Classification of Automated Lane-Change Styles by Modeling and Analyzing Truck Driver Behavior: A Driving Simulator Study
Classification of Automated Lane-Change Styles by Modeling and Analyzing Truck Driver Behavior: A Driving Simulator Study
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DOI：
10.1109/ojits.2022.3222442
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发表时间：
2022
影响因子：
2.6
通讯作者：
Zheng Wang;Muhua Guan;Jin Lan;Bo Yang;T. Kaizuka;Junichi Taki;Kimihiko Nakano
中科院分区：
文献类型：
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作者：
Zheng Wang;Muhua Guan;Jin Lan;Bo Yang;T. Kaizuka;Junichi Taki;Kimihiko Nakano
车道变换是一项要求很高的驾驶任务。许多交通事故都是由错误的操纵引起的。自动变道系统有可能减少驾驶员的工作量，提高驾驶安全性。一个挑战是提高驾驶员对自动化系统的接受度。从人为因素的角度来看，不同风格的自动化系统会提高用户的接受度，因为驾驶员可以在不同的驾驶场景中以不同的风格驾驶。提出了一种通过分析和建模卡车驾驶员行为来设计不同的自动驾驶换道方式的方法。卡车驾驶模拟器的实验，12名参与者进行了识别驾驶员模型参数。将换道方式分为三种类型：激进型、中等型和保守型。建议的自动变道系统进行了评估，另一个卡车驾驶模拟器实验与相同的12个参与者。此外，不同的车道变换决策风格对驾驶员的经验和接受的影响进行了评估，从自我卡车和周围车辆的角度。评价结果表明，不同的换道决策风格可以区分的驾驶员。总的来说，这三种风格被人类驾驶员评价为安全可靠。本研究的主要贡献在于，它为不同驾驶风格的自动驾驶系统的设计提供了见解。此外，这些观察结果可以应用于商业自动化卡车。
Lane change is a highly demanding driving task. A number of traffic accidents are induced by erroneous maneuvers. An automated lane-change system has the potential to reduce the driver workload and improve driving safety. A challenge is to improve the driver acceptance of the automated system. From the perspective of human factors, an automated system with different styles would improve user acceptance, because drivers could drive with different styles in different driving scenarios. This paper proposes a method to design different lane-change styles for automated driving by analyzing and modeling truck-driver behavior. A truck driving simulator experiment with 12 participants was conducted to identify the driver-model parameters. The lane change styles were classified into three types: aggressive, medium, and conservative. The proposed automated lane-change system was evaluated by another truck driving simulator experiment with the same 12 participants. Moreover, the effects of different lane-change decisionmaking styles on the driver experience and acceptance were evaluated from the perspectives of both the ego truck and surrounding vehicles. The evaluation results demonstrate that different lane-change decisionmaking styles can be distinguished by drivers. Overall, the three styles were evaluated by the human drivers as being safe and reliable. The main contribution of this study is that it provides the insights into the design of an automated driving system with different driving styles. Furthermore, these observations can be applied to commercial automated trucks.