With the advancement of edge computing, the computing power that was originally located in the center is deployed closer to the terminal, which directly accelerates the iteration speed of the "sensing-communication-decision-feedback" chain in the complex marine environments, including ship avoidance. The increase in sensor equipment, such as cameras, have also accelerated the speed of ship identification technology based on feature detection in the maritime field. Based on the SSD framework, this article proposes a deep learning model called DP-SSD. By adjusting the size of the detection frame, different feature parameters can be detected. Through actual data learning and testing, it is compatible with Faster RCNN, SSD and other classic algorithms. It was found that the proposed method provided high-quality results in terms of the calculation time, the processed frame rate, and the recognition accuracy. As an important part of future smart ships, this method has theoretical value and an influence on engineering.

The prediction of lane-change behavior is a challenging issue in intelligent and connected vehicles (ICVs), which can help vehicles predict in advance and change lanes safely. In this paper, a novel intelligent approach, which considering both the driving style-based lane-change environment and the driving trajectory-related parameters of the ICV and surrounding vehicles, is proposed to predict the lane-change behaviors for ICVs. By analyzing the characteristics of the lane-change behavior of the vehicle, a modified dataset for the prediction of lane-change behavior was established based on the Next-Generation Simulation (NGSIM) dataset, which is made up of real vehicle trajectories collected by camera. In the proposed approach, the hidden Markov model (HMM)-based model is designed to judge whether the current environment is suitable for lane change according to the driving environment parameters around the vehicle; then according to the driving state of the vehicle, a learning-based prediction-then-judgment model is proposed and designed to realize the prediction of the ICV’s lane-change behavior. Experiments are implemented by using the modified dataset. From the experimental results, the lane-change probability value on the target lane in the truth of the lane-change behavior calculated by the designed HMM-based model is basically above 0.5, indicating that the model can make a more accurate judgment on the surrounding lane-change environment. The proposed learning-based prediction-then-judgment model has an accuracy of 99.32% for the prediction of lane-change behavior, and the accuracy of the lane-change detection algorithm in the model is 99.56%. The experimental results show that the proposed approach has a good performance in the prediction of lane-change behavior, which could effectively assist ICVs to change lanes safely.

The mega-launch of low Earth orbit satellites (LEOs) represents a critical opportunity to integrate navigation and communication (NavCom), but first, challenges related to signal design must be overcome. This article proposes a novel signal scheme named CE-OFDM-PM. Via research on the in-band or adjacent band, it was found that the proposed signal scheme was suitable for S-band and had a wide normalized power spectrum density (PSD), high peak-to-side lobe ratio (PSR), and multiple peaks in autocorrelation. In an analysis of the simulation performance evaluation in navigation and communication, it is found that the proposed signal scheme has the potential for high accuracy, a code tracking accuracy of up to 0.85 m, a small mutual influence between the proposed signal scheme and other schemes, excellent anti-interference properties, and a better performance at both short and long distances in terms of its anti-multipath capability. Furthermore, the proposed signal scheme shows the ability to communicate between satellites and the ground and is outstanding in terms of its bit error rate (BER), CNR, and energy per bit to noise power spectral density ratio (Eb/N0). From the technical, theoretical, and application perspectives, our proposed signal scheme has potential as an alternative scheme in future BDS, PNTs, and even 5G/B5G.

To improve the standard point positioning (SPP) accuracy of integrated BDS (BeiDou Navigation Satellite System)/GPS (Global Positioning System) at the receiver end, a novel approach based on Long Short-Term Memory (LSTM) error correction recurrent neural network is proposed and implemented to reduce the error caused by multiple sources. On the basis of the weighted least square (WLS) method and Kalman filter, the proposed LSTM-based algorithms, named WLS–LSTM and Kalman–LSTM error correction methods, are used to predict the positioning error of the next epoch, and the prediction result is used to correct the next epoch error. Based on the measured data, the results of the weighted least square method, the Kalman filter method and the LSTM error correction method were compared and analyzed. The dynamic test was also conducted, and the experimental results in dynamic scenarios were analyzed. From the experimental results, the three-dimensional point positioning error of Kalman–LSTM error correction method is 1.038 m, while the error of weighted least square method, Kalman filter and WLS–LSTM error correction method are 3.498, 3.406 and 1.782 m, respectively. The positioning error is 3.7399 m and the corrected positioning error is 0.7493 m in a dynamic scene. The results show that the LSTM-based error correction method can improve the standard point positioning accuracy of integrated BDS/GPS significantly.

Real-time sub-meter location tracking is crucial for vehicle safety in transportation management, especially for large buses and dangerous goods vehicles, but many consumer level GNSS accuracy devices are just around 3-5 meters. This paper introduces a real-time array calibration (RAC) positioning technology, via road test comparison in different scenarios with other two terminals. Through the analysis of multiple indicators, it is found that the RAC terminal has better positioning stability than other terminals in different scenarios and can reach sub-meter accuracy. This technology can be used location based service (LBS) with sub-meter, and it will be widely applied to driver assistance system (DAS), unmanned aerial vehicle (UAV) navigation system and other fields.

The integrity and accuracy of positioning are crucial factors in vehicle safety. This article proposes an enhanced real-time array calibration (ERAC) technique based on real-time array error calibration with the Beidou Navigation Satellite System (BDS). Through fixed-point positioning tests and road tests, it is found that the ERAC method is superior to the traditional methods in terms of accuracy, stability, confidence, and the time required to reconstruct reliable points. The performance of the tested receiver is better than that of a commercial receiver with standard single-point positioning (SSP) in the context of multipath interference. Furthermore, the ERAC method meets the requirements for autonomous vehicle (AV) monitoring and public safety, especially in terms of the lane-level accuracy indicator, and its integrity indicators are greater than 95% to 97%. Additionally, the ERAC method has the potential to become an alternative solution for automatic vehicle safety monitoring in certain scenarios. This technique also has the ability to meet the wide positioning requirements of various vehicles and scenarios by adjusting its technical details.