Online Map Vectorization for Autonomous Driving: A Rasterization Perspective
Authors: Gongjie Zhang, Jiahao Lin, Shuang Wu, yilin song, Zhipeng Luo, Yang Xue, Shijian Lu, Zuoguan Wang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that incorporating rasterization into map vectorization greatly enhances performance with no extra computational cost during inference, leading to more accurate map perception and ultimately promoting safer autonomous driving. |
| Researcher Affiliation | Collaboration | Black Sesame Technologies Nanyang Technological University, Singapore |
| Pseudocode | No | The paper describes its methods and framework through text and diagrams, but it does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Project Page: https://github.com/ZhangGongjie/MapVR |
| Open Datasets | Yes | nu Scenes Map (basic) [2], which consists of two line-shaped map classes (lane divider and road boundary) and one polygon-shaped map class (pedestrian crossing). This dataset setup aligns with prior works on map vectorization [16, 6, 26, 20]. 2. nu Scenes Map (extended) [2], an extension of nu Scenes Map (basic) that incorporates more complex map elements, such as intersection, stopline area, and carpark area. 3. Argoverse2 [49], a large-scale dataset featuring the same classes as nu Scenes Map (basic). |
| Dataset Splits | Yes | nu Scenes Map (basic) validation set. nu Scenes Map (extended) validation set. Argoverse2 validation set. |
| Hardware Specification | Yes | All experiments, unless otherwise stated, are conducted with 8x NVIDIA RTX 3090 GPUs. Results were obtained with 8x NVIDIA A100 GPUs under the same training setups. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers, such as programming language versions or library versions (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | All experiments, unless otherwise stated, are conducted with 8x NVIDIA RTX 3090 GPUs. As the proposed Map VR is a generic framework with no reliance on specific model architecture, we adopt Map TR [20], the state-of-the-art model for map vectorization, as the base model. Our implementation aligns with Map TR [20]. The spatial size of the HD map is 480 × 240, such that each pixel represents 0.125m. We dilate the rasterized polylines by 2 pixels on each side. The softness τ for line rasterization is studied with values 0.5, 1.0, 2.0, 4.0, 6.0. Models are trained for 24 or 110 epochs using ResNet-50 as backbone. |