Control and Simulation Center, Harbin Institute of Technology, Harbin, China.
State Key Laboratory of Automotive Safety & Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, China.
Traffic Inj Prev. 2020;21(1):42-47. doi: 10.1080/15389588.2019.1707194. Epub 2020 Jan 27.
As one of the bases for designing a humanlike brake control system for the intelligent vehicle, drivers' deceleration behavior needs to be understood. There are two modes for drivers' deceleration behavior: (i) brake pedal input, by applying brake system to reduce the speed; (ii) no pedal input, by releasing the accelerator pedal without pressing the brake pedal, thus decelerating by naturalistic driving resistance. The deceleration behavior that drivers choose to press the brake pedal has been investigated in previous studies. However, releasing the accelerator pedal behavior has not received much attention. The objective of this study is to investigate factors that influence drivers' choice of the two deceleration modes using naturalistic driving data, which provide a theoretical foundation for the design of the brake control system. A logistic model was constructed to model drivers' deceleration mode, valued as "no pedal input" or "brake pedal input" for dependent variables. Factors such as Light condition, Intersection mode, Road alignment, Traffic flow, Traffic light, Ego-vehicle motion state, Lead vehicle motion state, Time headway (THW), and Ego-vehicle speed were considered in the model as independent variables. 393 deceleration events were selected from the naturalistic driving data, which used as the database for the regression model. As a result, 6 remarkable factors were found to influence drivers' deceleration model, which include Traffic flow, Intersection mode, Lead vehicle motion state, Ego-vehicle motion state, Ego-vehicle speed and THW. Specifically, (1) the possibility of drivers choosing "no pedal input" is gradually increasing with the increase of THW and speed; (2) The drivers prefer to choose "no pedal input" when the lead vehicle is decelerating compared to it's stationary. This probability is relatively high when the lead vehicle is traveling along the road; (3) the possibility of choosing "no pedal input" at intersection is higher than roads without intersection; (4) the possibility of choosing "no pedal input" is higher when traveling with more traffic flow. The drivers' deceleration behavior can be divided into "no pedal input" and "brake pedal input." The following six factors significantly affect drivers' choice of deceleration mode: Traffic flow, Intersection mode, Lead vehicle motion state, Ego-vehicle motion state, Ego-vehicle speed and THW. The logistic regression model can quantify the influence of these six factors on drivers' deceleration behavior. This study provides a theoretical basis for the braking system design of ADAS (Advanced Driving Assistant System) and intelligent control system.
作为设计智能汽车拟人制动控制系统的基础之一，需要了解驾驶员的减速行为。驾驶员的减速行为有两种模式：（i）制动踏板输入，通过应用制动系统来降低速度；（ii）无踏板输入，通过松开加速踏板而不踩制动踏板，从而通过自然驾驶阻力减速。在先前的研究中已经研究了驾驶员选择踩制动踏板的减速行为。然而，释放加速踏板的行为并没有受到太多关注。本研究的目的是使用自然驾驶数据研究影响驾驶员选择两种减速模式的因素，为制动控制系统的设计提供理论基础。构建了一个逻辑回归模型来对驾驶员的减速模式进行建模，将“无踏板输入”或“制动踏板输入”作为因变量的值。该模型将光照条件、交叉口模式、道路线形、交通流量、交通信号灯、本车运动状态、前车运动状态、时距（THW）和本车速度等因素作为自变量考虑。从自然驾驶数据中选择了 393 个减速事件，作为回归模型的数据库。结果发现，有 6 个显著因素影响驾驶员的减速模型，包括交通流量、交叉口模式、前车运动状态、本车运动状态、本车速度和 THW。具体来说：（1）随着 THW 和速度的增加，驾驶员选择“无踏板输入”的可能性逐渐增加；（2）与前车静止相比，当前车减速时，驾驶员更倾向于选择“无踏板输入”。当前车在道路上行驶时，这种概率相对较高；（3）在交叉口选择“无踏板输入”的可能性高于无交叉口的道路；（4）在交通流量较大时，选择“无踏板输入”的可能性较高。驾驶员的减速行为可分为“无踏板输入”和“制动踏板输入”。以下六个因素显著影响驾驶员对减速模式的选择：交通流量、交叉口模式、前车运动状态、本车运动状态、本车速度和 THW。逻辑回归模型可以量化这六个因素对驾驶员减速行为的影响。本研究为 ADAS（高级驾驶辅助系统）和智能控制系统的制动系统设计提供了理论依据。
