With the rapid development of deep learning, more and more deep learning-based motor imagery electroencephalograph (EEG) decoding methods have emerged in recent years. However, the existing deep learning-based methods usually only adopt the constraint of classification loss, which hardly obtains the features with high discrimination and limits the improvement of EEG decoding accuracy. In this paper, a discriminative feature learning strategy is proposed to improve the discrimination of features, which includes the central distance loss (CD-loss), the central vector shift strategy, and the central vector update process. First, the CD-loss is proposed to make the same class of samples converge to the corresponding central vector. Then, the central vector shift strategy extends the distance between different classes of samples in the feature space. Finally, the central vector update process is adopted to avoid the non-convergence of CD-loss and weaken the influence of the initial value of central vectors on the final results. In addition, overfitting is another severe challenge for deep learning-based EEG decoding methods. To deal with this problem, a data augmentation method based on circular translation strategy is proposed to expand the experimental datasets without introducing any extra noise or losing any information of the original data. To validate the effectiveness of the proposed method, we conduct some experiments on two public motor imagery EEG datasets (BCI competition IV 2a and 2b dataset), respectively. The comparison with current state-of-the-art methods indicates that our method achieves the highest average accuracy and good stability on the two experimental datasets.

Although deep reinforcement learning (DRL) methods are promising for making behavioral decisions in autonomous vehicles (AVs), their low training efficiency and difficulty to adapt to untrained cases hinder their applications. Introducing a human role in the DRL paradigm could improve training efficiency by using human prior knowledge and overcome untrained cases in deployment by online human takeover. In this study, a novel value-based DRL algorithm that leverages human guidance to improve its performance is proposed for addressing high-level decision-making problems in autonomous driving. We develop a new learning objective for DRL to increase the value of the human policy over the undertrained DRL policy so that the DRL agent can be encouraged to mimic human behaviors and thereby utilizing human guidance more efficiently. Our method can autonomously evaluate the importance of different human guidance, which makes it more robust for variation of human performance. The proposed DRL algorithm was used to address a challenging multiobjective lane-change decision-making problem. We collected human guidance from a human-in-the-loop driving experiment and evaluated our method in a high-fidelity simulator. Results validated the advantages of the proposed algorithm in terms of training efficiency and optimality in the decision-making problem compared to the baselines of state-of-the-art existing methods. Results also revealed the favorable fine-tuning ability of the proposed algorithm, which is promising for addressing the long-tail issue in DRL-based autonomous driving. Our methodology does not introduce additional domain knowledge so that it can be seamlessly applied to other similar issues. The supplementary video is available at https://youtu.be/Ec7WkqeLsB8.

Learning-based techniques hold considerable promise in achieving human-like autonomous driving. However, one deployed policy encounters difficulties in satisfying the drivers’ diverse decision-making preferences simultaneously. Meanwhile, training personalized policies for each driver from scratch is time-consuming and resource-intensive. To address these challenges, this paper proposes a human-guided continual learning framework, wherein the human drivers could real-time take over a deployed policy when it performs unsatisfactorily, and the autonomous vehicle (AV) agent would automatically acquire human demonstrations and dynamically alter itself in accordance with personalized decision-making preference. Furthermore, a priority experience memory-enabled elastic weight consolidation (PEM-EWC) mechanism is developed to prevent the AV agent from overfitting to a limited number of human demonstrations and catastrophically forgetting its acquired fundamental driving abilities. Driver-in-the-loop simulations and real-world experiments are conducted in representative autonomous driving decision-making scenarios, and experimental results demonstrate the superior equilibrium of our proposed approach in terms of driving safety, human likeness, and training efficiency, compared to other baselines, which suggests that it provides a promising solution for personalized decision-making in autonomous driving. The supplementary video is available at https://youtu.be/HKF0ayxMycc.

Harmonic drive is the core component of the industrial robot, and its fault diagnosis is crucial to the reliability and performance of the equipment. Most machine learning methods achieve good results based on the assumption of data balance. However, the scarce fault data of harmonic drive is difficult to collect, resulting in various imbalanced health status samples, which has an adverse effect on fault diagnosis. In this article, we propose a data generation method based on generative adversarial networks (GANs) to solve the problem of data imbalance and utilize the multiscale convolutional neural network (MSCNN) to realize the fault diagnosis of the harmonic drive. First, the data collected from three vibration acceleration sensors are preprocessed by fast Fourier transform (FFT) to obtain the frequency spectrum of the vibration signal. Second, multiple GANs were adopted to generate various fault spectrum data and the data selection module (DSM) is elaborately designed to filter and purify these data. Third, the filtered generated data will be combined with the real data to form a balanced dataset, and then the MSCNN is used to achieve multiclassification of the health status of the harmonic drive. Finally, the experiments have been implemented on an industrial robot vibration test bench to validate the effectiveness of our approach. The results have shown the fault multiclassification accuracy as 98.49% under imbalanced fault data conditions, which outperforms that of the other compared methods.

The rapid development of large language models (LLMs) and multimodal large models (MLMs) has introduced transformative opportunities for autonomous driving systems. These advanced models provide robust support for the realization of more intelligent, safer, and efficient autonomous driving. In this paper, we present a systematic review on the integration of LLMs and MLMs in autonomous driving systems. First, we provide an overview of the evolution of LLMs and MLMs, along with a detailed analysis of the architecture of autonomous driving systems. Next, we explore the applications of LLMs and MLMs in key components such as perception, prediction, decision making, planning, multitask processing, and human–machine interaction. Additionally, this paper reviews the core technologies involved in integrating LLMs and MLMs with autonomous driving systems, including multimodal fusion, knowledge distillation, prompt engineering, and supervised fine tuning. Finally, we provide an in-depth analysis of the major challenges faced by autonomous driving systems powered by large models, offering new perspectives for future research. Compared to existing review articles, this paper not only systematically examines the specific applications of LLMs and MLMs in autonomous driving systems but also delves into the key technologies and potential challenges involved in their integration. By comprehensively organizing and analyzing the current literature, this review highlights the application potential of large models in autonomous driving and offers insights and recommendations for improving system safety and efficiency.

Since emotions generally affect driver’s behavior, judgment, and reaction time, accurately identifying driver’s emotions is of great significance to improve the safety and comfort of intelligent driving system. However, the gender, skin color, age, and appearance of different drivers often have big differences, which will greatly interfere with the emotional recognition process. Besides, light intensity inside the vehicle varies with different time, weather, and location, which will also pose a challenge to driver emotion recognition. In this paper, a robust driver emotion recognition method based on feature separation is proposed to overcome the interference of individual differences and illumination changes. In order to realize the separation of expression-related features and irrelevant features, we design a high-purity feature separation (HPFS) framework based on partial feature exchange and the constraints of multiple loss functions. To verify that the proposed method can overcome the interference of illumination changes, we specifically create a multiple light intensities driver emotion recognition (MLI-DER) dataset and conduct a great deal of experiments on the dataset. In addition, to further demonstrate that our method can largely alleviate the interference of individual difference, some cross-subject emotion recognition experiments are conducted on two famous facial expression recognition datasets FACES and Oulu-CASIA and the experimental results are compared with that of some state-of-the-art methods.