  Perceiving the surrounding environment is essential for enabling autonomous
or assisted driving functionalities. Common tasks in this domain include
detecting road users, as well as determining lane boundaries and classifying
driving conditions. Over the last few years, a large variety of powerful Deep
Learning models have been proposed to address individual tasks of camera-based
automotive perception with astonishing performances. However, the limited
capabilities of in-vehicle embedded computing platforms cannot cope with the
computational effort required to run a heavy model for each individual task. In
this work, we present CERBERUS (CEnteR Based End-to-end peRception Using a
Single model), a lightweight model that leverages a multitask-learning approach
to enable the execution of multiple perception tasks at the cost of a single
inference. The code will be made publicly available at
https://github.com/cscribano/CERBERUS
