The collision avoidance capability of autonomous vehicles in extreme traffic conditions remains a focal point of research. This paper introduces an Adaptive Cruise Control (ACC) strategy based on Model Predictive Control (MPC) and Responsibility-Sensitive Safety (RSS) models. Simulations were conducted in the CARLA environment, where the lead vehicle underwent various rapid deceleration scenarios to optimize the following vehicle’s braking strategy. By integrating the multi-step predictive optimization capabilities of MPC with the dynamic safety assessment mechanisms of RSS, the proposed strategy ensures safe following distances while achieving rapid and precise speed adjustments, thereby enhancing the system’s responsiveness and safety. The model also incorporates a secondary optimization to balance comfort and stability, thereby improving the overall performance of autonomous vehicles. The use of multi-dimensional assessment metrics, such as Time to Collision (TTC), Time Exposed TTC (TET), and Time Integrated TTC (TIT), addresses the limitations of using TTC alone, which only reflects instantaneous collision risk. The optimization of the model in this paper aims to improve the safety and comfort of the following vehicle in scenarios with various gap distances, and it has been validated through the SSM multi-indicator approach. Experimental results demonstrate that the improved ACC model significantly enhances vehicle safety and comfort in scenarios involving large gaps and short-distance emergency braking by the lead vehicle, validating the method’s effectiveness in various extreme traffic scenarios.

Unmanned aerial vehicles (UAVs) are increasingly deployed to enhance the operational efficiency of city services. However, finding optimal solutions for the gather–return task pattern under dynamic environments and the energy constraints of UAVs remains a challenge, particularly in dense high-rise building areas. This paper investigates the multi-UAV path planning problem, aiming to optimize solutions and enhance data gathering rates by refining exploration strategies. Initially, for the path planning problem, a reinforcement learning (RL) technique equipped with an environment reset strategy is adopted, and the data gathering problem is modeled as a maximization problem. Subsequently, to address the limitations of stationary distribution in indicating the short-term behavioral patterns of agents, a Time-Adaptive Distribution is proposed, which evaluates and optimizes the policy by combining the behavioral characteristics of agents across different time scales. This approach is particularly suitable for the early stages of learning. Furthermore, the paper describes and defines the “Narrow-Elongated Path” Problem (NEP-Problem), a special spatial configuration in RL environments that hinders agents from finding optimal solutions through random exploration. To address this, a Robust-Optimization Exploration Strategy is introduced, leveraging expert knowledge and robust optimization to ensure UAVs can deterministically reach and thoroughly explore any target areas. Finally, extensive simulation experiments validate the effectiveness of the proposed path planning algorithms and comprehensively analyze the impact of different exploration strategies on data gathering efficiency.

Current deep learning fault diagnosis networks for mechanical equipment may fail to reliably identify out-of-distribution (OOD) faults when samples from previously unseen fault categories are unavailable during model development, leading to the misclassification of such OOD samples as known categories. To address this issue, a deep learning fault diagnosis framework based on the generalized extreme value theory (GEVT) is proposed. First, this framework replaces the traditional label with one sample, providing waveform-level evidence for the resulting decisions, enabling users to intuitively confirm results during both training and testing. Then, classification is performed based on the mean absolute distance (MAD) between network predictions and sample labels to enhance interpretability. Finally, the GEVT is employed to fit the extreme values of the MAD, and the cumulative distribution function probability is calculated. Based on this probability, a threshold is determined to detect OOD faults. Validation on the PHM2009 gearbox dataset and BJTU-RAO bogie dataset demonstrates that this framework not only effectively identifies previously unseen OOD samples but also maintains high detection accuracy for in-distribution (InD) data.

A systematic review of fault diagnosis techniques specifically for ship rolling bearings operating within the demanding marine environment is still lacking. This gap becomes increasingly critical as the Fourth Industrial Revolution, characterized by the pervasive influence of artificial intelligence (AI), continues to deepen. This article presents a comprehensive review of fault diagnosis methods for ship rolling bearings, covering research achievements up to and including 2026. The review begins by emphasizing the critical role of rolling bearings in marine propulsion systems and outlines their common failure modes. Subsequently, it categorizes and analyzes the predominant diagnostic approaches for shipboard bearings, identifying three primary categories: vibration signal analysis, acoustic emission technology, and wear debris detection techniques. For each category, the review elaborates on: the underlying principles, the current state of research, and the application of key enabling technologies such as sensor systems, signal processing methods, and data analysis algorithms in the context of bearing condition monitoring. By synthesizing these findings and outlining future research trajectories, this review aims not only to advance the field of intelligent fault diagnosis for marine applications but also to stimulate the development of emerging research directions within the broader AI domain.