Heated debates continue over the best autonomous driving framework. The classic modular pipeline is widely adopted in the industry owing to its great interpretability and stability, whereas the end-to-end paradigm has demonstrated considerable simplicity and learnability along with the rise of deep learning. We introduce a new modularized end-to-end reinforcement learning framework (ModEL) for autonomous driving, which combines the merits of both previous approaches. The autonomous driving stack of ModEL is decomposed into perception, planning, and control module, leveraging scene understanding, end-to-end reinforcement learning, and PID control respectively. Furthermore, we build a fully functional autonomous vehicle to deploy this framework. Through extensive simulation and real-world experiments, our framework has shown great generalizability to various complicated scenarios and outperforms the competing baselines.

翻译：围绕最佳自主驾驶框架的激烈辩论还在继续,传统模块化管道因其可解释性和稳定性而在该行业被广泛采用,而端到端模式则显示出相当简单和可学习性,再加上深层学习的兴起。我们为自主驾驶引入了新的模块化端到端强化学习框架(ModEL),这结合了以前两种做法的优点。MedEL的自主驾驶堆已经分解成感知、规划和控制模块,分别利用现场理解、端到端强化学习和PID控制。此外,我们建立了完全功能的自主工具来部署这一框架。通过广泛的模拟和现实世界实验,我们的框架已经显示出对各种复杂情景的高度普遍性,超越了相互竞争的基线。