The application of deep learning in remote sensing image classification has been paid more and more attention by industry and academia. However, manually designed remote sensing image classification models based on convolutional neural networks usually require sophisticated expert knowledge. Moreover, it is notoriously difficult to design a model with both high classification accuracy and few parameters. Recently, neural architecture search (NAS) has emerged as an effective method that can greatly reduce the heavy burden of manually designing models. However, it remains a challenge to search for a classification model with high classification accuracy and few parameters in the huge search space. To tackle this challenge, we propose TPENAS, a two-phase evolutionary neural architecture search framework, which optimizes the model using computational intelligence techniques in two search phases. In the first search phase, TPENAS searches for the optimal depth of the model. In the second search phase, TPENAS searches for the structure of the model from the perspective of the whole model. Experiments on three open benchmark datasets demonstrate that our proposed TPENAS outperforms the state-of-the-art baselines in both classification accuracy and reducing parameters.

Current vehicle trajectory prediction methods often fail to capture the complex spatial and temporal interactions among surrounding vehicles in dynamic environments, leading to suboptimal prediction accuracy. In response to these challenges, we propose a novel Transformer-based model for vehicle trajectory prediction, denoted as the Dynamic Spatial-Temporal Interaction-aware Transformer Network (DSTIFormer). The three main parts of the DSTIFormer architecture are a spatial-temporal embedding module, a spatial-temporal dynamics interaction-aware module, and a trajectory encoderdecoder module. The spatial-temporal embedding module aims to encode vehicular trajectory interactions into latent representations that simultaneously preserve spatial dependencies and temporal features. The second module utilizes a temporal transformer network (TTN) along with a spatial graph transformer network (STN) to capture the dynamic spatial-temporal interactions between vehicles. To make trajectory predictions more accurate, the third module uses a sequence-to-sequence (Seq2Seq) architecture as the encoder and an attention-based decoder layer to effectively extract the long-range sequential dependencies in the process. The model's prediction accuracy with the root mean squared error (RMSE) metric on two publicly available datasets: NGSIM and HighD, and the results demonstrate that our proposed model outperforms several benchmark methods in long-term prediction.

Encoding historical traffic scenarios is critical for autonomous driving trajectory prediction. However, existing methods face two fundamental challenges. First, neglecting future interactions leads to myopic predictions. Second, although query-centric paradigms capture multimodal interactions, their typically static query design may limit the adaptability of cross-temporal context fusion. To address these limitations, we propose Miformer, a trajectory prediction model that enhances historical–future spatial–temporal context interactions. First, we introduce a time query mechanism that transforms static agent interactions into dynamic temporal modeling by enabling cross-timestep information exchange between different agents and road geometry. Building upon this temporal foundation, we design a historical–future spatial–temporal interaction module (HF-STIM) that leverages iTransformer to model bidirectional dependencies between past and future contexts. To address the redundancy inherent in inverted embeddings, we further propose the minus-inverted Transformer (Mi-Transformer), which incorporates a dynamic residual learning mechanism to eliminate irrelevant dependencies while preserving critical bidirectional information flow. Finally, we employ a DETR-like decoder to generate diverse multimodal trajectory predictions. Experimental results demonstrate that Miformer achieves state-of-the-art performance on the INTERACTION dataset and exhibits competitive performance on the Argoverse dataset. These results highlight its effectiveness in real-world motion prediction. Our code is available at https://github.com/Morphlingxxx/Miformer-Trajectory-Prediction

Accurate motion forecasting is critical for safe decision-making in autonomous driving. Existing methods lack explicit supervision during the generation of multimodal candidate trajectories, which leads to inconsistent candidate quality and difficulty balancing trajectory diversity with scene adaptability. We propose Mix-Forecast, a motion forecasting framework based on mixed supervision that explicitly optimizes the trajectory decoding process by combining multi-hypothesis supervision with competitive optimal supervision. We design a mixed supervision decoding mechanism that fuses the layer-by-layer auxiliary guidance of multi-hypothesis supervision with competitive optimal supervision in a single decoder. This mechanism improves the diversity and accuracy of candidate trajectories without adding extra computational cost. We also construct the Mamba-Transformer hybrid architecture, which efficiently integrates historical motion features and spatiotemporal context through cross-layer memory transfer and selective spatiotemporal state space modeling. Furthermore, we represent high-dimensional trajectories in the reproducing kernel Hilbert space (RKHS) and design an exclusivity loss based on a regularized kernel Kullback-Leibler (KKL) divergence to enhance prediction diversity. Experiments on the Argoverse motion forecasting benchmarks show that Mix-Forecast performs exceptionally well, and ablation studies confirm the complementary contributions of mixed supervision, cross-layer memory, and KKL loss.