Researchers have published a comprehensive survey of one-stage object detectors for use in autonomous driving. These detectors are crucial for vehicles to quickly and accurately identify objects on the road, such as other cars, pedestrians, and traffic signs. The paper reviews various architectures, including YOLOv1, SSD, RetinaNet, and others, comparing their design choices, feature-fusion strategies, loss functions, and deployment trade-offs. It also discusses commonly used autonomous-driving datasets, evaluation metrics, open challenges, and future research directions. According to the authors, one-stage detectors balance speed, accuracy, efficiency, and robustness but still have a gap between benchmark results and real-world performance. --- Why it matters: This matters because it highlights the current state of one-stage object detectors for autonomous driving and identifies areas that need improvement. Understanding these limitations is crucial for engineers working on perception systems in self-driving cars to develop more reliable and efficient solutions. Source: https://arxiv.org/abs/2608.19014