Evaluation of Lane Departure Correction Systems Using a Stochastic Driver Model
Beijing Institute of Technology（北京理工大学）；University of California at Berkeley（加州大学伯克利分校）；University of Michigan Transportation Research Institute（密歇根大学交通研究所）
评估驾驶员辅助系统的效果和益处对提高系统性能至关重要。本文提出了一种新的框架，通过从自然驾驶数据中重现车道偏离事件来低成本测试和评估车道偏离纠正系统。首先，从密歇根大学交通研究所收集的Safety Pilot Model Deployment (SPMD)数据库中提取了529,096个车道偏离事件。其次，开发了一个由八个随机关键变量组成的随机车道偏离模型，以减少数据描述的维度并提高计算效率。为此，使用有界高斯混合模型(BGM)来描述驾驶员的随机车道偏离行为。然后设计了一个具有瞄准点控制器的车道偏离纠正系统，并从学习的随机驾驶员模型中生成一批车道偏离事件。最后，通过比较有和无纠正控制器的车辆横向偏离区域来评估所开发的评估方法。模拟结果表明，所提出的方法能够有效评估车道偏离纠正系统。
英文摘要：
Evaluating the effectiveness and benefits of driver assistance systems is crucial for improving the system performance. In this paper, we propose a novel framework for testing and evaluating lane departure correction systems at a low cost by using lane departure events reproduced from naturalistic driving data. First, 529,096 lane departure events were extracted from the Safety Pilot Model Deployment (SPMD) database collected by the University of Michigan Transportation Research Institute. Second, a stochastic lane departure model consisting of eight random key variables was developed to reduce the dimension of the data description and improve the computational efficiency. As such, we used a bounded Gaussian mixture model (BGM) model to describe drivers' stochastic lane departure behaviors. Then, a lane departure correction system with an aim point controller was designed, and a batch of lane departure events were reproduced from the learned stochastic driver model. Finally, we assessed the developed evaluation approach by comparing lateral departure areas of vehicles between with and without correction controller. The simulation results show that the proposed method can effectively evaluate lane departure correction systems.
Evaluation of A Semi-Autonomous Lane Departure Correction System Using Naturalistic Driving Data
University of Michigan Transportation Research Institute（密歇根大学交通研究所）；Beijing Institute of Technology（北京理工大学）；University of California at Berkeley（加州大学伯克利分校）
Analysis of mandatory and discretionary lane change behaviors for heavy trucks
University of Michigan（密歇根大学）；University of Michigan Transportation Research Institute（密歇根大学交通研究所）；Division of Safety Research, NIOSH, CDC（美国疾病控制与预防中心国家职业安全与健康研究所安全研究部）
Modeling the Lane-Change Reactions to Merging Vehicles for Highway On-Ramp Simulations
Global Center for Automotive Performance Simulation（全球汽车性能模拟中心）；Honda Research Institute, USA Inc.（美国本田研究所）；Virginia Tech Transportation Institute（弗吉尼亚理工大学交通研究所）
A Survey on Data-Driven Modeling of Human Drivers' Lane-Changing Decisions
School of Systems Science, Beijing Jiaotong University（北京交通大学系统科学学院）；Department of Automation, BNRist, Tsinghua University（清华大学自动化系）；Laboratory for Information and Decision Systems, Massachusetts Institute of Technology（麻省理工学院信息与决策系统实验室）
