  A panoptic driving perception system is an essential part of autonomous
driving. A high-precision and real-time perception system can assist the
vehicle in making the reasonable decision while driving. We present a panoptic
driving perception network (YOLOP) to perform traffic object detection,
drivable area segmentation and lane detection simultaneously. It is composed of
one encoder for feature extraction and three decoders to handle the specific
tasks. Our model performs extremely well on the challenging BDD100K dataset,
achieving state-of-the-art on all three tasks in terms of accuracy and speed.
Besides, we verify the effectiveness of our multi-task learning model for joint
training via ablative studies. To our best knowledge, this is the first work
that can process these three visual perception tasks simultaneously in
real-time on an embedded device Jetson TX2(23 FPS) and maintain excellent
accuracy. To facilitate further research, the source codes and pre-trained
models are released at https://github.com/hustvl/YOLOP.
