Reducing carbon emission to improving the economy of fuel vehicle is one of the effective ways to achieve Carbon Neutrality. The Internet of Vehicles (IoV) is a developing technology for deep integration of the Internet of Things (IoT) and transportation. The Industrial Internet of Things (IIoT) can incorporate vehicle information to pinpoint vehicle carbon emissions and provide a foundation for the subsequent carbon-neutral decision-making process. To achieve the precision needs of IoT, however, more than conventional Global Navigation Satellite Systems (GNSS) are required. To achieve carbon emission detection, provide high-precision positioning, and provide a foundation for subsequent carbon-neutral decision-making, it is essential to design a carbon emission detection and positioning system with the capability of vehicle networking. The geographic proximity of edge Ultra Wide Band (UWB) nodes and the merging of various data sources are two methods we suggest employing in this study to increase location accuracy in IIoT situations. After carefully examining the positioning error of the single-edge node and the range error achieved in the UWB communication system, we choose a suitable filtering strategy to enhance single-node accuracy. Following the improvement of single-node accuracy, we fuse the location information of multiple edge nodes using a Weighted Least Squares algorithm in the spatial dimension; in the temporal dimension, we use Extended Kalman filtering to fuse the data over a period of time due to the temporal correlation of inter-node communication. Experimental results demonstrate that our co-localization method, which combines temporal and spatial information, achieves higher localization accuracy in comparison with previous work.

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. STATVTPred achieves an AMR of 73.07% on the Beijing dataset, surpassing the Transformer by 6.38% and the LSTM Encoder-Decoder by 37.45%, while also reducing Distance Error (DE) by 26.93% and 20.95% in Beijing and Chengdu, respectively, also much lower than the baseline results. 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.