one-stage detectors were not faster than Faster R-CNN, and small-object AP stayed at or below 10.3 for every one-stage model tested. Streaming evaluation on Argoverse-HD shows that accuracy drops once processing latency is taken into account. The survey also summarizes datasets, metrics, open challenges, and future directions, and argues that detectors should be evaluated on driving-specific data, under adverse conditions, and on target hardware before they are considered deployment-ready.