Mitigating Motion Sickness in Automated Vehicles with Vibration Cue System

<p>Motion sickness is very common in road transport. To guarantee ride comfort and user experience, there is an urgent need for effective solutions to motion sickness mitigation in semi- and fully-automated vehicles. Considering both effectiveness and user-friendliness, a vibration cue system is proposed to inform passengers of the upcoming vehicle movement through tactile stimulation. By integrating the motion planning results from automated driving algorithms, the vibration cueing timing and patterns are optimized with the theory of motion anticipation. Using a cushion-based prototype of vibration cue system, 20 participants were invited to evaluate this solution in two conditions of driving simulator experiments. Results show that with the proposed vibration cue system, it could also help participants to comprehend the cues and to generate motion anticipation. The participants’ motion sickness degrees were significantly lowered. This research may serve as one foundation for the detailed system development in practical applications.</p><p>(This article has been accepted for publication in <i>Ergonomics</i>, published by Taylor & Francis.)</p><br>

Velocity Obstacle-Based Collision Avoidance and Motion Planning Framework for Connected and Automated Vehicles

This study proposes a novel collision avoidance and motion planning framework for connected and automated vehicles based on an improved velocity obstacle (VO) method. The controller framework consists of two parts, that is, collision avoidance method and motion planning algorithm. The VO algorithm is introduced to deduce the velocity conditions of a vehicle collision. A collision risk potential field (CRPF) is constructed to modify the collision area calculated by the VO algorithm. A vehicle dynamic model is presented to predict vehicle moving states and trajectories. A model predictive control (MPC)-based motion tracking controller is employed to plan collision-avoidance path according to the collision-free principles deduced by the modified VO method. Five simulation scenarios are designed and conducted to demonstrate the control maneuver of the proposed controller framework. The results show that the constructed CRPF can accurately represent the collision risk distribution of the vehicles with different attributes and motion states. The proposed framework can effectively handle the maneuver of obstacle avoidance, lane change, and emergency response. The controller framework also presents good performance to avoid crashes under different levels of collision risk strength.

Special Issue: Optimal Energy Management and Control in Connected and Automated Vehicles (CAVs)

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A Cooperative Positioning Method of Connected and Automated Vehicles with Direction-of-Arrival and Relative Distance Fusion

The rapid development of science and technology has created favorable conditions for Connected and Automated Vehicles (CAVs). Accurate localization is one of the fundamental functions of CAV to realize some advanced operations such as vehicle platooning. However, complicated urban traffic environments, such as the flyover, significantly influence vehicular positioning accuracy. The inability of CAV to accurately perceive self-localization information has become an urgent issue to be addressed. This paper proposed a novel cooperative localization method by introducing the relative Direction-of-Arrival (DOA) and Relative Distance (RD) into CAV to improve the localization accuracy of CAV in the multivehicle environment. First, the three-dimensional positioning error model of the host vehicle concerning adjacent vehicles in azimuth angle and pitch angle and intervehicle distances under the vehicle-to-vehicle communication was established. Second, two least-squares estimation algorithms, linear and nonlinear, are established to decrease the position errors by combining relative DOA and RD measurement information. To verify the proposed algorithm's effect, the PreScan-Simulink joint simulation is carried out. The results show that the host vehicle's localization accuracy by the proposed method can be improved by 25% compared with direct linearization. Besides, by combining relative DOA and relative RD measurement, the locating capability of the least-square-based nonlinear optimization method can be enhanced by 22%.