The exploration of advanced probes for cancer diagnosis and treatment is of high importance in fundamental research and clinical practice. In comparison with the traditional “always‐on” probes, the emerging activatable probes enjoy advantages in promoted accuracy for tumor theranostics by specifically releasing or activating fluorophores at the targeting sites. The main designing principle for these probes is to incorporate responsive groups that can specifically react with the biomarkers (e. g., enzymes) involved in tumorigenesis and progression, realizing the controlled activation in tumors. In this review, we summarize the latest advances in the molecular design and biomedical application of enzyme‐responsive organic fluorescent probes. Particularly, the fluorophores can be endowed with ability of generating reactive oxygen species (ROS) to afford the photosensitizers, highlighting the potential of these probes in simultaneous tumor imaging and therapy with rational design. We hope that this review could inspire more research interests in the development of tumor‐targeting theranostic probes for advanced biological studies.

In the environment perception stage of autonomous driving, vehicles need to track its surrounding objects quickly and accurately to avoid dangerous behaviors. Therefore, visual object tracking has important practical application value in autonomous driving system. However, the performance of most hierarchical convolutional feature trackers are limited by ignoring the complex environment of autonomous driving. In this paper, a novel Siamese Attention Network to explore the rich spatial and channel information of objects was proposed. Because of the lack of important information between the channel and the spatial position, the tracking performance is reduced by the challenges of illumination change and deformation. The spatial attention block and channel attention block focus on the importance of different spatial positions and channels, respectively. The effective fusion of the two makes our tracker achieve the state-of-the art performance of 0.300 in the EAO criterion of 2017, which exceeds the baseline by 5.7%.

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This article presents a lane-level localization system adaptive to different driving conditions, such as occlusions, complicated road structures, and lane-changing maneuvers. The system uses surround-view cameras, other low-cost sensors, and a lane-level road map which suits for mass deployment. A map-matching localizer is proposed to estimate the probabilistic lateral position. It consists of a sub-map extraction module, a perceptual model, and a matching model. A probabilistic lateral road feature is devised as a sub-map without limitations of road structures. The perceptual model is a deep learning network that processes raw images from surround-view cameras to extract a local probabilistic lateral road feature. Unlike conventional deep-learning-based methods, the perceptual model is trained by auto-generated labels from the lane-level map to reduce manual effort. The matching model computes the correlation between the sub-map and the local probabilistic lateral road feature to output the probabilistic lateral estimation. A particle-filter-based framework is developed to fuse the output of map-matching localizer with the measurements from wheel speed sensors and an inertial measurement unit. Experimental results demonstrate that the proposed system provides the localization results with submeter accuracy in different driving conditions.

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