论文标题
利用雷达感知的时间关系以进行自动驾驶
Exploiting Temporal Relations on Radar Perception for Autonomous Driving
论文作者
论文摘要
我们考虑使用汽车雷达传感器自动驾驶中的对象识别问题。与LiDAR传感器相比，雷达在全天候条件下具有成本效益且健壮的自主驾驶感知。但是，雷达信号在识别周围物体时具有低角度分辨率和精度。为了增强汽车雷达的能力，在这项工作中，我们从连续以自我为中心的鸟眼视图雷达图像框架中利用时间信息来识别雷达对象识别。我们利用对象的存在和属性（大小，方向等）的一致性，并提出一个时间关系层，以明确地模拟连续的雷达图像中对象之间的关系。在对象检测和多个对象跟踪中，我们显示了与几种基线方法相比，我们的方法的优越性。
We consider the object recognition problem in autonomous driving using automotive radar sensors. Comparing to Lidar sensors, radar is cost-effective and robust in all-weather conditions for perception in autonomous driving. However, radar signals suffer from low angular resolution and precision in recognizing surrounding objects. To enhance the capacity of automotive radar, in this work, we exploit the temporal information from successive ego-centric bird-eye-view radar image frames for radar object recognition. We leverage the consistency of an object's existence and attributes (size, orientation, etc.), and propose a temporal relational layer to explicitly model the relations between objects within successive radar images. In both object detection and multiple object tracking, we show the superiority of our method compared to several baseline approaches.
