Effective path planning is crucial for autonomous mobile robots navigating complex environments. The “global–local” coupled path planning algorithm exhibits superior global planning capabilities and local adaptability. However, these algorithms often fail to fully realize their potential due to low efficiency and excessive constraints. To address these issues, this study introduces a simpler and more effective integration strategy. Specifically, this paper proposes using a bi-layer map and a feasible domain strategy to organically combine the D*Lite algorithm with the Dynamic Window Approach (DWA). The bi-layer map effectively reduces the number of nodes in global planning, enhancing the efficiency of the D*Lite algorithm. The feasible domain strategy decreases constraints, allowing the local algorithm DWA to utilize its local planning capabilities fully. Moreover, the cost functions of both the D*Lite algorithm and DWA have been refined, enabling the fused algorithm to cope with more complex environments. This paper conducts simulation experiments across various settings and compares our method with A_DWA, another “global–local” coupled approach, which combines A* and DWA. D_DWA significantly outperforms A_DWA in complex environments, despite a 7.43% increase in path length. It reduces the traversal of risk areas by 71.95%, accumulative risk by 80.34%, global planning time by 26.98%, and time cost by 35.61%. Additionally, D_DWA outperforms the A_Q algorithm, a coupled approach validated in real-world environments, which combines A* and Q-learning, achieving reductions of 1.34% in path length, 67.14% in traversal risk area, 78.70% in cumulative risk, 34.85% in global planning time, and 37.63% in total time cost. The results demonstrate the superiority of our proposed algorithm in complex scenarios.

In this paper, we establish the Electric Vehicle Routing Problem with Time Windows Based on Driving Cycles (EVRPTW-DC) to optimize the delivery routing of electric vehicles (EVs). As energy consumption may affect the maximal driving range and the recharging behavior of EVs, we first develop a nonlinear electric energy consumption model based on typical driving cycles of suburban and urban areas, with consideration of vehicle load, travel distance, and speed. An adaptive particle swarm optimization algorithm is then designed to solve the problem. Moreover, we study cases built from the actual operational data of Company J and compare the optimal delivery schemes of EVRPTW-DC and EVRPTW under the traditional linear electric energy consumption law. The results show that our nonlinear energy consumption model, which provides a better simulation of energy consumption, can lead to a more realistic delivery plan. Finally, we explore the applicability of the proposed EVPRTW-DC and discuss the conditions of using a linear electric energy consumption coefficient.

Young drivers are more likely to be involved in traffic accidents. The study aims to explore mechanisms behind distracted driving behaviour, traffic safety environment, driving responsibility, and hazard perception. A conceptual model is proposed based on Stimulus-Organism-Response (S-O-R) theory. The self-reported data from 367 drivers are used to estimate and modify the model based on exploratory factor analysis, structural equation modelling, and bias-corrected bootstrap method. The regression relationships and the mediators have been identified. The traffic safety environment including the traffic enforcement and the driving condition isn’t related to the distracted driving behaviour. The traffic enforcement is associated the driving responsibility, the relationships between the driving responsibility, the hazard perception and the driving condition are significant, and the relationships between the distracted driving behaviour, the driving responsibility and the hazard perception are noteworthy. A positive traffic safety environment is beneficial to the safety of young drivers. The sense of driving responsibility and the self-cognition of hazard perception need attention for the early intervention of young drivers’ distracted driving behaviours.

Low-altitude logistics (LAL), supported by unmanned aerial vehicles (UAVs) and emerging urban air mobility operations within the low-altitude airspace (typically <1000 m), is rapidly reshaping last-mile distribution and time-critical delivery. However, LAL systems remain vulnerable to compound disruptions spanning weather, infrastructure, governance, and cybersecurity. Using a PRISMA-guided protocol, this systematic review synthesizes 1600 peer-reviewed studies published from 2020 to 2025 and combines bibliometric mapping (VOSviewer) with qualitative content analysis to consolidate the knowledge base on low-altitude logistics resilience (LALR). We conceptualize LALR via four coupled pillars, including robustness, adaptability, recoverability, and redundancy. The synthesize evidence across key vulnerability domains consists of platform reliability, communication and infrastructure readiness, regulatory fragmentation, cyber exposure, and weather-driven operational uncertainty. Building on the synthesis, we propose a Technology–Policy–Ecosystem roadmap that links (i) AI-enabled autonomy and risk-aware planning, (ii) adaptive governance tools such as regulatory sandboxes and dynamic airspace/UTM management, and (iii) ecosystem-level interventions, notably public–private partnerships and equity-oriented service design for underserved areas. We further outline a research agenda centered on measurable resilience metrics, activate redundancy design, climate-adaptive UAV operations, and digital-twin-enabled orchestration for scalable and sustainable LAL ecosystems.

Recognizing 3D objects from point clouds is a crucial technology for autonomous vehicles. Nevertheless, LiDAR (Light Detection and Ranging) point clouds are generally sparse, and they provide limited contextual information, resulting in unsatisfactory recognition performance for distant or small objects. Consequently, this article proposes an object recognition algorithm named Adaptive Scale and Correlative Attention PointPillars (ASCA-PointPillars) to address this problem. Firstly, an innovative adaptive scale pillars (ASP) encoding method is proposed, which encodes point clouds using pillars of varying sizes. Secondly, ASCA-PointPillars introduces a feature enhancement mechanism called correlative point attention (CPA) to enhance the feature associations within each pillar. Additionally, a data augmentation algorithm called random sampling data augmentation (RS-Aug) is proposed to solve the class imbalance problem. The experimental results on the KITTI 3D object dataset demonstrate that the proposed ASCA-PointPillars algorithm significantly boosts the recognition performance and RS-Aug effectively enhances the training effects on an imbalanced dataset.

In today’s environment of Internet+, people’s consumption patterns continue to upgrade, traditional logistics industry to adapt to the development of the times, is committed to the development of innovative logistics situation and the establishment of intelligent logistics. “The Urban freight taxis” is developed with the depth of the Internet out of the new form of short-distance logistics and distribution. “the Urban freight taxis” is committed to solving the logistics of the last mile of the operation, establishing information path between the driver and the customer through the network of vehicles. And, through the innovative starting price plus mileage innovation fee model, making the cost more transparent. Because this kind of form is newly developing, there are many deficiencies in the operation and management, and the development of “taxiing car” and some other car software has a great similarity with "the urban freight taxis.” And in this paper, we analysis matching of goods and car through people to match the car, so as to “the urban freight taxis” to make a reasonable planning development.