Department of Software, College of Engineering, Catholic Kwandong University, Gangneung 210-701, Korea.
Department of Beauty Design, College of Media & Art, Catholic Kwandong University, Gangneung 210-701, Korea.
Sensors (Basel). 2022 Jun 10;22(12):4414. doi: 10.3390/s22124414.
A fully autonomous vehicle must ensure not only fully autonomous driving but also the safety and comfort of its passengers. However, the self-driving technology that is currently completed focuses only on perfect driving and does not guarantee the safety and comfort of passengers. This paper proposes a braking-pressure and driving-direction determination system (BDDS), which computes the brake pressure and steering angle optimized for passenger safety by utilizing more diverse information than existing autonomous vehicles. The BDDS proposed in this paper consists of two modules. The road roughness classification module (RRCM) classifies the roughness of the road by using the pressure data applied to the suspension and the K-NN algorithm and computes the optimal brake pressure. The passenger recognition and sharing module (PRSM) identifies the current occupant status of the vehicle by using a body pressure sensor and CNN, shares the information with surrounding vehicles, and computes the optimal steering angle using passenger information and road information. As a result of the simulations described in this paper, the parameters of AI models were optimized. In addition, the RRCS was about 7% more accurate than the K-means clustering algorithm, and PRS was about 9% more accurate than the existing seat recognition system.
一辆完全自动驾驶的汽车不仅必须确保实现完全自动驾驶，还必须确保乘客的安全和舒适。然而，目前完成的自动驾驶技术仅专注于完美的驾驶，而不能保证乘客的安全和舒适。本文提出了一种制动压力和行驶方向确定系统（BDDS），它通过利用比现有自动驾驶汽车更多样的信息，计算出针对乘客安全的优化制动压力和转向角度。本文提出的 BDDS 由两个模块组成。道路粗糙度分类模块（RRCM）通过使用施加在悬架上的压力数据和 K-NN 算法对道路粗糙度进行分类，并计算出最佳的制动压力。乘客识别和共享模块（PRSM）通过使用体压传感器和 CNN 识别车辆当前的乘客状态，与周围车辆共享信息，并根据乘客信息和道路信息计算出最佳的转向角度。通过本文描述的模拟，优化了 AI 模型的参数。此外，RRCS 的准确性比 K-均值聚类算法高约 7%，PRS 的准确性比现有的座椅识别系统高约 9%。
