MetaBEV: Solving Sensor Failures for BEV Detection and Map Segmentation

Perception systems in modern autonomous driving vehicles typically take inputs from complementary multi-modal sensors, e.g., LiDAR and cameras. However, in real-world applications, sensor corruptions and failures lead to inferior performances, thus compromising autonomous safety. In this paper, we propose a robust framework, called MetaBEV, to address extreme real-world environments involving overall six sensor corruptions and two extreme sensor-missing situations. In MetaBEV, signals from multiple sensors are first processed by modal-specific encoders. Subsequently, a set of dense BEV queries are initialized, termed meta-BEV. These queries are then processed iteratively by a BEV-Evolving decoder, which selectively aggregates deep features from either LiDAR, cameras, or both modalities. The updated BEV representations are further leveraged for multiple 3D prediction tasks. Additionally, we introduce a new M2oE structure to alleviate the performance drop on distinct tasks in multi-task joint learning. Finally, MetaBEV is evaluated on the nuScenes dataset with 3D object detection and BEV map segmentation tasks. Experiments show MetaBEV outperforms prior arts by a large margin on both full and corrupted modalities. For instance, when the LiDAR signal is missing, MetaBEV improves 35.5% detection NDS and 17.7% segmentation mIoU upon the vanilla BEVFusion model; and when the camera signal is absent, MetaBEV still achieves 69.2% NDS and 53.7% mIoU, which is even higher than previous works that perform on full-modalities. Moreover, MetaBEV performs fairly against previous methods in both canonical perception and multi-task learning settings, refreshing state-of-the-art nuScenes BEV map segmentation with 70.4% mIoU.Comment: Project page: https://chongjiange.github.io/metabev.htm

DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving

Safety is the primary priority of autonomous driving. Nevertheless, no published dataset currently supports the direct and explainable safety evaluation for autonomous driving. In this work, we propose DeepAccident, a large-scale dataset generated via a realistic simulator containing diverse accident scenarios that frequently occur in real-world driving. The proposed DeepAccident dataset contains 57K annotated frames and 285K annotated samples, approximately 7 times more than the large-scale nuScenes dataset with 40k annotated samples. In addition, we propose a new task, end-to-end motion and accident prediction, based on the proposed dataset, which can be used to directly evaluate the accident prediction ability for different autonomous driving algorithms. Furthermore, for each scenario, we set four vehicles along with one infrastructure to record data, thus providing diverse viewpoints for accident scenarios and enabling V2X (vehicle-to-everything) research on perception and prediction tasks. Finally, we present a baseline V2X model named V2XFormer that demonstrates superior performance for motion and accident prediction and 3D object detection compared to the single-vehicle model

Synthesis and characterization of polyelectrolytes based on benzotriazole backbone

Suzuki coupling and Ni-catalyzed Yamamoto polymerizations were used to prepare conjugated polyelectrolytes based on n-type benzothiadiazole and benzotriazole unit backbone via a one-step synthesis, which started from their monomers with charged pendant ends. PBTBTz-SO3Na synthesized by Suzuki coupling polymerization showed a high M (n) value of 94 kDa, starting from the bilateral brominated benzotriazole and diboronic ester benzothiadiazole. The PBTBTz-SO3Na revealed aggregation phenomenon at the status of diluted solutions at optical absorption spectroscopy results. Dynamic light scattering (DLS) measurement provided evidence of negative solvatochromic effects of PBTBTz-SO3Na chains in solvents with lower polarity. While, PBTz-PyrBr, PBTz-TMABr, and PBTz-SO3Na which synthesized from Ni-catalyzed Yamamoto polymerizations of bilateral brominated benzotriazole units revealed higher molecular weight with PBTz-PyrBr, M (n) = 112 kDa; PBTz-TMABr, M (n) = 157 kDa; and PBTz-SO3Na, M (n) = 172 kDa, respectively. The ionic pendant groups of monomers have influence on the molecular weight of polymer based on benzotriazole backbone. The optical absorption spectroscopy and DLS measurements presented the negative solvatochromic effects of cationic-CPEs, PBTz-PyrBr, and PBTz-TMABr. Moreover, the UV-vis absorption of PBTz-TMABr displayed higher wavelength of solution (0.0167 mg/mL) than that of film, which was coated from its solution with concentration of 5 mg/mL. These studies displayed insights into the role of pendant ionic functionalities on the molecular weight and optoelectronic properties of the CPEs, which may find application in optoelectronic devices

Niobium doped anatase TiO2 as an effective anode material for sodium-ion batteries

Sodium-ion batteries are considered to be a promising low-cost alternative to common lithium-ion batteries in the areas where specific energy is less critical. Among all the anode materials studied so far, TiO2 is very promising due to its low operating voltage, high capacity, nontoxicity, and low production cost. Herein, we present Nb-doped anatase TiO2 nanoparticles with high capacity, excellent cycling performance, and excellent rate capability. The optimized Nb-doped TiO2 anode delivers high reversible capacities of 177 mA h g-1 at 0.1C and 108.8 mA h g-1 at 5C, in contrast to 150.4 mA h g-1 at 0.1C and only 54.6 mA h g-1 at 5C for the pristine TiO2. The good performance is likely to be associated with enhanced conductivity and lattice expansion due to Nb doping. These results, in combination with its environmental friendliness and cost efficiency, render Nb-doped TiO2 a promising anode material for high-power sodium-ion batteries

Mangiferin reduces uric acid via regulation of amino acid and lipid metabolism

Mangiferin, a functional compound extracted from edible plants, has been shown to exhibit favorable uric acid-lowering properties. However, the underlying molecular mechanisms still require further investigation. In this study, we utilized a rat model of hyperuricemia to assess the hypouricemic effect of mangiferin and explore its potential mechanism based on UHPLC-Q-Exactive Orbitrap mass spectrometry. Untargeted metabolomics analysis revealed 19 differential metabolites significantly altered in serum of hyperuricemic rats through principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), which were primarily related to amino acid metabolism and lipid metabolism. Among these differential metabolites, the levels of alpha-methylstyrene, indan-1-ol, and 4-D-hydroxysphinganine significantly increased, while the levels of L-leucine, 2-phenylacetamide, hippuric acid, benzoic acid, isoquinoline, phenylpyruvate, glycerone phosphate, N, N-diethylphenylacetamide and corticosterone significantly decreased in model rats. After mangiferin intervention, 14 metabolites were reversed through modulation of metabolic pathways. Our findings suggest that mangiferin may serve as a useful potential adjuvant in reducing uric acid effects

Effects of Harvesting Intensity on the Growth of <i>Hydrilla verticillata</i> and Water Quality

The effects of harvesting intensity on the growth of Hydrilla verticillata (L. fil.) Royle as well as water quality were studied in controlled experiments to provide a reference for managing submerged vegetation and purifying the water. The results showed that harvesting had a significant effect on the recovery of shoot growth and H. verticillata height. The harvested group recovered completely or mostly after two harvests, but the recovery time was significantly longer than the control group. The final biomasses of the harvested groups (15%, 30%, 45%, 60%, and 75% harvested) decreased to 66.61%, 49.13%, 43.95%, 43.77%, and 29.94% of the control group, respectively. The greater the harvesting intensity, the fewer the winter buds. Harvesting reduced the number of H. verticillata branches. Repeated harvesting at medium and low intensities during the rapid growth of H. verticillata effectively improved the water quality and inhibited the propagation and growth of phytoplankton. These results show that harvesting controlled the growth of H. verticillata, and that medium and low harvesting intensities were best when considering water quality