Characteristics and a Safety Analysis of Driver's Free Lane-changing Behavior in a Virtual Reality-based Connected Environment
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摘要: 传统驾驶模拟器难以精确模拟车联网环境中的复杂交互，如车速变化和车道变更。而连接虚拟现实（virtual reality，VR）的驾驶模拟器可以通过先进的传感器和实时数据处理，更逼真地还原车辆物理特性、交通流动态及实际道路环境。采用虚拟现实设备和驾驶模拟器，深入探索网联环境下的驾驶人自由换道行为与安全特征。基于交通仿真和三维建模技术搭建驾驶人自由换道实验系统，并建立自由换道场景库，进而开展驾驶人自由换道行为实验；基于广义估计方程建立驾驶人换道的间距选择模型和换道时间模型；基于加速失效模型分析网联环境对驾驶人自由换道行为的安全影响。研究结果表明：①网联环境下，女性驾驶人的自由换道间距与时间更长，年轻驾驶人的自由换道间距与时间更短；②每提高1 m/s2的加速度噪声，自由换道时的碰撞风险降低28%，每提高1 m的自由换道间距，自由换道时的碰撞风险增加1.1%；③年龄较大的驾驶人自由换道安全性更高，其中，中老年驾驶人（＞40岁）自由换道时的碰撞时间（time-to-collision，TTC）比青年驾驶人（＞27~40岁）、年轻驾驶人（＞18~27岁）分别高38.3%，64.3%；④女性驾驶人比男性驾驶人自由换道安全性更高，女性驾驶人自由换道时的TTC比男性驾驶人高20.1%。与普通环境相比：①驾驶人在网联环境下自由换道间距增加1.16 m、换道时间增加2.41 s、换道安全水平提高19.72%；②自由换道事故发生概率随着碰撞风险持续时间增加而降低，其中，碰撞风险持续时间为1，2，3，4 s时，网联环境下的自由换道事故发生概率比普通环境下分别低5.8%，17.2%，14.4%，3.0%，且该概率在不同性别、年龄的驾驶人自由换道中差异显著。Abstract: Traditional driving simulators need help to accurately simulate complex interactions, such as speed variations and lane changes in connected vehicle environments. The connected virtual reality (VR) driving simulator can more realistically replicate vehicle physical characteristics, traffic flow dynamics, and actual road environments using advanced sensors and real-time data processing. A driving simulation system for free lane-changing experiments is developed using traffic simulation and 3D modeling technologies, based on which a scenario library is established and further carry out experiments about free lane-changing behavior. Generalized estimating equations is adopted to establish models of gap selection and lane-changing time. An accelerated failure time model is adopted to analyze the safety impact of the connected environment on free lane-changing behavior. The results can be concluded in two aspects. In connected environments: ① Female drivers exhibit longer lane-changing gaps and need more time. Younger drivers show shorter gaps and need less time. ②An increase of 1 m/s2 in acceleration noise can reduce collision risk by 28% during lane changes, and a 1 m increase in lane-changing gap can increase collision risk by 1.1%.③Older drivers have a higher level of lane-changing safety. Middle-aged and elderly drivers (> 40 years old) show 38.3% and 64.3% higher regarding time-to-collision (TTC) than young (> 27~40 years old) and younger drivers (> 18~27 years old) do. ④Female drivers have a higher level of lane-changing safety than male drivers do, with a 20.1% higher of TTC during free lane-changes. Compared to non-connected environments: ①Drivers in connected environments show a 1.16 m increase in lane-changing gap, a 2.41 s increase in lane-changing time and a 19.72% improvement in the level of safety. ②The probability of occurring lane-changing accidents decreases with the increase of collision risk durations. Specifically, it reduces by 5.8%, 17.2%, 14.4%, and 3.0% at 1, 2, 3, and 4 s of collision risk duration, respectively. These probabilities vary significantly across drivers'genders and ages.
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图 8 自由换道不同驾驶状态的生存曲线
Figure 8. The survival curves of free lane-changing under different driving conditions
图 9 自由换道不同年龄驾驶人的生存曲线
Figure 9. The survival curves of free lane-changing for different age groups
图 10 自由换道不同性别驾驶人的生存曲线
Figure 10. The survival curves of free lane-changing for different genders
表 1 20号实验者换道实验数据
Table 1. The lane-changing experiment data from participant number 20
速度（/km/h） 普通环境 41 速度（/km/h） 普通环境 41 网联环境 43 当前车道前车间距/m 普通环境 45 网联环境 51 加速度噪声（/m/s） 普通环境 0.9 网联环境 0.8 目标车道后车间距/m 普通环境 33 网联环境 30 距离冲突时间TTC/s 普通环境 5 网联环境 7 换道持续时间/s 普通环境 7 网联环境 8 
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表 2 广义估计方程的自变量统计
Table 2. Explanatory variables statistics for generalized estimating equations
指标 变量 变量赋值 计数 占比/% 驾驶状态 普通环境 普通环境为1，否则为0 51 50.0 网联环境 网联环境为1，否则为0 51 50.0 速度（/km/h） 在换道行为期间的平均速度 驾驶参数 加速度噪声/（m/s） 换道开始点至执行点间的加速度标准差 换道间距选择/% 实验者选择换道间隙的情况 年龄/岁 ＞18~27（年轻） 实验者是年轻为1，否则为0 18 25.3 ＞27~40（青年） 实验者是青年为1，否则为0 26 51.0 > 40（中老年） 实验者是中老年为1，否则为0 7 13.7 性别 男 实验者性别男为1，否则为0 32 62.7 女 实验者性别女为1，否则为0 19 37.3
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表 3 自由换道GEE间距选择模型分析结果
Table 3. The analysis results of the GEE model for free lane-changing distance selection
模型 变量 系数Wald卡方值 显著性 间距选择模型 常数 3.08 219.25 < 0.001 网联环境（参照：普通环境） 1.16 5.64 0.013 速度 0.08 26.39 0.052 加速度噪声 -0.19 5.26 0.038 年龄：年轻（参照：青年） -0.17 4.18 < 0.001 年龄：中老年（参照：青年） 0.23 4.02 0.036 性别：女（参照：男） 0.15 3.97 0.029 Alpha 0.26
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表 4 自由换道GEE换道时间模型分析结果
Table 4. The analysis results of the GEE lane-changing time model for free lane-changing
模型 变量 系数 Wald卡方值显著性 换道时间模型 常数 3.92 7.03 0.009 网联环境（参照：普通环境） 2.41 5.94 0.014 速度 0.15 4.06 0.038 换道间距 0.04 8.82 0.005 年龄：年轻（参照：青年） -1.97 3.79 0.047 年龄：中老年（参照：青年） 3.41 14.05 < 0.001 性别：女（参照：男） 2.54 4.63 0.06 Alpha 0.46
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表 5 自由换道加速失效模型拟合结果
Table 5. The fitting results of the free lane-changing acceleration failure model
变量 系数 z 显著性exp(β) exp(β) 95%置信区间 上限 下限 常数 0.49 2.16 0.026 / / / 网联环境 0.18 1.98 0.042 1.23 1.846 2.108 加速度噪声 -0.22 -2.39 0.031 0.69 1.857 2.063 换道间距 0.011 4.15 0.004 1.03 1.792 2.034 年龄：年轻 -0.23 2.14 0.027 0.78 1.814 2.036 年龄：中老年 0.32 2.39 0.022 1.34 1.773 2.139 性别：男 -0.19 -2.13 0.031 0.81 1.876 2.043 P 3.16 0.875 3.065 θ 0.73 1.856 2.084 LL(0) -72.36 $ L L(\hat{\beta})$ -42.58 似然比统计 57.13 AIC 93
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