  A safe and robust autonomous driving system relies on accurate perception of
the environment for application-oriented scenarios. This paper proposes
deployment of the three most crucial tasks (i.e., object detection, drivable
area segmentation and lane detection tasks) on embedded system for self-driving
operations. To achieve this research objective, multi-tasking network is
utilized with a simple encoder-decoder architecture. Comprehensive and
extensive comparisons for two models based on different backbone networks are
performed. All training experiments are performed on server while Nvidia Jetson
Xavier NX is chosen as deployment device.
