Pedestrian detection plays an essential role in the navigation system of autonomous vehicles. Multisensor fusion-based approaches are usually used to improve detection performance. In this study, we aimed to develop a score fusion-based pedestrian detection algorithm by integrating the data of two light detection and ranging systems (LiDARs). We first evaluated a two-stage object-detection pipeline for each LiDAR, including object proposal and fine classification. The scores from these two different classifiers were then fused to generate the result using the Bayesian rule. To improve proposal performance, we applied two features: the central points density feature, which acts as a filter to speed up the process and reduce false alarms; and the location feature, including the density distribution and height difference distribution of the point cloud, which describes an object’s profile and location in a sliding window. Extensive experiments tested in KITTI and the self-built dataset show that our method could produce highly accurate pedestrian detection results in real-time. The proposed method not only considers the accuracy and efficiency but also the flexibility for different modalities.

Tiltrotor UAV is a hybrid aircraft that enjoys the advantages of both the conventional fixed‐wing and helicopter in a single platform. The design of a practical and efficient flight control system for this novel aircraft with a complex configuration is still a challenge. This paper proposes a practical fixed‐time control scheme for a novel distributed tiltrotor UAV to improve flight stability. Based on the analysis of the control principle, the dynamic model and control allocation are derived to address the coordinated control problems with redundant actuators. A fast fixed‐time sliding mode attitude controller is subsequently designed to improve the response speed and robustness of the control system. Furthermore, a smooth mode transition strategy is developed to guarantee the stable conversion between fixed‐wing and helicopter modes. Experiments are conducted under different modes to investigate the effectiveness of the proposed flight control scheme.

Navigation map generation based on remote sensing images is crucial in fields such as autonomous driving and geographic surveying. Style transfer is an effective method for obtaining a navigation map of the current environment. However, there is lack of robustness of the map-style transfer model, resulting in unsatisfactory quality of the generated navigation maps. To address these challenges, we average the parameters of generators sampled from different iterations with a dense sampling strategy in the Generative Adversarial Network (CycleGAN). The results demonstrate that the training efficiency of our method on the MNIST and generation quality on the Google Map dataset are significantly superior to traditional style transfer methods. Moreover, it performs well in multi-environment hybrid mapping. Our method improves the generalization ability of the model and converts existing navigation maps to other styles of maps precisely. It can better adapt to different types of urban layout and road planning, bringing innovative solutions for traffic management and navigation systems.