研究发现刹车时驾驶员关注的感知线索随刹车强度变化，挑战了传统模型假设。
Kinematic Discriminants of Deceleration Behavior Modes in Car-Following: Evidence from NGSIM Trajectory Data
- 区分环境信息与实际使用信息，用数据识别驾驶行为模式。
- 急刹车时关注车距变化速度，缓刹车时更关注视觉逼近感。
- 车距本身几乎不影响行为判断，适合用于自动驾驶系统设计。
本研究基于NGSIM轨迹数据集中的1,060,119条有效跟车观测（涉及2,932辆车辆），提出两阶段分析框架，区分环境中可测量的运动学变量与实际区分驾驶行为的变量。提取六种运动学特征，分别在-0.5 m/s²和-0.3 m/s²两个减速度阈值下检测减速事件。通过K-means聚类识别行为模式，并使用单因素方差分析与η²效应量评估各特征的判别能力。关键发现：(1) 阈值选择显著影响行为推断——严格阈值下可区分出三个可解释模式，宽松阈值则合并为两个；(2) 急刹车时以车距变化率为主导（η² = 0.715），中等刹车时以视觉逼近感为主导（η² = 0.574）；(3) 车头间距在整个条件下判别力极低（η² ≤ 0.014）。研究结果为感知线索优先级提供了实证依据，对ADAS预警系统与自动驾驶控制具有直接应用价值。
原文摘要 · Abstract (English)
Gap-closing rate and visual looming swap discriminative dominance depending on deceleration intensity - a finding that reconciles a long-standing conflict in the car-following literature and challenges spacing-centered assumptions in traditional driver behavior models. This study presents a two-stage analytical framework that distinguishes between information availability (kinematic variables measurable in the environment) and information utilization (variables that demonstrably separate driver behavioral patterns), applied to 1,060,119 valid car-following observations from the NGSIM trajectory dataset (2,932 vehicles). Six kinematic features are extracted, and deceleration events are detected under two threshold conditions (-0.5 m/s^2 and -0.3 m/s^2). K-means clustering identifies behavioral modes, and one-way ANOVA with eta-squared effect sizes ranks each feature's discriminative power. Three key findings emerge: (1) threshold selection fundamentally shapes behavioral inference - the stricter threshold yields three interpretable modes while the permissive threshold collapses these to two; (2) hard braking prioritizes gap-closing rate (eta^2 = 0.715) while moderate braking emphasizes visual looming (eta^2 = 0.574); and (3) spacing headway is negligible (eta^2 <= 0.014) across both thresholds. These findings provide empirically grounded candidates for perceptual cue prioritization and have direct implications for ADAS warning system design and autonomous vehicle control.
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