The ability to perceive and comprehend a traffic situation and to predict the intent of 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 is dependent 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：态势感知是指感知和理解交通状况，并预测自车周围车辆及道路使用者意图的能力。对于重型自动驾驶车辆而言，态势感知是自动化平台的关键组成部分，且依赖于自车的视野范围。然而，在城市场景中，自车的视野可能受到由基础设施、行驶车辆和停放车辆造成的遮挡及盲区影响。本文提出了一种利用集员估计和车联万物（V2X）通信来提升态势感知的框架。该框架提供安全保障，能够适应动态变化的场景，并集成于现有的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实验结果。