How to guarantee the effective planning trajectory of autonomous driving for mobile vehicles is the main challenge. This study provides a multi-constraint trajectory planning technique to construct safe, smooth, and dynamically viable trajectories in complicated situations. Firstly, a two-stage sampling path generation algorithm is proposed to obtain a cluster of candidate paths, considering road geometry, vehicle kinematics, and static obstacle avoidance constraints. Secondly, a cost function is designed to select the optimal path based on smoothness, consistency with the reference path, and distance to obstacles. Finally, a speed planning model is developed using convex optimization to allocate speed profiles for each path point with dynamic constraints, including time efficiency, boundary conditions, and dynamic obstacle avoidance. Experimental results on mobile vehicles demonstrate the effectiveness and stability of the proposed trajectory planning algorithm in real-world environments and its ability to handle various typical driving scenarios. The success rate of trajectory planning in the experiments was 94%, with an average planning time of 37.3ms.

Based on the characteristics of big data, the meaning of fog computing, and the spatiotemporal data characteristics of satellite navigation systems, the concept, connotation, and characteristics of Beidou big data were put forward. The Beidou big data processing process was sorted out, the preliminary architecture of the Beidou big data system with fog computing function was built, and finally the fog computing based Beidou big data system was proposed. Big data research content provides technical support for further tapping the value of Beidou satellite navigation system data and realizing the incremental development of satellite navigation systems.

In order to realise autonomous navigation of unmanned platforms in urban or off‐road environments, it is crucial to study accurate, versatile and real‐time road detection methods. This study proposes an adaptive road detection method that combines lane lines and obstacle boundaries, which can be applied to a variety of driving environments. Combining multi‐channel threshold processing, it is robust to lane feature detection under various complex situations. Obstacle information extracted from the grid image constructed by 3D LIDAR point cloud is used for lane feature selection to avoid interference from pedestrians and vehicles. The proposed method makes use of adaptive sliding window for feature selection, and piecewise least squares method for road line fitting. Experimental results on dataset and in real‐world environments show that the proposed method can overcome illumination changes, shadow occlusion, pedestrian, vehicle interference and so on in a variety of scenes. The proposed method has good enough efficiency, robustness and real‐time performance.

In order to stabilize a class of uncertain nonlinear strict-feedback systems with full-state constraints, an adaptive neural network control method is investigated in this paper. The state constraints are frequently emerged in the real-life plants and how to avoid the violation of state constraints is an important task. By introducing a barrier Lyapunov function (BLF) to every step in a backstepping procedure, a novel adaptive backstepping design is well developed to ensure that the full-state constraints are not violated. At the same time, one remarkable feature is that the minimal learning parameters are employed in BLF backstepping design. By making use of Lyapunov analysis, we can prove that all the signals in the closed-loop system are semiglobal uniformly ultimately bounded and the output is well driven to follow the desired output. Finally, a simulation is given to verify the effectiveness of the method.

To reduce the computation and to improve the performance of object detection and tracking algorithm with object appearance variation, a tracker based on sparse eigenbasis is proposed. According to the compressive sensing theory, the objects are described in a low‐dimensional sub‐space representation based on Karhunen–Loeve transform learned online. Meanwhile, combining the Bayesian inference, an adaptive object tracker is presented. First, the authors represent the appearance of the object in a low‐dimensional sub‐space, then the authors obtain the optimal estimation of the state parameters by Bayesian inference. Finally, the authors update the eigenbasis space using the optimal observations. Experimental results show that the proposed method is able to track the objects effectively and robustly under temporary occlusion and large illumination changes.

Lane detection and tracking is the basic component of many intelligent vehicle systems. In this study, a robust multilane detection and tracking method is proposed. Using the measurements provided by an in‐vehicle mono‐camera and a forward‐looking LIDAR, this algorithm can address challenging scenarios in real urban driving situations. The proposed approach makes use of steerable filters for lane feature detection, LIDAR‐based image drivable space segmentation for lane marking points validations and the RANdom SAmple Consensus technique for robust lane model fitting. To improve the robustness of the fitting further, the parallel lanes hypothesis is introduced. The detected lanes initialise particle filters for tracking, without knowing the ego‐motion information. The image processing procedures are carried out in inverse perspective mapping image, because of its convenience for multilane detection. Experimental results indicate that the algorithm in this study has robustness against various driving situations.