Intelligent vehicle cyberphysical systems can integrate real-time traffic scene perception with built-in high-definition maps to construct digital twins of real-world signalized intersections. Based on digital twins, this paper presents a traffic prediction method named cell transformer (cell-trans), comprising vehicle-, cell-, and road-level encoders and a decoder. The vehicle-level encoder first converts vehicle features into vehicle encodings, which the cell-level encoder then fuses with lane segment features to generate cell encodings. Next, the road-level encoder treats the connectivity between lane segments and the phase information at signalized intersections as a dynamic directed graph, extracting spatial-temporal evolution patterns to improve traffic predictions. The cell-trans is compared with baseline models on pNEUMA and CitySim data sets, and the performance comparison validates its optimal predictive accuracy. Moreover, the outstanding performance of the cell-trans is confirmed by ablation study, parameter analysis, and computational efficiency analysis. Finally, this paper develops a cell-trans-based motion planner for autonomous vehicles (AV) in a joint simulation platform combined CARLA and SUMO to indicate its contributions to AVs.

This special issue provides a premier forum for researchers in multimedia big data to share challenges and recent advancements in learning from noisy multimedia data. The multimedia age and its proliferation of devices and platforms is fueling exponential data growth. As computational power and deep learning algorithms rapidly evolve, the web has become a rich source of potential training data for robust machine learning, with search engines such as Google and Bing, Twitter, TikTok, Instagram, and short video sharing platforms offering large-scale data points in the hundreds of millions. The concurrent shift in the Internet to richer web data modalities such as text, audio, image, and video reveal further opportunities to leverage large-scale data for the automatic construction of a variety of datasets for model training and testing. However, the ubiquity of multimedia data means noise is a fundamental challenge, with a label noisea and a domain mismatcha the most critical issues in automatically collected datasets. Learning from noisy multimedia data tends towards poor performance, making it increasingly essential to address these challenges.

The present work reports on the hydrogen gas detection properties of Pt-AlGaN/GaN high electron mobility transistor (HEMT) sensors with recessed gate structure. Devices with gate recess depths from 5 to 15 nm were fabricated using a precision cyclic etching method, examined with AFM, STEM and EDS, and tested towards H 2 response at high temperature. With increasing recess depth, the threshold voltage (VTH) shifted from -1.57 to 1.49 V. A shallow recess (5 nm) resulted in a 1.03 mA increase in signal variation (AIDS), while a deep recess (15 nm) resulted in the highest sensing response (S) of 145.8% towards 300 ppm H 2 as compared to reference sensors without gate recess. Transient measurements demonstrated reversible H 2 response for all tested devices. The response and recovery time towards 250 ppm gradually decreased from 7.3 to 2.5 min and from 29.2 to 8.85 min going from 0 nm to 15 nm recess depth. The power consumption of the sensors reduced with increasing recess depth from 146.6 to 2.95 mW.