Spatial Temporal Attention based Target Vehicle Trajectory Prediction for Internet of Vehicles

Forecasting vehicle behavior within complex traffic environments is pivotal within Intelligent Transportation Systems (ITS). Though this technology plays a significant role in alleviating the prevalent operational difficulties in logistics and transportation systems, the precise prediction of vehicle trajectories still poses a substantial challenge. To address this, our study introduces the Spatio Temporal Attention-based methodology for Target Vehicle Trajectory Prediction (STATVTPred). This approach integrates Global Positioning System(GPS) localization technology to track target movement and dynamically predict the vehicle's future path using comprehensive spatio-temporal trajectory data. We map the vehicle trajectory onto a directed graph, after which spatial attributes are extracted via a Graph Attention Networks(GATs). The Transformer technology is employed to yield temporal features from the sequence. These elements are then amalgamated with local road network structure maps to filter and deliver a smooth trajectory sequence, resulting in precise vehicle trajectory prediction.This study validates our proposed STATVTPred method on T-Drive and Chengdu taxi-trajectory datasets. The experimental results demonstrate that STATVTPred achieves 6.38% and 10.55% higher Average Match Rate (AMR) than the Transformer model on the Beijing and Chengdu datasets, respectively. Compared to the LSTM Encoder-Decoder model, STATVTPred boosts AMR by 37.45% and 36.06% on the same datasets. This is expected to establish STATVTPred as a new approach for handling trajectory prediction of targets in logistics and transportation scenarios, thereby enhancing prediction accuracy.

Edge-guided inverse design of digital metamaterials for ultra-high-capacity on-chip multi-dimensional interconnect

The escalating demands of compute-intensive applications, including artificial intelligence, urgently necessitate the adoption of sophisticated optical on-chip interconnect technologies to overcome critical bottlenecks in scaling future computing systems. This transition requires leveraging the inherent parallelism of wavelength and mode dimensions of light, complemented by high-order modulation formats, to significantly enhance data throughput. Here we experimentally demonstrate a novel synergy of these three dimensions, achieving multi-tens-of-terabits-per-second on-chip interconnects using ultra-broadband, multi-mode digital metamaterials. Employing a highly efficient edge-guided analog-and-digital optimization method, we inversely design foundry-compatible, robust, and multi-port digital metamaterials with an 8xhigher computational efficiency. Using a packaged five-mode multiplexing chip, we demonstrate a single-wavelength interconnect capacity of 1.62 Tbit s-1 and a record-setting multi-dimensional interconnect capacity of 38.2 Tbit s-1 across 5 modes and 88 wavelength channels. A theoretical analysis suggests that further system optimization can enable on-chip interconnects to reach sub-petabit-per-second data transmission rates. This study highlights the transformative potential of optical interconnect technologies to surmount the constraints of electronic links, thus setting the stage for next-generation datacenter and optical compute interconnects.