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Analysis of drivers' deceleration behavior based on naturalistic driving data

机译:基于自然主义驾驶数据的驱动器减速行为分析

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Objective: 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. Methods: 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. Results: 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. Conclusion: 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.
机译:目的:作为设计用于智能车辆的人类制动控制系统的基础之一,需要理解驱动器的减速行为。驾驶员减速行为有两种模式:(i)制动踏板输入,通过施加制动系统来降低速度; (ii)通过在不按压制动踏板的情况下释放加速器踏板而没有踏板输入,从而通过突出驾驶阻力减速。在以前的研究中,已经研究了驱动器选择驾驶员按下制动踏板的减速行为。然而,释放加速器踏板行为并未受到很多关注。本研究的目的是调查利用自然化驾驶数据调查影响驾驶员选择两种减速模式的因素,这为制动控制系统的设计提供了理论基础。方法:构建逻辑模型以模拟驱动器减速模式,估值为“无踏板输入”或“制动踏板输入”,用于依赖变量。在模型中作为独立变量考虑了诸如灯条件,交叉路口,道路对准,交通流量,交通灯,自由车辆运动状态,铅动阀运动状态,时间升降机和自我车速等因素。结果:393减速事件选自自然驾驶数据,该数据用作回归模型的数据库。结果,发现了6个显着因素来影响驱动器的减速模型,包括交通流量,交叉点,铅车辆运动状态,自我车辆运动状态,自我车辆速度和THW。具体而言,(1)驾驶员选择“没有踏板输入”的可能性随着THW和速度的增加而逐渐增加; (2)当铅载体与静止相比,司机更愿意选择“无踏板输入”。当牵引车辆沿着道路行驶时,这种概率相对较高; (3)在交叉路口选择“没有踏板输入”的可能性高于没有交叉口的道路; (4)随着交通流量的行驶时,选择“无踏板输入”的可能性更高。结论:司机减速行为可分为“无踏板输入”和“制动踏板输入”。以下六个因素显着影响驱动器的减速模式:交通流量,交叉点,铅车辆运动状态,自我车辆运动状态,自我车辆速度和THW。 Logistic回归模型可以量化这六个因素对驱动器减速行为的影响。本研究为ADAS(先进的驾驶辅助系统)和智能控制系统提供了一种理论依据。

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