Visually induced motion sickness (VIMS) is evoked by conflicting motion sensory signals within the brain. Use of the simulator sickness questionnaire (SSQ) or postural stability measures to quantify one's VIMS experience only measures the changes between pre- and post-experiment. The motion sickness susceptibility questionnaire (MSSQ) is widely used to measure individual's sensitivity to motion sickness, but its applicability to VIMS has not been proven. We are introducing a novel VIMS susceptibility measure by combining measures of the subject's "sensitivity" and "endurance" to VIMS. The proposed VIMS susceptibility measure was tested for various VIMS inducing conditions, and demonstrated its effectiveness by conducting both between-subjects and within-subject comparisons for different VIMS conditions.
Modern day vehicles and especially driver assisted cars rely heavily on advanced sensors for navigation, localization and obstacle detection. Two of the most important sensors are the Inertial Measurement Unit and the Global Positioning System devices. The former is subject to wheel slippage and rough terrain, while the latter can be noisy and dependent on good satellite signals. The addition of camera sensors enables the usage of visual data for navigation tasks such as lane tracking and obstacle avoidance, localization tasks such as motion and pose estimation, and for general mapping and path planning. The proposed approach in this paper allows camera systems to work in conjunction with or replace both Inertial Measurement Unit and the Global Positioning System sensors. The proposed visual odometry and deep learning localization algorithms improve navigation and localization capabilities over current state-of-the-art methods. These algorithms can be used directly in today's advanced driver assistance systems, and take us one step closer towards full autonomy.
We present a line-scan stereo system and descriptor-based dense stereo matching for high-performance vision applications. Additionally we introduce a post-processing step based on total variation (TV) regularization for robust disparity estimation. Descriptor-based matching utilizes the Stochastic Binary Local Descriptor (STABLE). The performance of STABLE was shown to be superior to other binary descriptors, both w.r.t. stereo reconstruction quality as well as runtime performance. Regularized estimation of disparity maps is suggested as a hierarchical and iterative post-processing procedure where the Pseudo-Huber-TV norm was employed. We describe the hardware setup consisting of two line-scan cameras mounted in a car trailer and observing the road surface. Presented are results of 3D road surface reconstruction which are used in applications of road infrastructure maintenance.
Generating a disparity map has been a challenging issue for several decades. To improve the quality of estimated disparity map and reduce the computational complexity, efficient cost matching functions and cost aggregation methods have been developed. Especially, in case of sequential stereo matching procedure, computational complexity causes a problem in terms of the real time processing. To overcome this problem, we propose a temporal domain stereo matching method using the guided image filtering. The advantage of temporal stereo matching method is restricting a disparity search range while calculating a matching cost value along the horizontal pixel line. Additionally, we adopt the guided image filtering to improve the quality of estimated disparity map in updating procedure. Since the guided image filtering aggregates the cost value by considering object boundary region, the result of stereo matching accuracy is improved than conventional temporal stereo matching method. From the experiment results, we check that the proposed method generates the most accurate disparity map than conventional method.