Chery Automobile (China)

In mixed traffic environments, the spatial distribution of Connected and Automated Vehicles (CAVs) plays a decisive yet previously unquantified role in platoon stability and safety. This study establishes a generalized stability modeling framework for heterogeneous platoons composed of Human-Driven Vehicles (HDVs), Autonomous Vehicles (AVs), and CAVs. By introducing a spatial weighting coefficient (γ), the proposed model captures the influence of longitudinal CAV positioning and allows for flexible representation of any vehicle composition pattern. Linear stability analysis and time-domain simulations are conducted to investigate the interaction between spatial distribution, communication delay, and dynamic response. The results demonstrate that front- and center-loaded CAV configurations effectively suppress velocity perturbations and maintain string stability even under moderate delay, while rear-loaded configurations exhibit early instability. Furthermore, an optimal γ range of 0.3-0.5 is identified to minimize oscillation amplitude, providing a practical guideline for cooperative control strategies in mixed platoons. The findings offer theoretical insights and quantitative evidence for optimizing CAV deployment to enhance stability and robustness in future intelligent transportation systems.

The composition, structure, and dynamics of a transient ice sheet that forms and disintegrates on a boreal lake is influenced by meteorological and environmental processes. This includes trapping of upwelling methane from the lake sediments, which is in turn affected by eutrophication in the catchment area. Methane is a potent greenhouse gas, yet documented sources and sinks to the atmospheric budget are highly unbalanced. Here we explore a novel approach for quantifying methane ebullition from a boreal lake that combines seismic methods together with interdisciplinary observation methods. The DYNALake project centerpiece is an array of ~210 seismic geophones arranged in an aperiodic tiling configuration that we deployed in February 2025 on the ~20 cm thick ice of Lake Pääjärvi some 100 km north of Helsinki. The 10-km scale lake array is complemented by a sparser network of 31 land-based sensors installed around the lake between fall 2024 and spring 2025, three dense circular arrays enabling local beamforming and estimating array derived rotation, a DAS system with a 1 km-long fibre optic cable, an underwater echosounder to monitor potential methane ebullition, a rotational seismometer, a microphone to record seismo-acoustic waves, a Ground Penetrating Radar (GPR) survey, water chemistry measurements, manual ice thickness sampling and ice coring, and meteorological data. The project popularizes the subarctic wintertime fieldwork and the science by making a professional documentary for science communication, outreach, and education. We present initial results on spatial and temporal variations in lake-ice thickness and on the quality and characteristics of the recorded seismic data. The observations include distinct ice-guided wavefield signatures, including QS₀ (quasi-symmetric) and SH₀ (horizontally polarized shear) modes used to estimate elastic parameters such as Young’s modulus and Poisson’s ratio, as well as the dispersive QS (quasi-Scholte) mode that is primarily sensitive to ice thickness at higher frequencies. We compare signals from natural sources and hammer shots across the different sensor types. We show examples of noise correlation wavefields, beamforming results, and seismo-acoustic records that can be used to characterize seismic activity patterns and resolve variable ice properties. Seismic activity in the 0.03–0.2 Hz band increases during high-wind episodes, while higher-frequency signals (0.1–1000 Hz) correlate with rapid air-temperature cooling events. The GPR profile images the spatial ice variability across the lake that is compatible with the in situ measurements. The geochemical water sample analysis suggests Lake Pääjärvi is a source of methane. We discuss the potential of the data quality and the sensor configuration for signal detection and for icequake and passive tomography lake ice images to resolve spatially variable air and gas bubble properties that are controlled by environmental processes. This synthesis demonstrates that the application of environmental seismology concepts can form a bridge between bottom-up ebullition monitoring and remote-sensing approaches.

To address the challenges of insufficient mapping integrity, compromised real-time performance, and reduced robustness in visual Simultaneous Localization and Mapping (SLAM) systems for robots operating under varying lighting conditions, this study proposes a multi-robot cooperative SLAM method based on adaptive weighted LiDAR and visual fusion. Building upon the ORB-SLAM3 framework, the proposed approach integrates depth information from LiDAR to enhance the 3D perception capability of visual feature points. By improving the Extended Kalman Filter (EKF)algorithm, the method achieves efficient fusion of high-precision LiDAR ranging data and temporal information from visual sensors, thereby improving the pose estimation accuracy of individual robots. On this basis, combined with depth map generation techniques, a local dense point cloud map is constructed for each robot. Through a key frame matching mechanism and the Iterative Closest Point (ICP) registration algorithm, a coordinate transformation relationship is established between robots with overlapping fields of view. These local maps are then unified into a global coordinate system to accomplish the integration and construction of multi-robot maps. To validate the effectiveness of the proposed method, comprehensive experiments were conducted in the Gazebo simulation environment. The results demonstrate that the method significantly enhances the stability and environmental adaptability of the mapping process while maintaining real-time performance

Simultaneous Localization and Mapping (SLAM) is a critical research topic in autonomous driving, robotics, and related fields. Although LiDAR-based SLAM methods achieve excellent performance, they primarily extract feature points from the geometric structure of point clouds, failing to mitigate the adverse effects of noise interference on feature matching, which leads to inaccurate localization in complex environments. In this article, we present Saliency-LOAM, a novel LiDAR odometry method based on point cloud saliency. The framework proposes Saliency-Net, a feature extraction network that integrates geometric, intensity, and semantic information of point clouds to effectively predict the saliency information of different regions in 3D scenes. It assigns higher saliency weights to regions with greater feature stability. During the feature matching process, saliency weights are utilized to adjust the distance residual term, ensuring that the optimization equation focuses on more stable and higher-quality point cloud regions, thereby iteratively optimizing to achieve more accurate pose estimation. Experimental results on public datasets and real-world scenarios show that, compared to existing methods, the point cloud saliency mechanism better suppresses the negative impact of noise in raw point cloud data on point cloud matching, significantly improving the robustness and accuracy of LiDAR SLAM systems. Our code will be open-sourced at https://github.com/ck-png/Saliency-LOAM.

In order to improve the precision and reliability of the objective evaluation of vehicle vibration, a multi-condition drivability test is designed based on the GB/T4970-2009 standard. The three-axis vibration and acceleration data in the speed range of 30–120 km/h are collected from urban roads, national highways, and motorways. Combined with the improved empirical modal decomposition (EMD) method, a vibration signal optimization strategy with dynamic frequency band adjustment and double threshold screening is proposed. The empirical modal decomposition algorithm solves the problem of modal aliasing, establishes a speed-based frequency band adjustment mechanism and a suspension intrinsic frequency model, and constructs a dual-threshold filtering mechanism with instantaneous frequency probability density (≥85%) and energy entropy criterion, which effectively separates the driver operation from the noise interference. Compared with various improvement methods, the EMD dynamic dual-threshold method has the best correction effect, and the correction error of the integrated weighted acceleration root mean square value is reduced to 0.191%. The method breaks through the severe limitations of the traditional test conditions and provides a highly robust analysis framework for vehicle vibration comfort evaluation.