With the advancement of autonomous driving technology, especially the growing adoption of SAE Level 3 and above systems, drivers are transitioning from active controllers to supervisors who must take over in emergencies. For hearing-impaired drivers in a fatigued state, conventional voice alert systems often fail to provide timely and effective warnings, increasing safety risks. This study proposes an adaptive in-vehicle interface that combines visual and tactile feedback to address these challenges. Experiments were conducted to evaluate response accuracy, reaction time, and cognitive load under varying levels of driver fatigue. The findings show that the integration of visual and tactile cues significantly improves takeover efficiency and reduces mental strain in fatigued drivers. These results highlight the potential of multimodal designs in enhancing the safety and driving experience for hearing-impaired individuals. By providing practical strategies and evidence-based insights, this research contributes to the development of more inclusive and effective interaction designs for future autonomous driving systems.

In complex environments, communication networks within an unmanned ground vehicle (UGV) swarm are vulnerable to disruption due to terrain-induced blockage, high node mobility, and limited communication ranges. Employing an unmanned aerial vehicle (UAV) as a mobile relay provides an effective means of maintaining reliable communication. However, under partially observable and high-noise conditions, noisy observations may fail to accurately reflect the actual motion states of ground nodes, thereby degrading the reliability of UAV relay decisions. To address this issue, this paper proposes a Kalman filter–Soft Actor–Critic (KF-SAC) algorithm. This method employs a Kalman filter to fuse the motion models of the ground nodes with noisy measurements, thereby obtaining posterior estimates of motion states, such as position and velocity, as well as the corresponding motion trends. By feeding these estimates into the SAC agent as state inputs, the method improves the reliability of UAV relay trajectory decisions and enhances adaptability to dynamic and noisy environments. Simulation results demonstrate that, compared with standard SAC, the proposed method effectively reduces the overall communication cost of the UGV swarm and improves communication quality amidst complex terrain and noisy observation conditions.