Equipped with a wide span of sensors, predominant autonomous driving solutions are becoming more modular-oriented for safe system design. Though these sensors have laid a solid foundation, most massive-production solutions up to date still fall into L2 phase. Among these, Comma.ai comes to our sight, claiming one $999 aftermarket device mounted with a single camera and board inside owns the ability to handle L2 scenarios. Together with open-sourced software of the entire system released by Comma.ai, the project is named Openpilot. Is it possible? If so, how is it made possible? With curiosity in mind, we deep-dive into Openpilot and conclude that its key to success is the end-to-end system design instead of a conventional modular framework. The model is briefed as Supercombo, and it can predict the ego vehicle's future trajectory and other road semantics on the fly from monocular input. Unfortunately, the training process and massive amount of data to make all these work are not publicly available. To achieve an intensive investigation, we try to reimplement the training details and test the pipeline on public benchmarks. The refactored network proposed in this work is referred to as OP-Deepdive. For a fair comparison of our version to the original Supercombo, we introduce a dual-model deployment scheme to test the driving performance in the real world. Experimental results on nuScenes, Comma2k19, CARLA, and in-house realistic scenarios verify that a low-cost device can indeed achieve most L2 functionalities and be on par with the original Supercombo model. In this report, we would like to share our latest findings, shed some light on the new perspective of end-to-end autonomous driving from an industrial product-level side, and potentially inspire the community to continue improving the performance. Our code, benchmarks are at https://github.com/OpenPerceptionX/Openpilot-Deepdive.
翻译：虽然这些传感器已经为安全系统设计奠定了坚实的基础,但迄今为止的大规模生产解决方案仍然都落到L2阶段。其中,Comma.ai来到我们的视线,声称一个以单一摄像头和棋盘安装的市场后设备有999美元,它拥有处理L2情景的能力。不幸的是,整个系统由Coma.ai发行的公开源码软件被命名为Openpilot。它是否可能?如果是,它是如何做到的?我们深思熟虑,我们进入Openpilot,并得出结论,其成功的关键是端到端系统设计,而不是传统的模块框架。模型以超级电脑的形式简单介绍,它可以预测自家车未来轨迹和其他路标。不幸的是,培训过程和大量数据无法公开提供所有这些工作模式。为了实现深度调查,我们试图重新落实培训细节,并在公共基准上测试管道。在Sloveloplealalalal-Oral-Oral-Oral-Oral-Oral-Oral-Oral-Orental-Oal-Oral-Oral-lation Profal-lation Stal-lation-lation-lation-lation real-lation Profal-lation-lation-lational-lational-lational-lational-lational-lational-lational-lational-lational-lational-lation-lational-lation-lation-lation-lation-lation-lation-lation-lation-lation-lation-lation-lation-lation-lation-lation-lation-lation-lation-lation-lation-l-l-l-l) laut-l-l-l-l-l-l-l-l-l-l-l-l-l-l-l-l-l-lation-lation-l-l-l-l-l-l-l-l-l-l-l-l-l-l-l-l-l-l-l-l-l-l-l-lut-l-l-l-l-l-l-l-l-l-l-
相关内容
微信扫码咨询专知VIP会员