Human drivers have excellent perception and reaction abilities in complex environments such as dangerous highways, busy intersections, and harsh weather conditions. To achieve human-level driving performance, autonomous driving systems require powerful environmental perception systems and the ability to make accurate decisions in difficult situations, enabling vehicles to maintain smooth driving. However, making decisions based on visual perception is still a daunting challenge for autonomous vehicles, also current end-to-end networks and modular frameworks have limitations in effectively addressing perception, decision-making, and control, such as a lack of interpretability and weak generalization ability in complex environments. This paper proposes an elaborate modular pipeline for autonomous driving that effectively integrates semantic perception information, multi-level decision tasks, and control modules. The decision-making module comprehensively considers high-level maneuver selection and low-level motion control in both horizontal and vertical directions. The proposed MP framework is trained end-to-end by a novel hierarchical reinforcement learning method with a new action sampling mechanism. To adapt to the experimental scenarios, we use the CARLA simulation platform to collect data to evaluate the proposed autonomous driving framework and its new training method, considering various environmental factors such as sunny, dusk, and rainy weather. The results show that the framework exhibits smooth and effective driving strategies in different environments and can converge quickly and stably. The method improves learning efficiency and reduces unnecessary coupling and error propagation. Overall, the proposed framework and training method provide new ideas for improving existing autonomous driving systems.

Computer-aided automatic segmentation of retinal blood vessels plays an important role in the diagnosis of diseases such as diabetes, glaucoma, and macular degeneration. In this paper, we propose a multi-scale feature fusion retinal vessel segmentation model based on U-Net, named MSFFU-Net. The model introduces the inception structure into the multi-scale feature extraction encoder part, and the max-pooling index is applied during the upsampling process in the feature fusion decoder of an improved network. The skip layer connection is used to transfer each set of feature maps generated on the encoder path to the corresponding feature maps on the decoder path. Moreover, a cost-sensitive loss function based on the Dice coefficient and cross-entropy is designed. Four transformations—rotating, mirroring, shifting and cropping—are used as data augmentation strategies, and the CLAHE algorithm is applied to image preprocessing. The proposed framework is tested and trained on DRIVE and STARE, and sensitivity (Sen), specificity (Spe), accuracy (Acc), and area under curve (AUC) are adopted as the evaluation metrics. Detailed comparisons with U-Net model, at last, it verifies the effectiveness and robustness of the proposed model. The Sen of 0.7762 and 0.7721, Spe of 0.9835 and 0.9885, Acc of 0.9694 and 0.9537 and AUC value of 0.9790 and 0.9680 were achieved on DRIVE and STARE databases, respectively. Results are also compared to other state-of-the-art methods, demonstrating that the performance of the proposed method is superior to that of other methods and showing its competitive results.

Lane change and crash risk prediction are critical technologies for autonomous driving. An attention-based LSTM model is proposed in this paper for lane change behavior prediction in highways, considering both the surrounding vehicles’ information around the target vehicle at the current time and the historical trajectory data of the vehicle, and it shows higher accuracy and better interpretability than other models. There are two sections in this prediction model, including a pre-judgment model based on the C4.5 decision tree and bagging ensemble learning, and a multi-step lane change prediction model of LSTM with attention mechanism. In addition, the model is applied in the NGSIM datasets to test practicability and accuracy, and the precision of left lane change and right lane change is 98% and 94% respectively 1 second before the vehicle changes lane. Finally, to judge the safety of vehicles in the driving process, a crash risk prediction model based on Time-To-Collision is proposed, which further verifies the effectiveness of the method.

Link instability induced by users’ mobility is one of the challenges of optical wireless communications (OWC) inherited from the propagation nature of light. Hence, good understanding of the optical channel characteristics in dynamic environments plays a vital role in developing robust resource management strategies in OWC networks. Unfortunately, it is quite difficult to collect accurate indoor optical channel data in dynamic environments. In addition, indoor trajectory dataset is not publicly available. To overcome such limitations, this paper proposes a mobile terminal-centric analytical framework that captures the propagation channel characteristics in a mobile OWC network whose downlink is based on visible light and uplink is based on infrared light. We abstract the nature of human behavior by integrating both macro and micro mobility patterns. These patterns are then used to realize the spatiotemporal characteristics of optical wireless channels under longterm environment-confined mobility. The statistics derived from the developed framework indicate that the mobile line-of-sight (LOS) channel gain follows space-time-dependent multiple-peak Nakagami distributions, whereas the non-line-of-sight (NLOS) channel gain adheres to various space-time-dependent single peak distributions under different indoor layouts. The overall distribution of NLOS bandwidth follows space-time-dependent multiple-peak log-logistic distributions in downlinks and spacetime-dependent generalized log-logistic distributions in uplinks. Our investigation demonstrates that the indoor layout and the user’s environment-confined mobility pattern significantly impact the LOS dynamics but present limited impact on NLOS components. Motivated by the need for better channel models for mobile OWC, the proposed framework fills up an important gap in literature and help the research community to understand better the indoor optical wireless channel characteristics.

To perform multiplex profiling of single cells and eliminate the risk of potential sample loss caused by centrifugation, we developed a microfluidic flow cytometry and mass spectrometry system (μCytoMS) to evaluate the drug uptake and induced protein expression at the single cell level. It involves a microfluidic chip for the alignment and purification of single cells followed by detection with laser-induced fluorescence (LIF) and inductively coupled plasma mass spectrometry (ICP-MS). Biofunctionalized nanoprobes (BioNPs), conjugating ∼3000 6-FAM-Sgc8 aptamers on a single gold nanoparticle (AuNP) (K_d = 0.23 nM), were engineered to selectively bind with protein tyrosine kinase 7 (PTK7) on target cells. PTK7 expression induced by oxaliplatin (OXA) uptake was assayed with LIF, while ICP-MS measurement of ¹⁹⁵Pt revealed OXA uptake of the drug in individual cells, which provided further in-depth information about the drug in relation to PTK7 expression. At an ultralow flow of ∼0.043 dyn/cm² (20 μL/min), the chip facilitates the extremely fast focusing of BioNPs labeled single cells without the need for centrifugal purification. It ensures multiplex profiling of single cells at a throughput speed of 500 cells/min as compared to 40 cells/min in previous studies. Using a machine learning algorithm to initially profile drug uptake and marker expression in tumor cell lines, μCytoMS was able to perform in situ profiling of the PTK7 response to the OXA at single-cell resolution for tests done on clinical samples from 10 breast cancer patients. It offers great potential for multiplex single-cell phenotypic analysis and clinical diagnosis.