Fault Diagnosis of the Autonomous Driving Perception System Based on Information Fusion.

The reliability of autonomous driving sensing systems impacts the overall safety of the driving system. However, perception system fault diagnosis is currently a weak area of research, with limited attention and solutions. In this paper, we present an information-fusion-based fault-diagnosis method for autonomous driving perception systems. To begin, we built an autonomous driving simulation scenario using PreScan software, which collects information from a single millimeter wave (MMW) radar and a single camera sensor. The photos are then identified and labeled via the convolutional neural network (CNN). Then, we fused the sensory inputs from a single MMW radar sensor and a single camera sensor in space and time and mapped the MMW radar points onto the camera image to obtain the region of interest (ROI). Lastly, we developed a method to use information from a single MMW radar to aid in diagnosing defects in a single camera sensor. As the simulation results show, for missing row/column pixel failure, the deviation typically falls between 34.11% and 99.84%, with a response time of 0.02 s to 1.6 s; for pixel shift faults, the deviation range is between 0.32% and 9.92%, with a response time of 0 s to 0.16 s; for target color loss, faults have a deviation range of 0.26% to 2.88% and a response time of 0 s to 0.05 s. These results prove the technology is effective in detecting sensor faults and issuing real-time fault alerts, providing a basis for designing and developing simpler and more user-friendly autonomous driving systems. Furthermore, this method illustrates the principles and methods of information fusion between camera and MMW radar sensors, establishing the foundation for creating more complicated autonomous driving systems.

Status Set Sequential Pattern Mining Considering Time Windows and Periodic Analysis of Patterns.

The traditional sequential pattern mining method is carried out considering the whole time period and often ignores the sequential patterns that only occur in local time windows, as well as possible periodicity. Therefore, in order to overcome the limitations of traditional methods, this paper proposes status set sequential pattern mining with time windows (SSPMTW). In contrast to traditional methods, the item status is considered, and time windows, minimum confidence, minimum coverage, minimum factor set ratios and other constraints are added to mine more valuable rules in local time windows. The periodicity of these rules is also analyzed. According to the proposed method, this paper improves the Apriori algorithm, proposes the TW-Apriori algorithm, and explains the basic idea of the algorithm. Then, the feasibility, validity and efficiency of the proposed method and algorithm are verified by small-scale and large-scale examples. In a large-scale numerical example solution, the influence of various constraints on the mining results is analyzed. Finally, the solution results of SSPM and SSPMTW are compared and analyzed, and it is suggested that SSPMTW can excavate the laws existing in local time windows and analyze the periodicity of the laws, which solves the problem of SSPM ignoring the laws existing in local time windows and overcomes the limitations of traditional sequential pattern mining algorithms. In addition, the rules mined by SSPMTW reduce the entropy of the system.

Inosine enhances tumor mitochondrial respiration by inducing Rag GTPases and nascent protein synthesis under nutrient starvation.

Metabolic heterogeneity of tumor microenvironment (TME) is a hallmark of cancer and a big barrier to cancer treatment. Cancer cells display diverse capacities to utilize alternative carbon sources, including nucleotides, under poor nutrient circumstances. However, whether and how purine, especially inosine, regulates mitochondrial metabolism to buffer nutrient starvation has not been well-defined yet. Here, we identify the induction of 5'-nucleotidase, cytosolic II (NT5C2) gene expression promotes inosine accumulation and maintains cancer cell survival in the nutrient-poor region. Inosine elevation further induces Rag GTPases abundance and mTORC1 signaling pathway by enhancing transcription factor SP1 level in the starved tumor. Besides, inosine supplementary stimulates the synthesis of nascent TCA cycle enzymes, including citrate synthesis (CS) and aconitase 1 (ACO1), to further enhance oxidative phosphorylation of breast cancer cells under glucose starvation, leading to the accumulation of iso-citric acid. Inhibition of the CS activity or knockdown of ACO1 blocks the rescue effect of inosine on cancer survival under starvation. Collectively, our finding highlights the vital signal role of inosine linking mitochondrial respiration and buffering starvation, beyond serving as direct energy carriers or building blocks for genetic code in TME, shedding light on future cancer treatment by targeting inosine metabolism.