通过鸟瞰图识别交通场景，智能筛选车联网感知数据。
Situation Awareness for Intelligent Data Distribution in Connected Vehicles
- 用鸟瞰图结合检测与分割识别车辆周边交通状况。
- 在CARLA仿真中实现场景识别，提升数据传输效率。
- 适配真实城市数据集，适合自动驾驶系统优化决策。
车载传感器受限于视距和遮挡导致感知质量下降。此时，车联网协同感知可通过车联通信提供额外数据，但带来大量数据传输压力。车辆若能根据当前道路情境识别需求，可优先获取并利用相关数据。为此，本文提出基于鸟瞰图（Bird's-Eye-View）的车辆情境识别方法：先通过目标检测与语义分割识别周围环境，再结合开源投影网络Cam2BEV与情境识别神经网络进行上下文理解。该方法在CARLA模拟器中通过内置RGB相机与语义分割相机验证，并在Cityscapes与nuScenes城市驾驶数据集上测试其可迁移性。结果表明，所提情境识别方法能有效按需管理传感器数据，显著提升数据利用效率。源代码已公开于GitHub。
原文摘要 · Abstract (English)
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
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