论文标题
DEEPIPC：在真实环境中对自动驾驶汽车的深入整合和控制
DeepIPC: Deeply Integrated Perception and Control for an Autonomous Vehicle in Real Environments
论文作者
论文摘要
在这项工作中，我们介绍了DeePIPC，这是一种针对自动驾驶的新型端到端模型，该模型无缝地整合了感知和控制任务。与分别处理这些任务的传统模型不同，DEEPIPC创新地结合了感知模块，该模块可以处理RGBD图像以进行语义细分，并生成Bird's Eye View（BEV）映射，并利用这些洞察力，该模块将这些见解与GNSS和Angular Spepurements一起进行准确预测导航的速度。这种集成允许DEEPIPC有效地将复杂的环境数据转化为可操作的驾驶命令。我们的全面评估表明，DeePIPC在各种现实世界中的驾驶性和多任务效率方面的出色表现，为具有更精美的模型体系结构的端到端自动驾驶系统树立了新的基准。实验结果强调了DeePIPC显着增强自动驾驶汽车导航的潜力，这有望在自主驾驶技术的发展中向前迈出一步。要进行进一步的见解和复制，我们将在https://github.com/oskarnatan/deepipc上提供代码和数据集。
In this work, we introduce DeepIPC, a novel end-to-end model tailored for autonomous driving, which seamlessly integrates perception and control tasks. Unlike traditional models that handle these tasks separately, DeepIPC innovatively combines a perception module, which processes RGBD images for semantic segmentation and generates bird's eye view (BEV) mappings, with a controller module that utilizes these insights along with GNSS and angular speed measurements to accurately predict navigational waypoints. This integration allows DeepIPC to efficiently translate complex environmental data into actionable driving commands. Our comprehensive evaluation demonstrates DeepIPC's superior performance in terms of drivability and multi-task efficiency across diverse real-world scenarios, setting a new benchmark for end-to-end autonomous driving systems with a leaner model architecture. The experimental results underscore DeepIPC's potential to significantly enhance autonomous vehicular navigation, promising a step forward in the development of autonomous driving technologies. For further insights and replication, we will make our code and datasets available at https://github.com/oskarnatan/DeepIPC.
