We consider the problem of intelligently navigating through complex traffic. Urban situations are defined by the underlying map structure and special regulatory objects of e.g. a stop line or crosswalk. Thereon dynamic vehicles (cars, bicycles, etc.) move forward, while trying to keep accident risks low. Especially at intersections, the combination and interaction of traffic elements is diverse and human drivers need to focus on specific elements which are critical for their behavior. To support the analysis, we present in this paper the so-called Risk Navigation System (RNS). RNS leverages a graph-based local dynamic map with Time-To-X indicators for extracting upcoming sharp curves, intersection zones and possible vehicle-to-object collision points. In real car recordings, recommended velocity profiles to avoid risks are visualized within a 2D environment. By focusing on communicating not only the positional but also the temporal relation, RNS potentially helps to enhance awareness and prediction capabilities of the user.

翻译：本文研究智能穿越复杂交通场景的问题。城市交通情境由底层地图结构及停止线、人行横道等特殊管制对象定义。动态车辆（汽车、自行车等）在保持低事故风险的同时向前行驶。尤其在交叉路口，交通元素的组合与交互方式多样，人类驾驶员需关注对其行为至关重要的特定元素。为辅助此类分析，本文提出风险导航系统（RNS）。该系统利用基于图的局部动态地图与时间至X指标（Time-To-X），提取前方急弯、路口区域及潜在的车辆-物体碰撞点。在实车采集数据中，系统在二维环境中可视化避免风险的推荐速度剖面。通过聚焦传递空间位置关系与时间关联性，RNS系统有望增强用户的环境感知与预测能力。