Despite substantial progress in visual perception, object detection systems for autonomous driving still exhibit pronounced performance degradation in nighttime and low-light conditions, where reduced signal-to-noise ratio, blurred object boundaries, and scale ambiguity challenge reliable recognition. Existing YOLO-based detectors, primarily optimized for daytime imagery, struggle to maintain robustness under such adverse illumination. To address these issues, we propose YOLO-Night, a nighttime-oriented object detection framework that enhances the YOLO11 architecture through a structured integration of contrast enhancement, adaptive receptive field modeling, and multi-scale feature fusion. The framework incorporates a feature-level enhancement mechanism to improve low-contrast representations, employs depthwise switchable atrous convolution to dynamically adapt receptive fields for blurred and small objects, and introduces a multi-scale convolutional block to strengthen feature extraction under severe illumination degradation. In addition, a staged feature fusion strategy with an auxiliary low-level detection head was adopted to mitigate semantic misalignment across feature scales. Extensive experiments on the NightCity dataset demonstrated that YOLO-Night consistently outperformed the YOLO11n baseline, achieving improvements of +14.3% precision, +12.4% recall, and +10.4% mAP@50 under nighttime conditions while maintaining real-time inference capability. These results indicate that targeted architectural adaptations can substantially improve object detection robustness in low-light driving scenarios.

Determination of the triggering conditions for automobile airbags is a complex problem because vehicles often run under transient conditions due to the dynamic influence from varying road conditions. The research into malfunctions of automobile airbags is an important and challenging topic. In this work, the peak acceleration and moving window integration algorithms were used to obtain the high-risk abuse items from a test matrix that consisted of 111 items which were under multiple road environment excitation, including condition categories for typical roads, curb striking, potholes and drains, and roadblocks. A novel multi-channel data reduction method and improved clustering method based on abuse probability sorting were proposed, and based on the specific characteristic constraints for the automobile airbag malfunction test results, by construction of a false triggering probability model and discussion of the weight factors of the two algorithms, importance rankings for the high-risk false action items were realized based on the relative probability. Finally, 53 high-risk abuse items were identified from a large set of 111 test items through 10-channel acceleration sensors. After analysis and comparison of the test condition details, conclusions of high-risk items are drawn with regard to the vehicle-road excitation that affects airbag abuse. The consistency of the identification results with the Chinese national standard verifies the accuracy and effectiveness of the proposed method.

With the rapid growth of the global economy, transportation safety challenges have become increasingly prominent, resulting in substantial losses to life and property. Electric bikes (E-bikes) exhibit relatively high speeds and poor dynamic stability, leading to elevated driving risks, making accurate real-time E-bike detection a critical requirement for road safety and intelligent driving systems. Despite the unique significance of dedicated E-bike detection in Intelligent Transportation Systems (ITS), research on E-bike detection as a distinct category remains limited, compounded by a lack of open, complex-environment E-bike datasets. To address these gaps, we design a novel YOLO-DC model to improve the accuracy and real-time performance of E-bike detection in complex traffic environments. We enhance the YOLOv8s model with three targeted modifications: integrating Deformable Convolutional Network-v2 (DCNv2) into the backbone for improved spatial adaptability, embedding the Convolutional Block Attention Module (CBAM) in the neck for enhanced spatial-channel feature representation, and optimizing bounding box regression with a novel Hybrid Intersection over Union (HIoU) loss for small-object detection. To mitigate the scarcity of open E-bike data, we create the Electric Bikes Dataset (EBD), a curated dataset of over 1257 real-road images capturing E-bikes under diverse conditions (low light, occlusion, bad weather, tilted perspectives). The YOLO-DC model is validated on the EBD dataset and in real-world traffic scenarios, achieving a 12.4% improvement in mAP50 (mean Average Precision at IoU = 0.5) and an overall detection accuracy of 98.4% compared to the baseline YOLOv8s model. The YOLO-DC model delivers high real-time performance for E-bike detection and provides a scalable foundation for tracking in subsequent systems, with significant potential to advance intelligent vehicle perception systems and enhance ITS road safety capabilities.