Changing Lanes - Using Advance Sensor Technology to Understand Driver Behavior

变道 - 使用先进的传感器技术了解驾驶员行为

• 批准号: 1537423
• 负责人: Benjamin Coifman
• 金额: $ 37.37万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1537423&HistoricalAwards=false
• 关键词: Changing; Lanes; Using; Advance; Sensor

Traffic congestion impedes US competitiveness, needlessly slowing the movement of most persons and goods. The impacts of congestion are diverse, ranging from safety issues to increased costs of goods and services. The total financial cost of congestion in 2011 was $121 billion. To address this challenge we need a deeper understanding of how traffic flows (and at times does not flow). Freeway traffic is inherently difficult to study because there are thousands of vehicles interacting over several miles. While it is clear that lane change maneuvers are an important factor generating turbulence that impedes traffic, the details of how this turbulence forms and grows are beyond the resolution of existing traffic monitoring tools. This research will advance sensor technology to develop the right tools to understand and model the lane change process in detail. With this better understanding, operating agencies will be able to manage the freeways more efficiently and reduce congestion without building more facilities. Thus, using the existing infrastructure more efficiently to reduce the costs of congestion.This research seeks to greatly advance the understanding and microscopic modeling of where, when, how and why vehicles undertake lane change maneuvers on freeways. A lane change maneuver can take over a mile from start to finish before the traffic stream fully adjusts to the change, with driver behavior dependent upon only a few feet difference in the spacing between vehicles. Given the large number of vehicles interacting over long distances it has been virtually impossible to observe the microscopic details of the lane change maneuver process with conventional tools. This research will use advanced sensing technology to develop a deeper understanding of the microscopic factors that give rise to congestion. The work will use hundreds of hours of instrumented probe vehicle data that include positioning and ranging data for the ambient vehicles around the probe out to 80 m, collected on an urban freeway during peak periods with recurring and non-recurring congestion. Preliminary review indicates that these data include over 10,000 lane change maneuvers. This large quantity of microscopic vehicle interactions will be used to develop a deeper understanding and more accurate models of the lane change maneuver process to help develop the right tools necessary to mitigate traffic congestion. Models of lane changing behavior will be crucial as connected vehicle infrastructures are constructed and autonomous vehicles are introduced into manually operated traffic streams.

交通拥堵阻碍了美国的竞争力，不必要地减缓了大多数人员和货物的流动。交通拥堵的影响多种多样，从安全问题到商品和服务成本的增加。2011年，交通拥堵的总财务成本为1210亿美元。为了应对这一挑战，我们需要更深入地了解流量如何流动（有时不流动）。高速公路交通本来就很难研究，因为有数千辆汽车在几英里内相互作用。虽然很明显，车道变换操纵是产生阻碍交通的湍流的重要因素，但这种湍流如何形成和增长的细节超出了现有交通监测工具的分辨率。这项研究将推进传感器技术，开发正确的工具来详细了解和建模变道过程。有了更好的理解，运营机构将能够更有效地管理高速公路，减少拥堵，而无需建造更多的设施。因此，更有效地利用现有的基础设施，以减少costsofcongestation.This研究旨在大大推进理解和微观建模的车辆在何处，何时，如何以及为什么进行车道变换机动的高速公路。在交通流完全适应变化之前，车道变换机动可以从开始到结束占用超过一英里的时间，驾驶员的行为仅取决于车辆之间距离的几英尺差异。由于大量车辆长距离相互作用，实际上不可能用传统工具观察车道变换操纵过程的微观细节。这项研究将利用先进的传感技术，更深入地了解导致拥堵的微观因素。这项工作将使用数百小时的仪器探测车辆数据，其中包括探测器周围80米范围内的周围车辆的定位和测距数据，这些数据是在经常性和非经常性拥堵的高峰期在城市高速公路上收集的。初步审查表明，这些数据包括超过10，000个车道变换操纵。这些大量的微观车辆交互将用于开发更深入的理解和更准确的变道机动过程模型，以帮助开发缓解交通拥堵所需的正确工具。随着联网车辆基础设施的构建以及自动驾驶车辆被引入手动操作的交通流中，车道变换行为的模型将变得至关重要。