Evaluation methods for autonomous driving are crucial for algorithm optimization. However, due to the complexity of driving intelligence, there is currently no comprehensive evaluation method for the level of autonomous driving intelligence. In this paper, we propose an evaluation framework for driving behavior intelligence in complex traffic environments, aiming to fill this gap. We constructed a natural language evaluation dataset of human professional drivers and passengers through naturalistic driving experiments and post-driving behavior evaluation interviews. Based on this dataset, we developed an LLM-powered driving evaluation framework. The effectiveness of this framework was validated through simulated experiments in the CARLA urban traffic simulator and further corroborated by human assessment. Our research provides valuable insights for evaluating and designing more intelligent, human-like autonomous driving agents. The implementation details of the framework11https://github.com/AIR-DISCOVER/Driving-Intellenge-Evaluation-Framework and detailed information about the dataset22https://github.com/AIR-DISCOVER/Driving-Evaluation-Datasetcan be found at the provided links.

This paper investigates the physical layer security (PLS) issue of unmanned aerial vehicle (UAV) aided multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) networks with randomly distributed passive eavesdroppers (Eves). Considering Nakagami-m fading, we propose a novel secure communication protocol that integrates transmit antenna selection (TAS) and maximum ratio combining (MRC) diversity technology. Specifically, to tackle the challenges of low spectrum efficiency and PLS performance, we propose two TAS solutions: TAS-max UN and TAS-max UF, the first one aims to enhance the performance of UN and the other one focuses on UF. To mitigate the impact of passive eavesdropping, a secure protected zone is established around the UAV to limit the Eve’s ability. Accordingly, we derive the closed-form expressions for the ergodic secrecy rate to evaluate the impact of spatial randomness. Then, the accuracy of the derived expressions is verified through Monte-Carlo simulations.To further support the theoretical analysis, asymptotic expressions under the high-SNR regime are derived, which offer valuable insights into secrecy rate trends and model convergence. Moreover, we propose a three-dimensional UAV deployment optimization framework that adopts a hybrid approach combining grid-based evaluation and Genetic Algorithm refinement, which improves ESR performance while significantly reducing computational complexity. In addition, a Simulated Annealing based power allocation scheme is introduced to optimize the power coefficient aF, achieving enhanced secrecy rate with improved search efficiency and adaptability. Extensive simulation results confirm that the proposed TAS/MRC framework, together with the secure protected zone, consistently outperforms conventional OMA and MRT schemes in terms of secrecy rate and robustness. The impact of key system parameters, including power allocation, UAV altitude, antenna configuration, and Eve density, is also thoroughly analyzed.

The deep random vector functional link (dRVFL) and ensemble dRVFL (edRVFL) succeed in various tasks and achieve state-of-the-art performance compared with other randomized neural networks (NNs). However, existing edRVFL structures need more diversity and error correction ability in an independent network. Our work fills the gap by combining stacked deep blocks and residual learning with the edRVFL. Subsequently, we propose a novel dRVFL combined with residual learning, ResdRVFL, whose deep layers calibrate the wrong estimations from shallow layers. Additionally, we propose incorporating a scaling parameter to control the scaling of residuals from shallow layers, thus mitigating the risk of overfitting. Finally, we present an ensemble deep stacking network, SResdRVFL, based on ResdRVFL. SResdRVFL aggregates multiple blocks into a cohesive network, leveraging the benefits of deep learning and ensemble learning. We evaluate the proposed model on 28 datasets and compare it with the state-of-the-art methods. The comparative study demonstrates that the SResdRVFL is the best-performing approach in terms of average ranking and errors based on 28 datasets.

To tackle the twin challenges of limited battery life and lengthy charging durations in electric vehicles (EV s), this paper introduces an Energy-efficient Hybrid Model Predictive Planner (EHMPP), which employs an energy-saving optimization strategy. EHMPP focuses on refining the design of the motion planner to be seamlessly integrated with the existing automatic driving algorithms, without additional hardware. It has been validated through simulation experiments on the Prescan, CarSim, and Matlab platforms, demonstrating that it can increase passive recovery energy by 11.74% and effectively track motor speed and acceleration at optimal power. To sum up, EHMPP not only aids in trajectory planning but also significantly boosts energy efficiency in autonomous EVs.

Recent research in the field of Human Activity Recognition has shown that an improvement in prediction performance can be achieved by reducing the number of LSTM layers. However, this kind of enhancement is only significant on monolithic architectures, and when it runs on large-scale distributed training, data security and privacy issues will be reconsidered, and its prediction performance is unknown. In this paper, we introduce a novel framework: FedBChain, which integrates the federated learning paradigm based on a modified DeepConvLSTM architecture with a single LSTM layer. This framework performs comparative tests of prediction performance on three different real-world datasets based on three different hidden layer units (128, 256, and 512) combined with five different federated learning strategies, respectively. The results show that our architecture has significant improvements in Precision, Recall and F1-score compared to the centralized training approach on all datasets with all hidden layer units for all strategies: FedAvg strategy improves on average by 4.54%, FedProx improves on average by 4.57%, FedTrimmedAvg improves on average by 4.35%, Krum improves by 4.18% on average, and FedAvgM improves by 4.46% on average. Based on our results, it can be seen that FedBChain not only improves in performance, but also guarantees the security and privacy of user data compared to centralized training methods during the training process. The code for our experiments is publicly available (https://github.com/Glen909/FedBChain).

With the advancements of sensor hardware, traf-fic infrastructure and deep learning architectures, trajectory prediction of vehicles has established a solid foundation in intelligent transportation systems. However, existing solutions are often tailored to specific traffic networks at particular time periods. Consequently, deep learning models trained on one network may struggle to generalize effectively to unseen networks. To address this, we proposed a novel spatial-temporal trajectory prediction framework that performs cross-domain adaption on the attention representation of a Transformer-based model. A graph convolutional network is also integrated to construct dynamic graph feature embeddings that accurately model the complex spatial-temporal interactions between the multi-agent vehicles across multiple traffic domains. The proposed framework is validated on two case studies involving the cross-city and cross-period settings. Experimental results show that our proposed framework achieves superior trajectory prediction and domain adaptation performances over the state-of-the-art models.

Soft sensing of hard-to-measure variables is often crucial in industrial processes. Current practices rely heavily on conventional modeling techniques that show success in improving accuracy. However, they overlook the non-linear nature, dynamics characteristics, and non-Euclidean dependencies between complex process variables. To tackle these challenges, we present a framework known as a Knowledge discovery graph Attention Network for effective Soft sensing (KANS). Unlike the existing deep learning soft sensor models, KANS can discover the intrinsic correlations and irregular relationships between the multivariate industrial processes without a predefined topology. First, an unsupervised graph structure learning method is introduced, incorporating the cosine similarity between different sensor embedding to capture the correlations between sensors. Next, we present a graph attention-based representation learning that can compute the multivariate data parallelly to enhance the model in learning complex sensor nodes and edges. To fully explore KANS, knowledge discovery analysis has also been conducted to demonstrate the interpretability of the model. Experimental results demonstrate that KANS significantly outperforms all the baselines and state-of-the-art methods in soft sensing performance. Furthermore, the analysis shows that KANS can find sensors closely related to different process variables without domain knowledge, significantly improving soft sensing accuracy.

This paper investigates the power efficiency of physical layer security (PLS) in intelligent reflecting surface (IRS)-assisted multi-user uplink channels. Existing research works usually focus on enhancing secrecy performance, and neglect measures to improve power efficiency. In this paper, the optimization problem is formulated to minimize the sum radio frequency (RF) power of multiple users in the uplink channel subject to secrecy outage probability constraint. This problem is solved by an alternating optimization (AO) algorithm that includes three optimization sub-problems, i.e., phase shift matrix, receiving matrix, and RF power optimization. Furthermore, to reduce the complexity of the proposed AO algorithm, a deep learning (DL)-based approach is proposed to optimize the sophisticated phase shift matrix optimization process. Simulation results demonstrate that the proposed scheme can significantly reduce the average RF power, and the DL-based scheme achieves similar performance as AO algorithm while reducing the time complexity significantly.

In this paper, we propose a multi-roadside unit (RSU) assisted mobile crowd computing framework for intelligently connected vehicle (ICV) networks, where vehicles within RSUs’ coverage act as workers to provide their computation and communication resources for computing resource limited vehicle user equipments (VUEs). Physical (PHY) layer security is used to secure computation task offloading and results feedback in time-varying vehicular channels. Artificial noise (AN) assisted adaptive wiretap coding is adopted to enhance the security of offloading links. With PHY security, the intended receiver can decode secret message while eavesdropper cannot. A modified exhaustive two-dimensional (2D) search algorithm is proposed to optimize transmission rate and secrecy rate in an effective secrecy throughput maximization problem, and a multi-agent twin delayed deep deterministic policy gradient algorithm (MATD3) is utilized to assign VUEs’ tasks without a central controller, where a reward function is defined according to the computing costs, including execution time, energy consumption, and price paid for computing. Finally, simulations verify the effectiveness of the proposed framework.

We present a speech data corpus that simulates a "dinner party" scenario taking place in an everyday home environment. The corpus was created by recording multiple groups of four Amazon employee volunteers having a natural conversation in English around a dining table. The participants were recorded by a single-channel close-talk microphone and by five far-field 7-microphone array devices positioned at different locations in the recording room. The dataset contains the audio recordings and human labeled transcripts of a total of 10 sessions with a duration between 15 and 45 minutes. The corpus was created to advance in the field of noise robust and distant speech processing and is intended to serve as a public research and benchmarking data set.