# ROSS: Radar Off-road Semantic Segmentation

Source: [ROSS: Radar Off-road Semantic Segmentation](<https://www.sota2.com/research/paper/68fa70ea1e209131cea4df43>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:52:52.049Z

Authors: Peng Jiang, Srikanth Saripalli

Published: 2023-10-20T14:50:34.000Z

## Abstract

As the demand for autonomous navigation in off-road environments increases, the need for effective solutions to understand these surroundings becomes essential. In this study, we confront the inherent complexities of semantic segmentation in RADAR data for off-road scenarios. We present a novel pipeline that utilizes LIDAR data and an existing annotated off-road LIDAR dataset for generating RADAR labels, in which the RADAR data are represented as images. Validated with real-world datasets, our pragmatic approach underscores the potential of RADAR technology for navigation applications in off-road environments.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2310.13551>)

[PDF](<https://arxiv.org/pdf/2310.13551.pdf>)