基于驾驶员制动行为的汽车碰撞风险评估方法
A Method for Vehicle Collision Risk Assessment through Inferring Driver's Braking Actions in Near-Crash Situations
摘要：
潜在车辆碰撞下的驾驶信息和数据为广泛的真实世界观察驾驶员行为和紧急情况下显著影响驾驶安全的相关因素创造了机会。此外，这些数据的可用性还通过评估驾驶员在接近碰撞场景中的行为并提供及时的警告来增强防撞系统（CAS）。这些应用激发了对能够推断驾驶风险、驾驶员/车辆特性和道路环境之间关系的启发式工具的需求。本文采集了大量的真实驾驶数据，构建了一个包含多个“人-车-路”属性的综合数据集。所提出的方法分两步进行。在第一步中，基于变精度粗糙集（VPRS）的分类技术被应用到从现场驾驶数据集提取约简的核心子集，该约简的核心子集表示与驾驶安全评估最相关的本质属性集。第二步，通过引入互信息熵来量化各属性的重要性，设计决策策略，然后通过加权“人-车-路”因素的累加计算出反映实际情况的驾驶风险代表性指标。所提出的方法的性能证明在离线分析的驾驶数据收集在现场试验中，其目的是推断紧急制动行动在未来的短期内。结果表明，我们提出的模型是一个很好的替代提供改进的实时预警，因为它的高预测精度和稳定性。
英文摘要：
Driving information and data under potential vehicle crashes create opportunities for extensive real-world observations of driver behaviors and relevant factors that significantly influence the driving safety in emergency scenarios. Furthermore, the availability of such data also enhances the collision avoidance systems (CASs) by evaluating driver's actions in near-crash scenarios and providing timely warnings. These applications motivate the need for heuristic tools capable of inferring relationship among driving risk, driver/vehicle characteristics, and road environment. In this paper, we acquired amount of real-world driving data and built a comprehensive dataset, which contains multiple "driver-vehicle-road" attributes. The proposed method works in two steps. In the first step, a variable precision rough set (VPRS) based classification technique is applied to draw a reduced core subset from field driving dataset, which presents the essential attributes set most relevant to driving safety assessment. In the second step, we design a decision strategy by introducing mutual information entropy to quantify the significance of each attribute, then a representative index through accumulation of weighted "driver-vehicle-road" factors is calculated to reflect the driving risk for actual situation. The performance of the proposed method is demonstrated in an offline analysis of the driving data collected in field trials, where the aim is to infer the emergency braking actions in next short term. The results indicate that our proposed model is a good alternative for providing improved warnings in real-time because of its high prediction accuracy and stability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
