Autonomous driving is currently one of the most popular topics, and computer vision is the key and perception-related technology in this field. Although images have the ability to contain a large amount of crucial feature information, deep learning methods require a significant number of samples, resulting in low data utilization. This paper proposes a learning from demonstration approach that leverages the control laws from human expert drivers to guide a deep convolutional neural network (CNN) in learning features from RGB images captured by the forward-facing camera. Subsequently, the CNN is utilized as a differentiable control layer to obtain an autonomous driving agent with the capabilities of an expert driver. Experimental results of this method are analyzed for designing a lane-keeping control system that learns human driving style via behavioral cloning.

In virtual environments (VEs), users can explore a large virtual scene through the viewpoint operation of a head-mounted display (HMD) and movement gains combined with redirected walking technology. The existing redirection methods and viewpoint operations are effective in the horizontal direction; however, they cannot help participants experience immersion in the vertical direction. To improve the immersion of upslope walking, this study presents a virtual climbing system based on passive haptics. This virtual climbing system uses the tactile feedback provided by sponges, a commonly used flexible material, to simulate the tactile sense of a user's soles. In addition, the visual stimulus of the HMD, the tactile feedback of the flexible material, and the operation of the user's walking in a VE combined with redirection technology are all adopted to enhance the user's perception in a VE. In the experiments, a physical space with a hard-flat floor and three types of sponges with thicknesses of 3, 5, and 8 cm were utilized. We recruited 40 volunteers to conduct these experiments, and the results showed that a thicker flexible material increases the difficulty for users to roam and walk within a certain range. The virtual climbing system can enhance users' perception of upslope walking in a VE.

Immersive Virtual Reality (VR) systems that combine Head Mounted Displays (HMDs) and a position tracking system support the multiple users or participants to collaborate in the same physical space for a large-scale virtual environment. Because the multiple users sharing the same physical space are in a dynamic state, the key technique of multi-user VR system is how to solve the problem of potential collisions among the users who are moving both virtually and physically. In order to better solve the collision problem caused by such dynamic changes, this work presents a new strategy of multi-user redirected walking using dynamic artificial potential fields, which generates repulsion to ‘push’ users away from obstacles and other users, and uses gravity to ‘attract’ users to an open or unobstructed space. In this method, the users not only get repulsive forces from walls, but also from other users and their future states that are called avatars. At the same time, the users will get gravitational force from the steering target. The target selection considers the size of open space, the distance between the steering target and the boundary of physical space, and the distance between the steering target and the center of the physical space. Therefore, the system can steer users to an open area in the physical space to further reduce collisions. To verify the validity of our method, we developed a software to statistically analyze the influence of different factors, such as the physical space size and the number of users. Data from experiments shows that our method reduces the potential user resets by about 20%.