  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.
