The ability to perceive and comprehend a traffic situation and to estimate the state of the vehicles and road-users in the surrounding of the ego-vehicle is known as situational awareness. Situational awareness for a heavy-duty autonomous vehicle is a critical part of the automation platform and depends on the ego-vehicle's field-of-view. But when it comes to the urban scenario, the field-of-view of the ego-vehicle is likely to be affected by occlusion and blind spots caused by infrastructure, moving vehicles, and parked vehicles. This paper proposes a framework to improve situational awareness using set-membership estimation and Vehicle-to-Everything (V2X) communication. This framework provides safety guarantees and can adapt to dynamically changing scenarios, and is integrated into an existing complex autonomous platform. A detailed description of the framework implementation and real-time results are illustrated in this paper.

翻译：感知和理解交通场景以及估计自车周围车辆和道路使用者状态的能力被称为态势感知。重型自动驾驶车辆的态势感知是自动化平台的关键组成部分，且依赖于自车的视场。但在城市场景中，自车的视场极易受到基础设施、行驶车辆和停泊车辆所造成的遮挡和盲区影响。本文提出了一种利用集员估计和车联网通信来提升态势感知的框架。该框架能够提供安全保障，适应动态变化的场景，并集成于现有复杂自动驾驶平台中。本文详细阐述了该框架的实现过程及实时测试结果。