Chandrasekaran, Kavin (Elektrobit Automotive GmbH), Grigorescu, Sorin Mihai (Transilvania University of Brasov), Dubbelman, Gijs (Eindhoven University of Technology), Jancura, Pavol (Eindhoven University of Technology)

A Resource Efficient Fusion Network for Object Detection in Bird’s-Eye View Using Camera and Raw Radar Data

Scheduled for presentation during the Regular Session "Sensing, Vision, and Perception I" (WeAT2), Wednesday, September 25, 2024, 10:50−11:10, Salon 5

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

Cameras can be used to perceive the environment around the vehicle, while affordable radar sensors are popular in autonomous driving systems as they can withstand adverse weather conditions unlike cameras. However, radar point clouds are sparser with low azimuth and elevation resolution that lack semantic and structural information of the scenes, resulting in generally lower radar detection performance. In this work, we directly use the raw range-Doppler (RD) spectrum of radar data, thus avoiding radar signal processing. We independently process camera images within the proposed comprehensive image processing pipeline. Specifically, first, we transform the camera images to Bird’s-Eye View (BEV) Polar domain and extract the corresponding features with our camera encoder- decoder architecture. The resultant feature maps are fused with Range-Azimuth (RA) features, recovered from the RD spectrum input from the radar decoder to perform object detection. We evaluate our fusion strategy with other existing methods not only in terms of accuracy but also on computational complexity metrics on RADIal dataset.