论文标题
V2VNET：联合感知和预测的车辆到车辆通信
V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction
论文作者
论文摘要
在本文中，我们探讨了使用车辆对车辆（V2V）通信的使用，以改善自动驾驶汽车的感知和运动预测性能。通过智能地汇总从附近的多个车辆收到的信息，我们可以从不同的观点观察到相同的场景。这使我们能够通过闭塞并在远距离观察参与者，其中观察结果非常稀疏或不存在。我们还表明，我们发送压缩深度图映射激活的方法可实现高精度，同时满足沟通带宽要求。
In this paper, we explore the use of vehicle-to-vehicle (V2V) communication to improve the perception and motion forecasting performance of self-driving vehicles. By intelligently aggregating the information received from multiple nearby vehicles, we can observe the same scene from different viewpoints. This allows us to see through occlusions and detect actors at long range, where the observations are very sparse or non-existent. We also show that our approach of sending compressed deep feature map activations achieves high accuracy while satisfying communication bandwidth requirements.
