CAREER: Collision Prediction and Vehicular Control Using an Episode-Based Modeling Framework

职业：使用基于事件的建模框架进行碰撞预测和车辆控制

• 批准号: 1351647
• 负责人: Samer Hamdar
• 金额: $ 40.1万
• 依托单位: George Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-06-01 至 2020-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1351647&HistoricalAwards=false
• 关键词: CAREER; Collision; Prediction; Vehicular; Control

The objective of the Faculty Early Career Development (CAREER) program award is to explore mechanisms for predicting freeway vehicle-to-vehicle collisions and specifying the pre and post-collision maneuvers to improve traffic mobility and safety. This research utilizes concepts from the fields of economics, psychology, and engineering to specify performance measures for traffic system evaluation. In order to realize the research objective, the acceleration and lane-changing driving behaviors that lead to freeway vehicle-to-vehicle collisions will be modeled. A stochastic episode-based framework will be adopted where the driving experience is represented by a series of episodes. Within each episode, a driver accelerates/decelerates in order to maximize his/her preferences and this is represented by a subjective utility function. This function captures risky behavior and collisions. Once collisions occur, another function representing vehicle dynamics will be used to capture the vehicular post-collision movements. The resulting model that integrates the driver's preferences along with vehicle dynamics will be calibrated in real-time using data collected through an instrumented vehicle, a 3-D driving simulator, and a virtual laboratory environment. A simulation tool will be developed to test different pre and post-collision maneuvers that potentially lead to safer and more efficient freeway traffic networks.If successful, the research will help in the development of different passive and active driving assistance systems. The objective of these systems will be to increase driver's responsiveness and to provide a safer driving environment especially for younger drivers. For this reason, high-school and university students in the Greater Washington Metropolitan Area will be involved in the data-collection and educational activities. Traffic safety workshops will be organized. A virtual interactive 3-D environment will be utilized in these workshops while collaborating with local agencies including the National Children's Museum.

学院早期职业发展(Career)项目奖的目标是探索预测高速公路车辆对车辆碰撞的机制，并指定碰撞前和碰撞后的机动措施，以提高交通机动性和安全性。本研究运用经济学、心理学和工程学等领域的概念，对交通系统评价的绩效指标进行了细化。为了实现研究目标，将对导致高速公路车-车碰撞的加速和变道驾驶行为进行建模。将采用基于随机情节的框架，其中驾驶体验由一系列情节来表示。在每一集中，司机为了最大化他/她的偏好而加速/减速，这由一个主观效用函数表示。此函数捕获危险行为和冲突。一旦发生碰撞，将使用另一个表示车辆动力学的函数来捕获碰撞后的车辆运动。由此产生的模型将整合驾驶员的偏好和车辆动力学，将使用通过仪表化车辆、3-D驾驶模拟器和虚拟实验室环境收集的数据进行实时校准。将开发一种模拟工具来测试不同的碰撞前和碰撞后操作，这些操作可能会导致更安全和更高效的高速公路交通网络。如果成功，这项研究将有助于开发不同的被动和主动驾驶辅助系统。这些系统的目标将是提高司机的反应能力，并提供一个更安全的驾驶环境，特别是对年轻司机来说。因此，大华盛顿大都市区的高中生和大学生将参与数据收集和教育活动。将组织交通安全讲习班。这些讲习班将利用虚拟互动3-D环境，同时与包括国家儿童博物馆在内的当地机构合作。