EAPSI: Object Recognition for the Purpose of Traffic Compliance of Autonomous Vehicles

EAPSI：用于自动驾驶车辆交通合规性的物体识别

• 批准号: 1515589
• 负责人: Joseph Campbell
• 金额: $ 0.51万
• 依托单位: Campbell Joseph
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-01 至 2016-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1515589&HistoricalAwards=false
• 关键词: EAPSI; Object; Recognition; Purpose; Traffic

In order for an autonomous vehicle to effectively navigate a road network, it needs to have accurate information about road elements such as traffic signs, traffic lights, and road markings so that it can comply with local traffic laws. Traditionally this information is gathered through detailed maps or visual recognition, however, these approaches can fail if there is insufficient map data available or on-board sensors are unsuccessful at locating road elements. This project proposes to build a system which can detect these road elements by analyzing the behavior of nearby road vehicles. This research will be performed under the guidance of Dr. Marcelo H. Ang Jr. at the National University of Singapore. Dr. Ang previously co-authored related research and as such will be a source of invaluable expertise, in addition to being a member of a lab with access to a cutting-edge autonomous vehicle platform.Nearby vehicles will be detected by using a combination of LIDAR sensors and cameras which are mounted to an autonomous vehicle platform. Vehicle positions will be extrapolated from the sensor data in conjunction with localization data provided by the vehicle platform, from which features will be extracted and fed into a machine learning classifier. Recent research has shown great success at identifying pedestrian paths by filtering out noise in pedestrian positions via clustering then modelling with a Naive Bayes Classifier, so these techniques will be used in order to identify road elements that lie on a vehicle?s path. The results from the classifier will be used in conjunction with other traditional identification methods in order to improve the overall identification rate of road elements. This NSF EAPSI award is funded in collaboration with the National Research Foundation of Singapore.

为了让自动驾驶汽车有效地在道路网络中导航，它需要有关于道路元素的准确信息，如交通标志、交通信号灯和道路标记，以便它能够遵守当地的交通法规。传统上，这些信息是通过详细的地图或视觉识别来收集的，然而，如果没有足够的地图数据可用或车载传感器无法成功定位道路元素，这些方法可能会失败。本项目提出建立一个系统，可以检测这些道路元素，通过分析附近的道路车辆的行为。本研究将在Marcelo H博士的指导下进行。小安在新加坡国立大学。Ang博士曾参与相关研究，因此将成为宝贵的专业知识来源，此外，他还将成为一个拥有先进自动驾驶汽车平台的实验室的成员。附近的车辆将通过安装在自动驾驶汽车平台上的激光雷达传感器和摄像头的组合来检测。车辆位置将结合车辆平台提供的定位数据从传感器数据中推断出来，从中提取特征并输入机器学习分类器。最近的研究表明，通过聚类过滤行人位置的噪声，然后用朴素贝叶斯分类器建模，在识别行人路径方面取得了巨大的成功，因此这些技术将用于识别车辆上的道路元素？分类器的结果将与其他传统的识别方法结合使用，以提高道路元素的整体识别率。这个NSF EAPSI奖是与新加坡国家研究基金会合作资助的。