Navigating complex urban intersections remains a significant challenge for autonomous vehicles due to highly dynamic and dense traffic environments. This paper presents a novel trajectory planning framework designed to address these challenges by integrating a dual-layer probabilistic rad-lane intention prediction model with an efficient sampling-based trajectory planner. The proposed dual-layer model comprises three components: short-term vehicle kinematic prediction-based road-level intention inference, Interactive Multiple Model (IMM)-based lane-level intention inference, and target-lane-based trajectory generation. This architecture broadens the application scope and validates the effectiveness of the IMM-based multi-model probabilistic fusion framework in urban intersection scenarios. By incorporating both road-level and lane-level contextual information, the proposed trajectory prediction method significantly enhances prediction accuracy. Additionally, a longitudinal sampling strategy based on predefined maneuver modes is employed to improve the probabilistic completeness of existing parametric curve-based sampling techniques, facilitating rapid and effective obstacle avoidance. A notable advantage of the proposed sampling strategy is its ability to efficiently and probabilistically generate safe and feasible trajectories in challenging intersection scenarios. Extensive simulation results demonstrate that the proposed framework outperforms existing methods in terms of both efficiency and safety in urban intersection vehicle conflict scenarios.

In order to solve the problem of the side slip of the wheeled mobile robot adopting the steering trapezium, a novel pure rolling steering system for wheeled mobile robot is designed. It takes advantage of synchronous belt characteristic with long-distance, variable-direction and precise transmission and is equipped with a dual power differential drive axle. This paper details the design points of the whole mechanism; sets up the kinematic models of four-wheeled Ackerman steering, crab movement, and pivot turn modes; and then the steering drive control strategies of various modes of the robot is established. A prototype equipped with a control system consisting of a control center, four steering motors, two drive motors, an electromagnetic clutch and four angle sensors is constructed. Finally, the test results for pivot turn and four-wheel minimum radius steering on the hard road shows that the trajectory slip error are less than 0.2% and 1.7%, respectively. This verifies clearly the correctness and effectiveness of the proposed pure rolling steering system for wheeled mobile robot.

Lunar exploration is progressing rapidly, with craters serving as critical landmarks for precise spacecraft navigation and landing. Although deep learning has enhanced conventional crater identification techniques, persistent limitations include significant computational overhead and pronounced reliance on high-quality data. This study introduces SparseDLE, an efficient real-time crater detector built upon an optimized DETR framework. The method introduces a sparse query selection strategy to progressively refine results from coarse to fine; proposes a morphable grid attention moduleto enhance algorithmic efficiency; and designs a quality match optimizer to improve model accuracy. Experiments demonstrate that the proposed algorithm achieves an F1-score of 84.49%, outperforming existing state-of-the-art methods.

Connected and autonomous vehicles (CAVs) have been deployed rapidly at unstructured intersections. To prevent frequent stops and ensure safety, CAVs should pass through the intersection within recommended departure time windows while adhering to specific motion constraints on terrains with irregular boundaries and slopes. It leads to a multi-vehicle trajectory planning (MVTP) problem at intersection areas, for which a mixed-integer nonlinear programming (MINLP) model with non-differentiable multiple if-else expressions is applicable but difficult to solve efficiently by existing optimizers. To this end, this study proposes a centralized-distributed computing framework to tackle this problem in an efficient manner. Specifically, at the centralized level, an improved conflict-based search (CBS) and hybrid A* algorithm is used to generate coarse trajectories satisfying departure time windows constraints whereas handling unknown if-else expressions. At the distributed level, each CAV performs a simple NLP-based trajectory optimization, with collision-avoidance ensured through linear corridor constraints. Experimental validation and comparisons with benchmarks demonstrate that the effectiveness and efficiency of the proposed framework.

In intersection scenarios, agents exhibit diverse intent choices, making trajectory prediction problems fraught with significant uncertainty. This study proposes a cross-intersection trajectory prediction method that considers the probability of agent intent. It integrates a vehicle speed model, an intent predictor based on agent kinematics, and a trajectory prediction method based on graph neural networks to enhance the accuracy of vehicle agent trajectory predictions by precisely capturing the intent of vehicle agents at intersections. Through training and validation on a large dataset of real-world driving data, experiments have demonstrated the method's capability to predict the behavior of traffic agents in intersection scenarios accurately. Specific experimental results on the nuScenes dataset show MinADE_5, MinADE_10, MissRate_5,2, and MissRate_10,2 values of 1.70, 1.45, 0.63, and 0.48, respectively.