Department of Automotive Engineering, Clemson University, Greenville 29607, SC, USA.
J Safety Res. 2017 Dec;63:145-148. doi: 10.1016/j.jsr.2017.10.009. Epub 2017 Oct 18.
Modern automobiles are going through a paradigm shift, where the driver may no longer be needed to drive the vehicle. As the self-driving vehicles are making their way to public roads the automakers have to ensure the naturalistic driving feel to gain drivers' confidence and accelerate adoption rates.
This paper filters and analyzes a subset of radar data collected from SHRP2 with focus on characterizing the naturalistic headway distance with respect to the vehicle speed.
The paper identifies naturalistic headway distance and compares it with the previous findings from the literature.
A clear relation between time headway and speed was confirmed and quantified. A significant difference exists among individual drivers which supports a need to further refine the analysis.
By understanding the relationship between human driving and their surroundings, the naturalistic driving behavior can be quantified and used to increase the adoption rates of autonomous driving. Dangerous and safety-compromising driving can be identified as well in order to avoid its replication in the control algorithms.
现代汽车正在经历一场范式转变，在这种转变中，驾驶员可能不再需要驾驶车辆。随着自动驾驶汽车驶上公共道路，汽车制造商必须确保自然的驾驶感觉，以获得驾驶员的信心并加速采用率。
本文从 SHRP2 中筛选和分析了一部分雷达数据，重点是根据车辆速度对自然的车头时距进行特征描述。
本文确定了自然的车头时距，并将其与文献中的先前发现进行了比较。
确认并量化了时间车头时距和速度之间的明显关系。个体驾驶员之间存在显著差异，这支持了进一步细化分析的必要性。
通过了解人类驾驶及其周围环境之间的关系，可以量化自然驾驶行为，并提高自动驾驶的采用率。为了避免在控制算法中复制危险和危及安全的驾驶行为，也可以识别这种行为。
