Radar and Camera Fusion for Object Forecasting in Driving Scenarios

In this paper, we propose a sensor fusion architecture that combines data collected by the camera and radars and utilizes radar velocity for road users' trajectory prediction in real-world driving scenarios. This architecture is multi-stage, following the detect-track-predict paradigm. In the detection stage, camera images and radar point clouds are used to detect objects in the vehicle's surroundings by adopting two object detection models. The detected objects are tracked by an online tracking method. We also design a radar association method to extract radar velocity for an object. In the prediction stage, we build a recurrent neural network to process an object's temporal sequence of positions and velocities and predict future trajectories. Experiments on the real-world autonomous driving nuScenes dataset show that the radar velocity mainly affects the center of the bounding box representing the position of an object and thus improves the prediction performance.