论文标题
Cerberus：具有多任务学习的简单有效的多合一汽车感知模型
CERBERUS: Simple and Effective All-In-One Automotive Perception Model with Multi Task Learning
论文作者
论文摘要
感知周围环境对于实现自主或辅助驾驶功能至关重要。该域中的常见任务包括检测道路使用者，以及确定车道边界和分类驾驶条件。在过去的几年中，已经提出了各种各样的强大深度学习模型，以解决基于相机的汽车感知的单个任务，并以惊人的表现解决。但是，车载嵌入式计算平台的有限功能无法应对为每个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（使用单个模型的基于中心的端到端感知），这是一种轻巧的模型，利用多任务学习方法，以以单个推论为代价实现多个感知任务的执行。该代码将在https://github.com/cscribano/cerberus上公开提供。
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
