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
翻译:观察周围环境对于促成自主或辅助驾驶功能至关重要。这一领域的共同任务包括探测道路使用者,以及确定车道边界和驾驶条件分类。在过去几年里,提出了各种强大的深层学习模式,以解决以摄影机为基础的汽车观感的个别任务,其性能令人吃惊。然而,车内嵌入的计算平台的能力有限,无法应付为每项任务运行重型模型所需的计算工作。在这项工作中,我们介绍了CERBERUS(CENTER以单一模型为基础的端至端端对端观),一个轻量模型,利用多任务学习方法,以单一推算为代价,执行多种认知任务。该代码将在https://github.com/cscribano/CERBERUS上公布。该代码将在https://gthub.com/cscribano/CERBERUS上公布。