Deformable Radar Polygon Occupancy Representation for Short-range Collision Avoidance

A Lightweight and Predictable Occupancy Representation for Short-range applications

Deformable Radar Polygon: A Lightweight and Predictable Occupancy Representation for Short-range Collision Avoidance,
Xiangyu Gao, Sihao Ding, Harshavardhan Reddy Dasari

IEEE Sensors Journal (arXiv 2203.01442)

@article{xiangyu2022deformable,
    title={Deformable Radar Polygon: A Lightweight and Predictable Occupancy Representation for Short-range Collision Avoidance},
    author={Xiangyu, Gao and Sihao, Ding and Reddy, Dasari Harshavardhan},
    journal={arXiv preprint arXiv:2203.01442},
    year={2022}}

Update

(July 4, 2024) Code and sample data release.

Abstract

Inferring the drivable area in a scene is crucial for ensuring a vehicle avoids obstacles and facilitates safe autonomous driving. In this paper, we concentrate on detecting the instantaneous free space surrounding the ego vehicle, targeting shortrange automotive applications. We introduce a novel polygonbased occupancy representation, where the interior signifies free space, and the exterior represents undrivable areas for the egovehicle. The radar polygon consists of vertices selected from point cloud measurements provided by radars, with each vertex incorporating Doppler velocity information from automotive radars. This information indicates the movement of the vertex along the radial direction. This characteristic allows for the prediction of the shape of future radar polygons, leading to its designation as a “deformable radar polygon”. We propose two approaches to leverage noisy radar measurements for producing accurate and smooth radar polygons. The first approach is a basic radar polygon formation algorithm, which independently selects polygon vertices for each frame, using SNR-based evidence for vertex fitness verification. The second approach is the radar polygon update algorithm, which employs a probabilistic and tracking-based mechanism to update the radar polygon over time, further enhancing accuracy and smoothness. To accommodate the unique radar polygon format, we also designed a collision detection method for short-range applications. Through extensive experiments and analysis on both a self-collected dataset and the open-source RadarScenes dataset, we demonstrate that our radar polygon algorithms achieve significantly higher IoU-gt and IoUsmooth values compared to other occupancy detection baselines, highlighting their accuracy and smoothness.

Use Deformable Radar Polygon

All radar configurations and algorithm configurations are included in config.py.

Software Requirement and Installation

Python 3.6, and libraries in requirements.txt.

Download Sample Data

From below Google Drive link download the sample data 'scene.pickle'.
```
https://drive.google.com/file/d/12NV46iAPfws4SVUuxyfHbsqE_BibqAyK/view?usp=sharing
```
Note that the sample data is from the scene 143 of RadarScenes Dataset. You can try other data by downloading the RadarScenes, and read and save other scenes of data with the read_radarscene_data.py.

Relocate the pickle data under the project directory.

./deformable_radar_polygon_occupancy_representation/scene.pickle

Run Basic Radar Polygon Formation Algorithm

python polygon_radarscene_singleFrame.py

The polygon results are saved in ./res/results_polygon/radarScene143_poly as pickle file and their visualization is in ./res/results_bev/radarScene143_poly.