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.

翻译：本文提出DeepIPC——一种专为自动驾驶设计的新型端到端模型，该模型将感知与控制任务无缝集成。与传统独立处理感知和控制的模型不同，DeepIPC创新性地融合了感知模块与控制器模块：感知模块处理RGBD图像进行语义分割并生成鸟瞰图映射，控制器模块则利用这些信息结合GNSS和角速度测量值精确预测导航航路点。这种集成使DeepIPC能够高效地将复杂环境数据转化为可执行的驾驶指令。我们的综合评估表明，DeepIPC在多种真实场景下展现出卓越的可驾驶性和多任务效率，以更精简的模型架构树立了端到端自动驾驶系统的新标杆。实验结果凸显了DeepIPC在显著增强自动驾驶车辆导航方面的潜力，标志着自动驾驶技术开发的重大进步。为便于深入探究与复现，我们将于https://github.com/oskarnatan/DeepIPC 公开代码与数据集。