ABSTRACT Over the years, object detection technology based on deep learning has attracted extensive research in autonomous driving. Achieving a robust object detection network under all weather conditions (e.g., sunny, fog, nighttime, rain and snow) is highly significant for autonomous driving systems, which ensure safety by recognising pedestrians, vehicles, traffic lights, etc. This paper proposes a robust multi‐object detection network named All Weather‐You Only Look Once (AW‐YOLO) based on YOLOv8, with a trade‐off between precision and lightweightness. Considering the blurring or absence of the salient object features of the image under all weather conditions, we propose a developed dilation‐wise residual (D‐DWR) module. Specifically, it combines the dilatation‐wise residual module with the dilated re‐param block using a large kernel convolution to see wide without going deep, greatly improving the feature extraction ability. Moreover, we introduce an efficient dynamic upsampler (DySample) that formulates upsampling from the viewpoint of point sampling and avoids dynamic convolution, which can improve the network's ability to feature fusion. Lightweight is an essential requirement for autonomous driving. To this end, we adopt a multi‐scale shared detection head (MSSD‐Head) to achieve lightweight deployment in autonomous vehicles. Experimental results show that the mAP50‐95 values of AW‐YOLO on the KITTI and Adverse Conditions Dataset with Correspondences (ACDC) datasets exceed the baseline model YOLOv8 by 1.7% and 1.5%, respectively. Meanwhile, the parameters and model size of AW‐YOLO have decreased by 21.4% and 20.4%, respectively.

Traffic forecasting has recently attracted increasing interest due to the popularity of online navigation services, ridesharing and smart city projects. Owing to the non-stationary nature of road traffic, forecasting accuracy is fundamentally limited by the lack of contextual information. To address this issue, we propose the Hybrid Spatio-Temporal Graph Convolutional Network (H-STGCN), which is able to "deduce" future travel time by exploiting the data of upcoming traffic volume. Specifically, we propose an algorithm to acquire the upcoming traffic volume from an online navigation engine. Taking advantage of the piecewise-linear flow-density relationship, a novel transformer structure converts the upcoming volume into its equivalent in travel time. We combine this signal with the commonly-utilized travel-time signal, and then apply graph convolution to capture the spatial dependency. Particularly, we construct a compound adjacency matrix which reflects the innate traffic proximity. We conduct extensive experiments on real-world datasets. The results show that H-STGCN remarkably outperforms state-of-the-art methods in various metrics, especially for the prediction of non-recurring congestion.

Traffic speed prediction is a long-standing and critically important topic in the area of Intelligent Transportation Systems (ITS). Recent years have witnessed the encouraging potentials of deep neural networks for real-life applications of various domains. Traffic speed prediction, however, is still in its initial stage without making full use of spatio-temporal traffic information. In light of this, in this paper, we propose a deep learning method with an Error-feedback Recurrent Convolutional Neural Network structure (eRCNN) for continuous traffic speed prediction. By integrating the spatio-temporal traffic speeds of contiguous road segments as an input matrix, eRCNN explicitly leverages the implicit correlations among nearby segments to improve the predictive accuracy. By further introducing separate error feedback neurons to the recurrent layer, eRCNN learns from prediction errors so as to meet predictive challenges rising from abrupt traffic events such as morning peaks and traffic accidents. Extensive experiments on real-life speed data of taxis running on the 2nd and 3rd ring roads of Beijing city demonstrate the strong predictive power of eRCNN in comparison to some state-of-the-art competitors. The necessity of weight pre-training using a transfer learning notion has also been testified. More interestingly, we design a novel influence function based on the deep learning model, and showcase how to leverage it to recognize the congestion sources of the ring roads in Beijing.

A system using feature optimization and neural learning techniques is presented for detection of upright frontal faces. The feature set is optimized in terms of the Fisher's linear discrimination (FLD) criterion. A neural network (NN) method is used to learn a complex mapping function for the classification, given the optimized feature set. The optimization of feature set reduces the burden of the subsequent NN classifier and improves its performance in learning speed and classification rates. Experimental results show that the feature set optimized by using the FLD transform significantly improves the detection rate while maintaining the same false alarms. Our system produces higher detection and lower missing rates than several existing state-of-the-art face detection systems, with an average false detection rate.