联网车辆中智能数据分发的情境感知
Situation Awareness for Intelligent Data Distribution in Connected Vehicles
- University of Stuttgart（斯图加特大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对自动驾驶感知受限问题，提出基于鸟瞰图的情境识别方法，利用Cam2BEV和神经网络识别交通情境，实现数据优先分发，并在模拟和真实数据集上验证有效性。
AI中文摘要：
车载传感器的局限性以及遮挡造成的盲区导致自动驾驶车辆感知质量下降。在这种情况下，协同感知通过车联一切通信提供额外数据以增强本地感知，从而产生大量数据传输。车辆可以通过识别当前交通情境，专注于获取和利用与当前道路环境相关的数据。为实现这一目标，我们提出了一种利用鸟瞰图进行车辆情境识别的概念。首先，通过目标检测和语义分割识别车辆周围的情境，随后使用由开源投影变换网络Cam2BEV和情境识别神经网络组成的情境识别模块来理解交通上下文。通过在CARLA模拟器上使用内置RGB相机和语义分割相机运行软件，对该概念进行了评估和验证。此外，在Cityscapes和nuScenes城市驾驶数据集上验证了情境识别模块在真实世界应用中的可移植性。总体而言，所提出的情境识别方法通过优先处理与当前交通情境相关的数据，实现了高效的传感器数据管理。源代码可在以下链接获取：this https URL
英文摘要：
The limitations of on-board sensors and blind spots caused by occlusion cause the reduction of perception quality in autonomous vehicles. In such cases, cooperative perception provides additional data via Vehicle-to-Everything communication to enhance local perception, causing a large volume of data transmission. The vehicle can focus on acquiring and utilizing relevant data according to the prevailing road context by identifying the current traffic situation. To achieve this, we propose a concept for the situation identification of the vehicle using Bird's-Eye-View images. Firstly, the situation around the vehicle is identified using object detection with semantic segmentation, followed by understanding the context of the traffic using a situation identification module consisting of an open-source projective transformation network Cam2BEV and a situation identification neural network. The concept was evaluated and validated by running the software on the CARLA simulator using the in-built RGB camera and the semantic segmentation camera. Additionally, the portability of the situation identification module for real-world applications was verified on Cityscapes and nuScenes urban driving datasets. Overall, the proposed situation identification approach enables efficient sensor data management by prioritizing relevant data to the current traffic situation. The source code is available in the following link: https://github.com/akshaynarla/DySi_Select
