探索驾驶环境理解的OpenStreetMap可用性
Exploring OpenStreetMap Availability for Driving Environment Understanding
摘要：
随着人工智能的巨大成就，车辆技术已经从以人为中心的驾驶向全自动驾驶迈进。智能车辆应该能够理解驾驶员对环境的感知以及控制车辆的行为。由于高数字化地图信息已经可以提供丰富的静态道路，建筑物和交通基础设施的环境背景下，这将是值得探索的地图数据能力，驾驶任务的理解。作为商业地图的替代品，OpenStreetMap（OSM）数据是一个免费的开放数据集，这使得它在勘探研究中是独一无二的。这项研究的重点是两个任务，利用OSM驾驶环境的理解。首先，从OSM元素中检索驾驶场景属性，将其与车辆动态信号相结合以用于驾驶事件识别。利用转向角变化并基于双向循环神经网络（Bi-RNN），将驾驶序列分割并分类为保持车道、向左变道、向右变道、左转和右转事件。第二，对于自动驾驶感知，OSM数据可以用于渲染虚拟街景，表示为先验知识，以与视觉/激光系统融合，用于道路语义分割。从OSM、图像和激光雷达点生成五种不同类型的道路掩模，并将其融合以表征驾驶员视角下的可驾驶空间。另一种数据驱动的方法是基于全卷积网络（FCN），讨论了OSM对深度学习方法的可用性，以揭示补偿街景图像和自动道路语义标注的潜在用途。
英文摘要：
With the great achievement of artificial intelligence, vehicle technologies have advanced significantly from human centric driving towards fully automated driving. An intelligent vehicle should be able to understand the driver's perception of the environment as well as controlling behavior of the vehicle. Since high digital map information has been available to provide rich environmental context about static roads, buildings and traffic infrastructures, it would be worthwhile to explore map data capability for driving task understanding. Alternative to commercial used maps, the OpenStreetMap (OSM) data is a free open dataset, which makes it unique for the exploration research. This study is focused on two tasks that leverage OSM for driving environment understanding. First, driving scenario attributes are retrieved from OSM elements, which are combined with vehicle dynamic signals for the driving event recognition. Utilizing steering angle changes and based on a Bi-directional Recurrent Neural Network (Bi-RNN), a driving sequence is segmented and classified as lane-keeping, lane-change-left, lane-change-right, turn-left, and turn-right events. Second, for autonomous driving perception, OSM data can be used to render virtual street views, represented as prior knowledge to fuse with vision/laser systems for road semantic segmentation. Five different types of road masks are generated from OSM, images, and Lidar points, and fused to characterize the drivable space at the driver's perspective. An alternative data-driven approach is based on a Fully Convolutional Network (FCN), OSM availability for deep learning methods are discussed to reveal potential usage on compensating street view images and automatic road semantic annotation.
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。
