PriorLane: A Prior Knowledge Enhanced Lane Detection Approach Based on Transformer

• URL: http://arxiv.org/abs/2209.06994v1
• Date: Thu, 15 Sep 2022 01:48:08 GMT
• Title: PriorLane: A Prior Knowledge Enhanced Lane Detection Approach Based on Transformer
• Authors: Qibo Qiu, Haiming Gao, Wei Hua, Gang Huang and Xiaofei He
• Abstract summary: PriorLane is used to enhance the segmentation performance of the fully vision transformer. PriorLane utilizes an encoder-only transformer to fuse the feature extracted by a pre-trained segmentation model with prior knowledge embeddings. Experiments on our Zjlab dataset show that Prior-Lane outperforms SOTA lane detection methods by a 2.82% mIoU.
• Score: 10.55399679259444
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Lane detection is one of the fundamental modules in self-driving. In this paper we employ a transformer-only method for lane detection, thus it could benefit from the blooming development of fully vision transformer and achieves the state-of-the-art (SOTA) performance on both CULane and TuSimple benchmarks, by fine-tuning the weight fully pre-trained on large datasets. More importantly, this paper proposes a novel and general framework called PriorLane, which is used to enhance the segmentation performance of the fully vision transformer by introducing the low-cost local prior knowledge. PriorLane utilizes an encoder-only transformer to fuse the feature extracted by a pre-trained segmentation model with prior knowledge embeddings. Note that a Knowledge Embedding Alignment (KEA) module is adapted to enhance the fusion performance by aligning the knowledge embedding. Extensive experiments on our Zjlab dataset show that Prior-Lane outperforms SOTA lane detection methods by a 2.82% mIoU, and the code will be released at: https://github. com/vincentqqb/PriorLane.

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