In this paper, we present an adaptive walker system designed to address limitations in current intelligent walker technologies. While recent advancements have been made in this field, existing systems often struggle to seamlessly interpret user intent for speed control and lack adaptability across diverse scenarios and terrain. Our proposed solution incorporates high-resolution tactile sensors, deep learning algorithms, IMU sensors, and linear motors to dynamically adjust to the user's intentions and terrain changes. The system is capable of predicting the user's desired speed with an error margin of only 20.99%, relying solely on tactile input from hand and arm contact points. Additionally, it maintains the walker's horizontal stability with an error of less than 1 degree by adjusting leg lengths in response to variations in ground angle. This adaptive walker enhances user safety and comfort, particularly for individuals with reduced strength or cognitive abilities, and offers reliable assistance on uneven terrain such as uphill and downhill paths.

In-car VR applications typically synchronize virtual motion with real vehicle movement to minimize visual-vestibular mismatch. However, this approach limits virtual movement to directions in which the vehicle can physically move, typically restricting the experience to horizontal motion. This study introduces a method to expand the range of virtual motion by simulating vertical movement, leveraging vehicle acceleration to induce a vertical pitch illusion via manipulation of gravitoinertial perception. We conducted a two-phase study evaluating (1) optimal vertical gain values for maximizing perceptual realism in a controlled environment and (2) user experience factors such as motion sickness and presence in an on-road VR flight simulation under realistic driving conditions. Our findings show that users tend to prefer vertical gains that exceed theoretically valid mappings, and highlight the importance of aligning virtual motion with perceived inertial cues to enhance the realism and coherence of vertical motion in in-car VR applications.

Passengers can engage more in nondriving-related tasks owing to recent advancements in autonomous vehicles (AVs), making immersive tools such as virtual reality (VR) appealing; however, motion sickness (MS) remains a significant challenge. We present SYNC-VR, a system that aligns with visual, haptic, and auditory cues and provides proprioceptive feedback to illustrate its effect on MS and presence within the in-vehicle VR. We conducted an experiment with 24 participants using a real vehicle along a route with known MS-triggering events. Using subjective and physiological measures, we assessed participants’ presence and MS under four conditions by gradually varying the level of synchronized input sensations. Results reveal that SYNC-VR reduces MS and increases the sense of presence. Additionally, it emphasizes the impact of our interactive VR content and its role in achieving proprioceptive feedback with haptic feedback through electrical muscle stimulation, introducing an innovative approach to MS mitigation in in-vehicle VR.

As self-driving car technology matures, autonomous vehicle research is moving toward building more human-centric interfaces and accountable experiences. Driving simulators avoid many ethical and regulatory concerns about self-driving cars and play a key role in testing new interfaces or autonomous driving scenarios. However, apart from validity studies for manual driving simulation, the capabilities of driving simulators in replicating the experience of self-driving cars have not been widely investigated. In this paper, we build six self-driving simulation platforms with varying levels of visual and motion fidelities ranging from a screen-based in-lab simulator to the mixed-reality on-road simulator we propose. We compare the sense of presence and simulator sickness for each simulator composition, as well as its visual and motion fidelities with a user study. Our novel mixed-reality automotive driving simulator, named MAXIM, showed highest fidelity and presence. Our findings suggest how visual and motion configurations affect experience in autonomous driving simulators.