Autonomous driving paper index
A Hybrid Human–Vehicle Dynamic Model for Motion Sickness Estimation in Intelligent Driving Scenarios
One-line summary
This paper proposes a motion sickness estimation method that combines vehicle dynamics with human factors.
Engineering notes
Key topics: autonomous driving, autonomous vehicle, perception, prediction. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
With the development of intelligent driving technology, there are more and more complaints about motion sickness from passengers in autonomous vehicles. A precise tool is needed to measure ride comfort. This paper proposes a motion sickness estimation method that combines vehicle dynamics with human factors. It integrates the six-degree-of-freedom SVC model into a comprehensive human-car interaction framework. Real-world experiments collected relevant data from in-vehicle tests, the average accuracy rate of head linear acceleration prediction is 89.1%, and the accuracy rate of angular velocity prediction is 84.6%. The heart rate variability data and participant feedback are consistent with the model prediction. This consistency means the method can capture real passenger motion perception. It helps to improve the ride comfort of autonomous driving.
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