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MapVectorizationEvalToolkit

This repository contains the official code of the evaluation toolkit proposed in the NeurIPS 2023 paper "Online Map Vectorization for Autonomous Driving". We propose a rasterization-based evaluation process which converts the vectorized predictions and ground truth instances into rasterized maps respectively in the bird's-eye-view, on which we perform evaluation with mask-based intersection-over-union.

The main repository of our proposed method can be found here. Also, checkout our NeurIPS 2023 paper for more details.

Installation and Usage

Clone this repository to your local working directory. The main evaluator is located in map_evaluator.py. The default evaluation setting is provided in eval_config.py.

We include a demo script showing the procedure of using the evaluator in demo.py. Sample data is also included in sample_data.json consisting of both ground truth data and predictions.

To perform the evaluation, run

python demo.py --cfg eval_config.py

Further clarification on evaluation settings

The evaluation settings used in all our evaluation, including both the parameters for rasterization and the specific evaluation metrics, are provided in eval_config.py.

An example to include more classes for evaluation:

LINE_CLASSES = {"divider": 0, "boundary": 2, "new_line_class_A": 3, "new_line_class_B": 4}
POLYGON_CLASSES = {"ped_crossing": 1, "new_polygon_class_A": 5}

The region of interest for map element evaluation is $\pm 15m$ (left-right) and $\pm 30m$ (front-rear). We use a resolution of $480 \times 240$ for the rasterized map. A dilation of size 5 (2px on each side) is applied on each rasterized map element. We use a loose IoU threshold (0.25:0.50:0.05) for line elements and a tight IoU threshold (0.50:0.75:0.05) for polygon elements because line elements are in general more difficult to precisely localize.

The default evaluation metrics are defined as

QUERY_LINE = [
    ("AP", "all", "all", 100),
    ("AP", "all", "divider", 100),
    ("AP", "all", "boundary", 100),
    ("AP", 0.25, "all", 100),
    ("AP", 0.25, "divider", 100),
    ("AP", 0.25, "boundary", 100),
    ("AP", 0.50, "all", 100),
    ("AP", 0.50, "divider", 100),
    ("AP", 0.50, "boundary", 100),
    ("AR", "all", "all", 1),
    ("AR", "all", "all", 10),
    ("AR", "all", "all", 100),
    ("AR", "all", "divider", 100),
    ("AR", "all", "boundary", 100),
]
QUERY_POLYGON = [
    ("AP", "all", "ped_crossing", 100),
    ("AP", 0.50, "ped_crossing", 100),
    ("AP", 0.75, "ped_crossing", 100),
    ("AR", "all", "ped_crossing", 1),
    ("AR", "all", "ped_crossing", 10),
    ("AR", "all", "ped_crossing", 100),
]

which can be extended for additional IoU thresholds of interest, or new classes to be evaluated.

We recommend using the same settings of evaluation for a fair comparison among this future line of research.

Citation

If you find our evaluation toolkit useful, please consider citing:

@inproceedings{zhang2023online,
  title={Online Map Vectorization for Autonomous Driving: A Rasterization Perspective},
  author={Zhang, Gongjie and Lin, Jiahao and Wu, Shuang and Song, Yilin and Luo, Zhipeng and Xue, Yang and Lu, Shijian and Wang, Zuoguan},
  journal={Advances in Neural Information Processing Systems},
  year={2023}
}

Acknowledgement

Our evaluation toolkit is following the Average Precision-based metric in an object detection style inspired by COCOeval.