With the rapid development of intelligent transportation systems, lane detection and traffic sign recognition have become critical technologies for achieving full autonomous driving. These technologies offer crucial real-time insights into road conditions, with their precision and resilience being paramount to the safety and dependability of autonomous vehicles. This paper introduces an innovative method for detecting and recognizing multi-lane lines and intersection stop lines using computer vision technology, which is integrated with traffic signs. In the image preprocessing phase, the Sobel edge detection algorithm and weighted filtering are employed to eliminate noise and interference information in the image. For multi-lane lines and intersection stop lines, detection and recognition are implemented using a multi-directional and unilateral sliding window search, as well as polynomial fitting methods, from a bird’s-eye view. This approach enables the determination of both the lateral and longitudinal positioning on the current road, as well as the sequencing of the lane number for each lane. This paper utilizes convolutional neural networks to recognize multi-lane traffic signs. The required dataset of multi-lane traffic signs is created following specific experimental parameters, and the YOLO single-stage target detection algorithm is used for training the weights. In consideration of the impact of inadequate lighting conditions, the V channel within the HSV color space is employed to assess the intensity of light, and the SSR algorithm is utilized to process images that fail to meet the threshold criteria. In the detection and recognition stage, each lane sign on the traffic signal is identified and then matched with the corresponding lane on the ground. Finally, a visual module joint experiment is conducted to verify the effectiveness of the algorithm.

This article proposes a tracking model for non-holonomic constraint robots, enabling the realization of leadersteered rigid geometry formations. By employing cameras and local leader-based approaches, the challenge posed by traditional separation-bearing control methods, which are incapable of establishing rigid formation for both translational and rotational control, is resolved. In addition, to maintain the connectivity of the sensing topology, the field-of-view (FOV) constraints of the on-board cameras are integrated into the controller design. A conversion approach is used to translate the FOV constraints into a rigid geometry formation. Additionally, there is a trade-off between visibility constraints and the leader-steered rigid geometry formation, particularly when the trajectory of the global leader has significant curvature. To address this problem, a continuously smooth transition function is employed. Ultimately, a fixedtime distributed control protocol and distributed observers are developed to realize the formation framework. Experimental results demonstrate that the proposed control protocol effectively achieves rigid geometric formations and satisfies FOV constraints.

This study proposes an enhanced integration framework for the global navigation satellite system (GNSS) and inertial navigation system (INS). The framework combines real-time differential GNSS corrections with an adaptive extended Kalman filter (EKF) to address positional accuracy and system robustness challenges in practical navigation scenarios. The proposed method dynamically compensates for positioning inaccuracies and sensor drift by integrating differential GNSS corrections to reduce errors and employing an adaptive EKF to address temporal synchronization discrepancies and misalignment angle deviations. Simulation and experimental results demonstrate that the framework keeps horizontal positioning error within 2 m and achieves a maximum accuracy improvement of 4.2 m compared to conventional single-point positioning. This low-cost solution ensures robust performance for practical autonomous navigation scenarios.

This article presents a formation control strategy for nonholonomic wheeled mobile robots that integrates both rigid and flexible formations to achieve efficient exploration and robust obstacle avoidance. A two-layer constraint framework is introduced to ensure safe and coordinated formation control. Specifically, the outer layer maintains the perception-based topology by enforcing field-of-view constraints, while the inner layer constructs control barrier functions based on directional obstacle models defined in the robot's local frame, which prevent interrobot collisions and enable safe navigation in unknown environments. Furthermore, the proposed controller guarantees predefined-time convergence and maintains formation objectives even in the presence of unknown obstacles, by using a Lipschitz continuous projection operator to enable smooth transitions between rigid and flexible formations without violating FOV constraints. Finally, the presented framework is validated through a real-world experiment, which confirms its ability to achieve coordinated formation tracking and obstacle avoidance in a complex environment.

This article investigates formation control of nonholonomic mobile robots equipped with heterogeneous sensors, with particular emphasis on resilience against failures in the sensor network topology. The system ensures seamless reconfiguration to an alternative sensing source under visual occlusion, thereby maintaining stability despite abrupt state variations. To maintain formation integrity under varying sensing conditions, a dual-strategy framework is adopted: it employs a high-precision sensing topology in normal operation and switches to IMU-based position estimation with topology preservation when vision sensing becomes unavailable, with the transition governed by a triggering condition derived from the active sensing set. Such transitions, which inevitably induce state jumps and potentially destabilize the control process, are addressed by integrating an adaptive backstepping controller that attenuates these jumps and suppresses unbounded derivatives, thereby ensuring stable control inputs. The formation dynamics are reformulated and integrated with a control Barrier function (CBF)-based scheme to guarantee prescribed-time convergence, while a recovery mechanism is incorporated to handle boundary violations induced by state jumps. Theoretical analysis and hardware experiments corroborate the effectiveness of the proposed framework in suppressing input instability and ensuring stable operation despite sensor failures.