Accurate prediction of vehicle trajectories in complex traffic environments is essential for path planning and safety decisions in autonomous driving systems. However, purely data-driven models lack physical constraints, making it challenging to ensure reliability and consistency in dynamic traffic scenarios, while physics models face challenges in maintaining long-term prediction reliability under complex traffic conditions. To address these issues, a trajectory prediction method is proposed by combining a data-driven model based on graph neural networks and Informer with a physics model, fused through discriminative learning. Firstly, graph neural networks are utilized to extract spatial information, and the Informer is used for long-term trajectory encoding and decoding to capture the temporal dynamics of trajectories. Then, to improve the physical plausibility of trajectory predictions, a kinematic model with Cubature Kalman Filtering is employed to estimate the trajectory distribution. Furthermore, discriminative learning is designed to fuse a physics model into the decoder of the data-driven model using a generative adversarial approach. This paper conducts ablation experiments and comparative tests on real-world highway trajectory data from the NGSIM dataset. The evaluation confirms that the proposed method achieves consistent and high-quality prediction results across multiple scenarios.

Human feedback in generative systems is a highly active frontier of research that aims to improve the quality of generated content and align it with subjective preferences. Existing efforts predominantly focus on text-only large language models (LLMs) or text-based image generation, while cross-modal generation between audio and text remains largely unexplored. Moreover, there is currently no open-source preference dataset to support the deployment of alignment algorithms in this domain. In this work, we take audio speech translation (AST) and audio captioning (AAC) tasks as examples to explore how to enhance the performance of mainstream audio-based text generation models with limited human annotation. Specifically, we propose an novel framework named IPO that includes a model adversarial sampling concept--human annotators act as referees to determine model outcomes, using these results as pseudo-labels for the corresponding beam search hypotheses. Given these imbalance win-loss results, IPO effectively enable the two models to update interactively to win the next round of adversarial sampling. We conduct both subjective and objective evaluations to demonstrate the alignment benefits of IPO and its enhancement on model perception and generation capacities. On both AAC and AST, a few hundreds of annotations significantly enhance the weak model, and the strong model can also be encouraged to achieve new state-of-the-art results in terms of different objective metrics. Additionally, we show the extensibility of IPO by applying it to the reverse task of text-to-speech generation, improving the robustness of system on unseen reference speaker.

Vehicle trajectory prediction is a critical and complex task in autonomous driving systems, where accurate prediction is essential to ensure both safety and comfort. Given that driving style impacts future trajectory prediction, integrating driving style information is crucial. In this paper, a trajectory prediction framework is proposed, in which a spatial-temporal information fusion network incorporating driving style is leveraged. Driving style is captured through a denoising Transformer autoencoder for dimensionality reduction and refined using fuzzy k-means++ clustering. Temporal information and spatial interactions of the vehicle are dynamically extracted using Transformer and graph neural network, and the future trajectory distribution is generated using a Transformer decoder enhanced by the KAN network with a sine function. Ablation and comparison experiments are carried out on the public NGSIM dataset. The results demonstrate that our model outperforms others in prediction accuracy, with improvements of up to 29.7% across evaluation metrics.

Our framework employs a hybrid model of Bidirectional Temporal Convolutional Network and Bidirectional Gated Recurrent Unit for sequential intention prediction. Additionally, a TransformerConv-based Graph Attention Network captures spatial interactions from historical frames and incorporates temporal features to generate spatial-temporal features, which are further processed by an intention-inspired context extraction attention mechanism to generate inputs for final trajectory prediction. On the NGSIM US-101 and I-80 datasets, our model achieves a 93.20% accuracy in predicting sequential driving intentions at a prediction horizon of 3 seconds. By incorporating these predicted intentions into the trajectory prediction network, it reduces the RMSE by 11.5% over a prediction horizon of 5 seconds compared to state-of-the-art methods, demonstrating its effectiveness in highway scenarios.

The interpretability of Convolutional Neural Networks (CNNs) has garnered significant attention in computer vision. Integrated Gradients (IG), as a widely used feature attribution method, quantifies the contribution of input features (pixels) to model predictions by accumulating gradients along an interpolation path. However, the linear interpolation path employed by IG can result in inflated attribution scores for irrelevant (unimportant) pixels, introducing noise into saliency maps and reducing their reliability. To address this issue, this paper proposes an improved feature attribution method called Dynamic Weighted Path Integration (DWPI). DWPI incorporates a dynamic interpolation path strategy, prioritizing the movement of the least important pixels to minimize interference from irrelevant pixels in attribution results. Additionally, DWPI leverages the model output rate to weight gradients, amplifying the influence of high-quality gradients and further enhancing the reliability of saliency maps. Experimental results on the ImageNet validation set demonstrate that DWPI outperforms other methods across various models and four standard perturbation test metrics. Ablation study further confirms the effectiveness of the dynamic interpolation path strategy and gradient weighting mechanism.

Colorectal cancer is one of the malignant tumors with high morbidity and mortality rates worldwide. Colonoscopy is currently the most effective clinical method for screening colorectal polyps and is of great significance for the early detection and intervention of the disease. It is crucial to accurately segment the polyp area from colonoscopy images. However, the significant variation in polyp appearance and the blurred boundaries with surrounding mucosa make accurate and robust automatic segmentation highly challenging. To address this, we propose a feature fusion network guided by multi-scale convolutional attention, named MsCAFF (Multi-scale Convolutional Attention-based Feature Fusion). Specifically, the Convolutional Attention Multi-scale Fusion module (CAMF) fuses multi-scale information from high-level feature layers based on convolution operations. It enhances the spatial representation of the feature map through channel and spatial attention mechanisms from CBAM (Convolutional Block Attention Module), while generating a global feature map to serve as the initial guidance for the RA (Reverse Attention) module. This design allows the model to better focus on the target when roughly locating polyp regions, without losing critical boundary information. Through quantitative and qualitative evaluations on multiple challenging datasets, the results show that our network MsCAFF achieves significant improvement in segmentation accuracy.

Thermal runaway of lithium-ion batteries is one of the key challenges hindering the development of electric vehicles. Realizing timely fault detection in battery systems is of great significance for preventing thermal runaways and safeguarding people’s lives and properties. As it is difficult to obtain fault battery datasets in the real world, there is a need to develop novel fault detection methods that can operate with normal data. In this paper, we propose an optimized Gated Recurrent Unit autoencoder architecture that integrates the sparse representation technique to detect battery faults in electric vehicles. Firstly, the Gated Recurrent Unit is employed to efficiently learn the information in battery data from normal electric vehicles. Then, the autoencoder utilizes the sparse representation technique to improve its ability to recognize abnormal data by learning a set of basis vectors that can sparsely represent normal data. Finally, the reconstruction errors between the original and reconstructed vectors are calculated in a sliding window and compared to the threshold to detect the fault. The effectiveness of the proposed method is verified on a real operating dataset including two normal electric vehicles and two faulty electric vehicles. The results show that it can provide early alarm time and reduce the probability of false alarms.

Lithium-ion batteries are widely used in portable devices and mobile medical equipment due to their high energy density and long cycle life. However, long charging times for lithium-ion batteries can limit their usability. This paper proposes a multi-stage constant current charging protocol.

Lithium-ion batteries are the crucial energy source for electric vehicles. However, they experience capacity degeneration when used in low-temperature environments. It is necessary to preheat them before using. In this paper, a core temperature-aware optimal preheating strategy, featuring a multi-stage constant-current discharge heating method, is proposed to heat lithium-ion batteries in low-temperature environments. Firstly, this paper builds an internal battery temperature distribution model based on Fourier’s law of heat conduction. Secondly, the temperature distribution model is coupled within the battery model to display the comprehensive performance of the battery. Thirdly, decreasing heating time and reducing capacity loss jointly formulate a multi-objective optimization problem solved by dynamic programming(DP) algorithm. Judging by simulation results, heating time is downsized and the capacity loss is reduced at the same time, proving the progressiveness of the proposed strategy.

Counterfactual explanations (CFEs) exemplify how to minimally modify a feature vector to achieve a different prediction for an instance. CFEs can enhance informational fairness and trustworthiness, and provide suggestions for users who receive adverse predictions. However, recent research has shown that multiple CFEs can be offered for the same instance or instances with slight differences. Multiple CFEs provide flexible choices and cover diverse desiderata for user selection. However, individual fairness and model reliability will be damaged if unstable CFEs with different costs are returned. Existing methods fail to exploit flexibility and address the concerns of non-robustness simultaneously. To address these issues, we propose a conceptually simple yet effective solution named Counterfactual Explanations with Minimal Satisfiable Perturbations (CEMSP). Specifically, CEMSP constrains changing values of abnormal features with the help of their semantically meaningful normal ranges. For efficiency, we model the problem as a Boolean satisfiability problem to modify as few features as possible. Additionally, CEMSP is a general framework and can easily accommodate more practical requirements, e.g., casualty and actionability. Compared to existing methods, we conduct comprehensive experiments on both synthetic and real-world datasets to demonstrate that our method provides more robust explanations while preserving flexibility.

The driving performance of electric vehicles seriously degrades due to the deterioration of lithium-ion batteries at low temperatures. Preheating lithium-ion batteries can effectively improve the driving range of electric vehicles at subzero temperatures. In this paper, an optimal pulse heating strategy is proposed for low-temperature heating of lithiumion battery. Firstly, this paper establishes a coupling model to describe the electro-thermal-aging behavior of battery. Secondly, the heating time and capacity loss jointly form a multi-objective optimization problem with the current constraint. The optimization problem is solved by using the particle swarm optimization(PSO) algorithm and the effect of weighting coefficient on heating performance is discussed to obtain the optimal pulse current. The results show that the proposed strategy can effectively reduce heating time without causing serious capacity reduction.

Traditional lighting systems suffer from the problem of low energy efficiency and low illumination quality due to its disappointing management. To address this issue, in this paper, a novel traffic-flow adaptive scheme of smart lighting systems is proposed on the basis of the cyber-physical cloud system. The cyber-physical cloud system consists of the digital twin and cyber-physical system. The operation of the lighting system is simulated in the counterpart twin system with the digital twin technology. The cyber-physical system realizes data collection, information interaction, analysis, and processing, as well as complex computation and remote control. The traffic adaptive scheme works according to the brightness sequence to improves the energy efficiency of the lighting system and provide higher illumination quality for drivers. Extensive simulation results verify the proposed control scheme could improve the energy efficiency of lighting systems.

The filter-based real-time energy management method has been proved practical and widely utilized in hybrid energy storage systems. However, the determination for the cutoff frequency of the energy-split filter is challenging. In this paper, an optimal filter-based energy management strategy is proposed for a battery/ultracapacitor electric vehicle to minimize the total energy consumption. A cost function of energy consumption for the cutoff frequency is established first. Considering the working condition of ultracapacitors, dynamic programming is adopted to obtain the optimal cutoff frequency series, i.e., the optimal energy distribution between batteries and ultracapacitors. Such an off-line optimization process is carried out under different driving cycles, e.g., urban and highway road conditions. Optimization results are used to determine the optimal cutoff frequency of a real-time filter-based energy management strategy. Simulation results indicate that the proposed strategy can minimize the total energy consumption of the hybrid energy storage system with ultracapacitors state of charge limitations being guaranteed. Compared with the existing real-time energy management strategies, the energy consumption is reduced 23.85% under aggressive acceleration conditions and 7.08% under urban conditions by the proposed strategy.

This paper proposes an optimal car-following strategy for pure electric vehicles (EVs) with the aim of keeping an expected headway of the leader and reducing vehicle battery loss. In particular, a car-following system model is established. The primary task of the automatic vehicle is to follow the trajectory of the preceding car and maintain an expected headway. Then, the paper analyzes the powertrain of the electric vehicle. The loss of battery life over a period of time is proportional to the acceleration, so it takes the battery life into consideration. The Q-learning algorithm is conducted for the optimal car-following strategy using system data instead of system dynamics information. It utilizes reward function and greedy strategy to select actions to train the following vehicle to achieve car-following safety. When there is no collision in these two cars, acceleration is considered into reward function to reduce battery loss. Finally, it is verified by simulation that the proposed car-following strategy can keep good tracking, maintain the expected headway from the preceding vehicle, and reduce battery loss.

Distant supervised relation extraction (RE) has been an effective way of finding novel relational facts from text without labeled training data. Typically it can be formalized as a multi-instance multi-label problem.In this paper, we introduce a novel neural approach for distant supervised (RE) with specific focus on attention mechanisms.Unlike the feature-based logistic regression model and compositional neural models such as CNN, our approach includes two major attention-based memory components, which is capable of explicitly capturing the importance of each context word for modeling the representation of the entity pair, as well as the intrinsic dependencies between relations.Such importance degree and dependency relationship are calculated with multiple computational layers, each of which is a neural attention model over an external memory. Experiment on real-world datasets shows that our approach performs significantly and consistently better than various baselines.