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In the evolving landscape of mixed human-machine driving environments, autonomous vehicles (AVs) confront the challenge of anticipating the lane-changing intentions and subsequent driving trajectories of neighboring vehicles. This capability is essential for optimizing safety, efficiency, and comfort in decision-making processes. This paper introduces a novel hybrid prediction model, the LSTM-GAT-Bilayer-GRU, which leverages deep learning to enhance predictive accuracy and real-time responsiveness in dynamic traffic scenarios. The proposed model consists of two main components: a lane change prediction model (LSTM-GAT) and a trajectory prediction model (G-BiLayer-GRU), to process and predict complex vehicular interactions and environmental dynamics effectively. The efficacy of this integrated model was tested using the HighD dataset for training, validation, and testing purposes. The results of a benchmark analysis indicate that the proposed model demonstrated superior prediction performance and reliability over the Support Vector Machine (SVM), Random Forest (RF), AlexNet and Back-Propagation Through Time (BPTT) in the context of lane change intention recognition. Combining LSTM for temporal data processing with GAT for spatial interaction analysis, along with the GRU’s precise trajectory prediction, achieved the best error evaluation metric and balanced prediction time consuming metric under the six prediction time-interval, marks a substantial advancement in AVs technology. This integration guarantees smooth operation of AVs in intricate driving scenarios, fine-tuning their reactions to bolster road safety and passenger comfort.

Many designs of in-memory logic computing using ReRAM have been proposed and even a few of them have been experimentally demonstrated. However, the ability to cascade and extend the designed circuits is limited. In this work, we elaborate a design called time-sum-logic based on 2T1R memristor arrays and implement arithmetic computing circuits using memristors by integrating both time and space dimension. The core method of our design is to use sum of minterms to transfer logic to time sequence by 2T1R memristor arrays. This method is extensible by extending the number of layers to realize multi-bit arithmetic computing. Remarkably, cascading between different layers is elaborated carefully and pipelines are adopted to manage the sequence of memristors’ writing and reading and lower the complexity and latency. In this way, the proposed circuits will be more efficient and can support high data throughout. Based on our design, 4-bit full adder and $4\times 4$ multiplier are designed with pipelines, and n-bit full adders and m $\times $ n multipliers could be easily constructed. The low latency and high efficiency confirm the advantages we have illustrated and show a promised application prospect.

A multi-scale attention-based model (MSAM) is proposed as a surrogate model for uncertainty analysis (UA) in ground penetrating radar (GPR) simulation. Instead a thousand of full-wave simulations, the surrogate model converts the uncertain inputs to electric fields, and the output uncertainty is effectively quantified. Global feature aggregation (GFA) module and local affinity reconstruction (LAR) are presented to improve the model representation capability by Affinity calculation under different receptive fields. In addition, a new loss function is proposed to accelerate the convergence of the model for training data with a wider range of input disturbances. The effectiveness and accuracy of the surrogate model are verified by comparing the UA results with the Monte Carlo method (MCM). In comparison with existing deep learning methods, the proposed method can efficiently get higher quality predictions. Meanwhile, the Sobol indices evaluated by MSAM accord with those of MCM, and the mean square error between them is only 0.0005. However, the MCM needs to run the full-wave simulation one thousand times to converge, which is much more time consuming than the proposed surrogate model.

Active fault detection (AFD) is considered an effective incipient failure-detection method. In this paper, an auxiliary input resonance signal proposal that can detect different incipient faults quickly while keeping the system stable is proposed for AFD. To maintain the stability of the system, auxiliary input signals with a sinusoidal form, which should minimize and maximize the effect in fault-free cases and fault cases, are designed on the YJBK parameterization architecture. Subsequently, the fault detection signal is transferred from the traditional system output signal to the cumulative sum of the designed residual signal. Finally, some illustrative examples are given to indicate the effectiveness of the proposed unified operation architecture and some encourage results have been obtained, the simulation results show that the designed auxiliary input signal can not only ensure the normal operation of the system, but also quickly and effectively detect the occurrence of minor faults.