Large language models (LLMs), AI systems trained to process and generate human language, are increasingly being integrated into autonomous vehicles, leading to the emergence of LLM-assisted driving systems (LADSs). Rigorous evaluation of LADSs is essential for driving technological progress and building user trust. However, there is currently a lack of specific evaluation metrics for LADSs, and existing LLM evaluation methods are not readily applicable to LADSs. To evaluate the performance of LADSs, this study proposes four assessment indices that collectively consider driving robustness, safety, and ethical decision-making. First, cosine similarity is employed to guide the injection of disturbances, establishing a basis for quantitative input-output analysis. Second, robustness and safety indices are proposed to characterize vehicle performance, while an LLM-based evaluator is used to assess ethical behavior. To enhance alignment with human judgment, a language-numerical optimization algorithm is developed for prompt tuning. By integrating the knowledge base, Cohen's Kappa (κ) between the experienced driver and the LLM-based evaluator reaches 0.81, indicating strong agreement. Additionally, this study first identifies and analyzes a novel phenomenon termed "extreme thinking". Building on these results, a multi-dimensional safety assessment index is proposed to evaluate LADSs. The proposed indices and methods are validated using over 1000 data segments collected from both simulations and experiments.

Reducing undesirable path crossings among tra jectories of different robots is vital in multi-robot navigation missions, which not only reduces detours and conflict scenarios, but also enhances navigation efficiency and boosts productivity. Despite recent progress in multi-robot path-crossing-minimal (MPCM) navigation, the majority of approaches depend on the minimal squared-distance reassignment of suitable desired points to robots directly. However, if obstacles occupy the passing space, calculating the actual robot-point distances becomes complex or intractable, which may render the MPCM navigation in obstacle environments inefficient or even infeasible. In this paper, the concurrent-allocation task execution (CATE) algorithm is presented to address this problem (i.e., MPCM navigation in obstacle environments). First, the path-crossing related elements in terms of (i) robot allocation, (ii) desired-point convergence, and (iii) collision and obstacle avoidance are en coded into integer and control barrier function (CBF) constraints. Then, the proposed constraints are used in an online constrained optimization framework, which implicitly yet effectively minimizes the possible path crossings and trajectory length in obstacle environments by minimizing the desired point allocation cost and slack variables in CBF constraints simultaneously. In this way, the MPCM navigation in obstacle environments can be achieved with flexible spatial orderings. Note that the feasibility of solutions and the asymptotic convergence property of the proposed CATE algorithm in obstacle environments are both guaranteed, and the calculation burden is also reduced by concurrently calculating the optimal allocation and the control input directly without the path planning process. Finally, extensive simulations and experiments are conducted to validate that the CATE algorithm (i) outperforms the existing state-of-the-art baselines in terms of feasibility and efficiency in obstacle environments, (ii) is effective in environments with dynamic obstacles and is adaptable for per forming various navigation tasks in 2D and 3D, (iii) demonstrates its efficacy and practicality by 2D experiments with a multi-AMR onboard navigation system, and (iv) provides a possible solution to evade deadlocks and pass through a narrow gap.

The rapid increase in electric vehicle (EV) adoption underscores the urgent need for advanced charging infrastructure and strategies. This survey provides a comprehensive examination of battery charging, with a particular focus on control and optimization dimensions. It meticulously reviews a variety of control methods and optimization techniques, addressing critical factors such as charging efficiency, battery longevity, safety protocols, thermal management, and cell balancing. By enhancing our understanding of these crucial aspects, this paper not only highlights the current state of battery charging control and optimization but also sets the stage for future research and developments in this dynamic field.

This article explores the challenge of achieving scalable and constrained consensus in general linear multiagent systems (MASs), where agents can occasionally join and leave the network. Two distributed model predictive control (DMPC)-based consensus methods are developed to tackle the scalability, performance, and constraint challenges. The first approach uses an innovative online DMPC optimization that integrates with a predesigned scalable consensus protocol, ensuring constraint satisfaction while achieving scalable consensus. The second method leverages tracking DMPC, enabling each agent to adhere to a locally evolving time-specific reference, which is continually updated through the utilization of the predicted state sequences from neighboring agents. Moreover, it is shown that the feasibility of the associated optimization problems can be recursively ensured with the suitably designed cost function and constraints. In addition, the scalable consensus property of the constrained MAS is guaranteed. Finally, the simulation results illustrate the effectiveness of the proposed algorithms.

Precise path tracking and agilely avoiding obstacles are essential for the stability and safety of autonomous driving. In this paper, we introduce a uniform safe path tracking control strategy that combines obstacle avoidance with path tracking via a barrier function. Unlike the conventional hierarchical collision avoidance methods, our approach employs an integral heuristic barrier function that addresses obstacle avoidance planning and reference trajectory tracking problems simultaneously. Via this, the complex safe trajectory following problem is simplified into a tractable yaw angle tracking problem. We then present a novel finite-time adaptive barrier function-based sliding mode controller that handles input saturation and enhances robustness. This ensures precise and robust yaw angle tracking within specified performance constraints. Moreover, the proposed approach achieves accelerated finite-time convergence compared to the exponential convergence rate. Finally, the Carsim-Simulink co-simulations and real-vehicle experiments validate the effectiveness and superiority of our method in addressing the path-tracking challenge, while upholding driving safety.

This study addresses the challenges and solutions for achieving flexible and resilient platooning in Intelligent and Connected Vehicles (ICVs) under diverse constraints. We focus on enabling vehicles to freely join or leave the platoon and maintaining resilience against adversarial cyberattacks within the network. We propose a hierarchical distributed coordination framework that combines high-level event-driven cluster coordination with lower-level decoupled longitudinal and lateral control designs. Each normal vehicle updates its longitudinal state by solving a distributed optimization-based control problem, utilizing both itself and neighboring vehicles’ information. Meanwhile, the lateral control scheme employs a decentralized optimization algorithm to facilitate lane-changing coordination. Additionally, we develop a distributed attack detection algorithm that enables the identification and removal of adversarial vehicles from the platoon. The stability of the closed-loop system is proven, and simulation results validate the effectiveness of our framework in achieving flexible and resilient vehicle platooning.

The predictive energy management strategy (PEMS) offers potential advantages in enhancing the driving economy of electrified vehicles using vehicle speed prediction. However, realizing accurate predictions in practical contexts remains a challenge. Departing from conventional PEMS that rely on historical speed or static traffic data, we introduce a real-time traffic-aware PEMS for improved performance. To better understand the interplay between the host vehicle and its surrounding traffic, we use a Transformer network as the predictor that employs the speeds and relative distances of the surrounding six vehicles to forecast future speed sequences for the host vehicle. To augment this data-driven approach, we develop a dual-predictor strategy based on the deep ensemble technique. This strategy measures the Transformer’s output uncertainty to gauge prediction reliability and introduce an automated threshold mechanism. Based on this threshold and real-time uncertainties, the strategy chooses between the Transformer and an exponential predictor to achieve improved prediction outcomes. A reinforcement learning method is integrated as the PEMS optimizer. For validation, we generate training data with traffic information based on the next generation simulation (NGSIM) dataset and create a test scenario in the SUMO simulator. The results confirm that speed predictions based on real-time traffic data surpass traditional PEMS, either directly inputting traffic data or excluding it. The Transformer predictor significantly outperforms the state-of-the-art predictor. Importantly, our dual-predictor design amplifies prediction accuracy by 27.2% against the standard single-network predictor under non-training conditions. Overall, our PEMS enhances driving economy by 11.1% relative to traffic-unaware models and 8.0% over non-Transformer schemes.

Driver drowsiness detection is of great significance in improving driving safety and has been widely studied in recent years. However, some existing methods have not fully utilized the drowsiness-related information, and some methods are susceptible to interference from the redundant information of input data. To address these issues, a video-based driver drowsiness detection method according to the key facial features including facial landmarks and local facial areas (VBFLLFA) is proposed in this paper. In order to fully utilize the key facial features related to drowsiness and exclude the interference of redundant information, the head movement information is obtained through facial landmark analysis and the movement information of eyes and mouth is acquired from the local facial areas. And the spatial filtering based on the common spatial pattern (CSP) algorithm is introduced to improve the discrimination of different classes of samples. To adequately extract the temporal and spatial features, a two-branch multi-head attention (TB-MHA) module is designed in this paper. Furthermore, the center loss with center vector distance penalty is introduced to further improve the discrimination of different classes of samples in the feature space. In addition to two public datasets, we specifically create a novel video-based driver drowsiness detection (VBDDD) dataset to evaluate the effectiveness of our method. The experimental results verify that our method can achieve very excellent performance in driver drowsiness detection tasks.

This article studies the formation stabilization problem of asynchronous nonlinear multiagent systems (MAS) subject to parametric uncertainties, external disturbances, and bounded time-varying communication delays. A self-triggered min–max distributed model predictive control (DMPC) approach is proposed to address this problem. At triggering instants, each agent solves a local min–max optimization problem based on local system states and predicted states of neighbors, determines its next triggering instant, and broadcasts its predicted state trajectory to the neighbors. As a result, the communication load is greatly alleviated while retaining robustness and comparable control performance compared to periodic DMPC algorithms. In order to handle time-varying delays, a novel consistency constraint is incorporated into each local optimization problem to restrict the deviation between the newest predicted states and previously broadcasted predicted states. Consequently, each agent can utilize previously predicted states of its neighbors to achieve cooperation in the presence of the asynchronous communication and time-varying delays. The proposed algorithm’s recursive feasibility and MAS’s closed-loop stability at triggering instants are proven. Finally, numerical simulations are conducted to verify the theoretical results.

This article studies the formation tracking problem of a team of autonomous underwater vehicles (AUVs) with the ocean current disturbances. A distributed Lyapunov-based model predictive controller (DLMPC) is designed such that AUVs can keep the desired formation while tracking the reference trajectory, despite the presence of external disturbances. The DLMPC inherits the stability and robustness of the extended state observer (ESO)-based auxiliary control law and invokes online optimization to improve formation tracking performance of the multi-AUV system. The closed-loop stability of the multi-AUV system is guaranteed by the stability constraint that utilizes the ESO-based auxiliary controller and the associated Lyapunov function. Furthermore, the inter-AUV collision avoidance can be achieved by incorporating well-designed artificial potential fields-based cost term in the formation tracking cost function. Extensive simulations on the Saab Falcon AUVs are carried out, demonstrating the superior control performance and robustness of the proposed method.