Li, Yuxin (Nanyang Technological University), Li, Yiheng (Nanyang Technological University), Yang, Xulei (Institute for Infocomm Research (I2R), Agency for Science, Techn), Yu, Mengying (Desay SV Automotive), Huang, Zihang (Desay SV Automotive), Wu, Xiaojun (Desay SV Automotive), Yeo, Chai Kiat (Nanyang Technological University)

QuadBEV: An Efficient Quadruple-Task Perception Framework Via Birds’-Eye-View Representation

Scheduled for presentation during the Regular Session "Sensing, Vision, and Perception IV" (ThBT5), Thursday, September 26, 2024, 14:50−15:10, Salon 13

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

Birds’-Eye-View (BEV) perception has become a vital component of autonomous driving systems due to its ability to integrate multiple sensor inputs into a unified representation, enhancing performance in various downstream tasks. However,the computational demands of BEV models pose challenges for real-world deployment in vehicles with limited resources. To address these limitations, we propose QuadBEV, an efficient multitask perception framework that leverages the shared spatial and contextual information across four key tasks: 3D object detection, lane detection, map segmentation, and occupancy prediction. QuadBEV not only streamlines the integration of these tasks using a shared backbone and task-specific heads but also addresses common multitask learning challenges such as learning rate sensitivity and conflicting task objectives. Our framework reduces redundant computations, thereby enhancing system efficiency, making it particularly suited for embedded systems. We present comprehensive experiments that validate the effectiveness and robustness of QuadBEV, demonstrating its suitability for real-world applications.