  In this paper, we present a novel sequence generation-based framework for
lane detection, called Lane2Seq. It unifies various lane detection formats by
casting lane detection as a sequence generation task. This is different from
previous lane detection methods, which depend on well-designed task-specific
head networks and corresponding loss functions. Lane2Seq only adopts a plain
transformer-based encoder-decoder architecture with a simple cross-entropy
loss. Additionally, we propose a new multi-format model tuning based on
reinforcement learning to incorporate the task-specific knowledge into
Lane2Seq. Experimental results demonstrate that such a simple sequence
generation paradigm not only unifies lane detection but also achieves
competitive performance on benchmarks. For example, Lane2Seq gets 97.95\% and
97.42\% F1 score on Tusimple and LLAMAS datasets, establishing a new
state-of-the-art result for two benchmarks.
