Pedestrian fatalities and injuries most likely occur in vehicle-pedestrian crashes. Meanwhile, engineers have tried to reduce the problems by developing a pedestrian detection function in Advanced Driver-Assistance Systems (ADAS) and autonomous vehicles. However, the system is still not perfect. A remaining problem in pedestrian detection is the performance reduction at nighttime, although pedestrian detection should work well regardless of lighting conditions. This study presents an evaluation of pedestrian detection performance in different lighting conditions, then proposes to adopt multispectral image and deep neural network to improve the detection accuracy. In the evaluation, different image sources including RGB, thermal, and multispectral format are compared for the performance of the pedestrian detection. In addition, the optimizations of the architecture of the deep neural network are performed to achieve high accuracy and short processing time in the pedestrian detection task. The result implies that using multispectral images is the best solution for pedestrian detection at different lighting conditions. The proposed deep neural network accomplishes a 6.9% improvement in pedestrian detection accuracy compared to the baseline method. Moreover, the optimization for processing time indicates that it is possible to reduce 22.76% processing time by only sacrificing 2% detection accuracy.

Current pedestrian navigation applications have been developed for the smartphone platform and guide users on a 2D top view map. The Augmented Reality (AR)-based navigation from the first-person view could provide a new experience for pedestrians compared to the current navigation. This research proposes a marker free system for AR-based indoor navigation. The proposed system adopts the RGB-D camera to observe the surrounding environment and builds a point cloud map using Simultaneous Localization and Mapping (SLAM) technology. After that, a hybrid map is developed by integrating the point cloud map and a floor map. Finally, positioning and navigation are performed on the proposed hybrid map. In order to visualize the augmented navigation information on the real scene seamlessly, this research proposes an orientation error correction method to improve the correctness of navigation. The experimental results indicated that the proposed system could provide first-person view navigation with satisfactory performance. In addition, compared to the baseline without any error correction, the navigation system with the orientation error correction method achieved significantly better performance. The proposed system is developed for the smart glasses and can be used as a touring tool.

It is crucial for an autonomous vehicle to predict cyclist behavior before decision-making. When a cyclist is on real traffic roads, his or her body orientation indicates the current moving directions, and his or her head orientation indicates his or her intention for checking the road situation before making next movement. Therefore, estimating the orientation of cyclist’s body and head is an important factor of cyclist behavior prediction for autonomous driving. This research proposes to estimate cyclist orientation including both body and head orientation using deep neural network with the data from Light Detection and Ranging (LiDAR) sensor. In this research, two different methods are proposed for cyclist orientation estimation. The first method uses 2D images to represent the reflectivity, ambient and range information collected by LiDAR sensor. At the same time, the second method uses 3D point cloud data to represent the information collected from LiDAR sensor. The two proposed methods adopt a model ResNet50, which is a 50-layer convolutional neural network, for orientation classification. Hence, the performances of two methods are compared to achieve the most effective usage of LiDAR sensor data in cyclist orientation estimation. This research developed a cyclist dataset, which includes multiple cyclists with different body and head orientations. The experimental results showed that a model that uses 3D point cloud data has better performance for cyclist orientation estimation compared to the model that uses 2D images. Moreover, in the 3D point cloud data-based method, using reflectivity information has a more accurate estimation result than using ambient information.

The automotive industry is advancing toward fully automated driving, where perception systems rely on complementary sensors such as LiDAR and cameras to interpret the vehicle’s surroundings. For Level 4 and higher vehicles, redundancy is vital to prevent safety-critical failures. One way to achieve this is by using data from one sensor type to support another. While much research has focused on reconstructing LiDAR point cloud data using camera images, limited work has been conducted on the reverse process—reconstructing image data from LiDAR. This paper proposes a deep learning model, named LiDAR Generative Camera (LiGenCam), to fill this gap. The model reconstructs camera images by utilizing multimodal LiDAR data, including reflectance, ambient light, and range information. LiGenCam is developed based on the Generative Adversarial Network framework, incorporating pixel-wise loss and semantic segmentation loss to guide reconstruction, ensuring both pixel-level similarity and semantic coherence. Experiments on the DurLAR dataset demonstrate that multimodal LiDAR data enhances the realism and semantic consistency of reconstructed images, and adding segmentation loss further improves semantic consistency. Ablation studies confirm these findings.

Unmanned aerial vehicles (UAVs) are increasingly used in applications such as inspection and search and rescue, yet safe and intuitive teleoperation remains challenging in cluttered and GPS-denied environments. This paper presents a modular haptic teleoperation framework that integrates Unity, ROS2, optical motion capture, a physical UAV platform, and a stylus-based haptic device for bidirectional human–robot interaction. Operator inputs are mapped to UAV velocity commands, while obstacle proximity is rendered as continuous haptic feedback to enhance spatial awareness. A Control Barrier Function (CBF)-based command filtering layer is incorporated to modify unsafe velocity commands in real time. The system is evaluated through both Unity-based simulation and real-world experiments using a DJI Tello UAV and OptiTrack motion capture. In the virtual experiments, four conditions were compared: baseline, CBF only, haptic only, and CBF + haptic. The combined CBF + haptic condition reduced the average task completion time from 62.9 s to 42.8 s and resulted in no observed collisions under the evaluated virtual scenarios. The real-world experiments further confirmed stable force–distance behavior, bounded latency, and feasible haptic-assisted UAV navigation in a constrained indoor environment. These results indicate that combining haptic feedback with CBF-based safety control can improve teleoperation efficiency, safety, and usability under the tested conditions while providing a practical step toward simulation-to-real haptic UAV teleoperation.