Xiaojun Bi

Xiaojun Bi, Mingjie He, Yiwen Sun

Lane-changing decisions, which are crucial for autonomous vehicle path planning, face practical challenges due to rule-based constraints and limited data. Deep reinforcement learning has become a major research focus due to its advantages in data acquisition and interpretability. However, current models often overlook collaboration, which affects not only impacts overall traffic efficiency but also hinders the vehicle's own normal driving in the long run. To address the aforementioned issue, this paper proposes a method named Mix Q-learning for Lane Changing(MQLC) that integrates a hybrid value Q network, taking into account both collective and individual benefits for the greater good. At the collective level, our method coordinates the individual Q and global Q networks by utilizing global information. This enables agents to effectively balance their individual interests with the collective benefit. At the individual level, we integrated a deep learning-based intent recognition module into our observation and enhanced the decision network. These changes provide agents with richer decision information and more accurate feature extraction for improved lane-changing decisions. This strategy enables the multi-agent system to learn and formulate optimal decision-making strategies effectively. Our MQLC model, through extensive experimental results, impressively outperforms other state-of-the-art multi-agent decision-making methods, achieving significantly safer and faster lane-changing decisions. The code is available at https:github.com/pku-smart-city/source_code/tree/main/MQLC.

Juexiao Zhou, Zhongyi Han, Mankun Xin et al.

Global population aging presents increasing challenges to healthcare systems, with coronary artery disease (CAD) responsible for approximately 17.8 million deaths annually, making it a leading cause of global mortality. As CAD is largely preventable, early detection and proactive management are essential. In this work, we introduce DigitalShadow, an advanced early warning system for CAD, powered by a fine-tuned facial foundation model. The system is pre-trained on 21 million facial images and subsequently fine-tuned into LiveCAD, a specialized CAD risk assessment model trained on 7,004 facial images from 1,751 subjects across four hospitals in China. DigitalShadow functions passively and contactlessly, extracting facial features from live video streams without requiring active user engagement. Integrated with a personalized database, it generates natural language risk reports and individualized health recommendations. With privacy as a core design principle, DigitalShadow supports local deployment to ensure secure handling of user data.

Yu Zhang, Xinchao Wang, Xiaojun Bi et al.

Single-image dehazing is a challenging problem due to its ill-posed nature. Existing methods rely on a suboptimal two-step approach, where an intermediate product like a depth map is estimated, based on which the haze-free image is subsequently generated using an artificial prior formula. In this paper, we propose a light dual-task Neural Network called LDTNet that restores the haze-free image in one shot. We use transmission map estimation as an auxiliary task to assist the main task, haze removal, in feature extraction and to enhance the generalization of the network. In LDTNet, the haze-free image and the transmission map are produced simultaneously. As a result, the artificial prior is reduced to the smallest extent. Extensive experiments demonstrate that our algorithm achieves superior performance against the state-of-the-art methods on both synthetic and real-world images.