ABSTRACT During autonomous driving, accurate detection of vehicles and pedestrians is important. Under foggy conditions, vehicle and pedestrian targets in visual sensor images often appear blurred, occluded, or with reduced contrast, which significantly hinders feature extraction. To address these challenges, in this paper, based on YOLO11, namely, DB‐YOLO, a vehicle and pedestrian detection algorithm for foggy scenes, is proposed. First, in accordance with the atmospheric scattering theory of imaging under foggy conditions, a dual‐backbone feature extraction structure is designed to implicitly model the degradation mechanism of foggy images, fuse high‐level and low‐level features, and gradually increase the receptive field, improving the representation ability of the model for foggy images. Second, a C3K2_iAFF module is introduced. This module employs an attention‐based data fusion approach to achieve more efficient feature fusion by suppressing irrelevant features that are polluted by haze while improving useful features that are related to the target. Additionally, an efficient upconvolution block (EUCB) upsampling method is used in the neck network to further suppress haze noise and improve key features. The experimental results reveal that DB‐YOLO achieved mAP50 values of 56.6% and 40% on two public foggy datasets, representing improvements of 8.1% and 2.5%, respectively, over the baseline model. The proposed approach has better detection performance than multiple state‐of‐the‐art (SOTA) models do. This study provides effective technical support for increasing the perceptual robustness of autonomous driving systems under adverse weather conditions.

Predicting future trajectories of surrounding agents is essential for safety-critical autonomous driving. Most existing work focuses on predicting marginal trajectories for each agent independently. However, it has rarely been explored in predicting joint trajectories for interactive agents. In this work, we propose Bi-level Future Fusion (BiFF) to explicitly capture future interactions between interactive agents. Concretely, BiFF fuses the high-level future intentions followed by low-level future behaviors. Then the polyline-based coordinate is specifically designed for multi-agent prediction to ensure data efficiency, frame robustness, and prediction accuracy. Experiments show that BiFF achieves state-of-the-art performance on the interactive prediction benchmark of Waymo Open Motion Dataset.