# KAN-RCBEVDepth: A multi-modal fusion algorithm in object detection for autonomous driving

Source: [KAN-RCBEVDepth: A multi-modal fusion algorithm in object detection for autonomous driving](<https://www.sota2.com/research/paper/68fa74a51e209131cea62e84>)

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

Data updated: 2025-10-26T03:14:35.342Z

Authors: Zhihao Lai, Chuanhao Liu, Shihui Sheng, Zhiqiang Zhang

Published: 2024-08-04T16:54:49.000Z

## Abstract

Accurate 3D object detection in autonomous driving is critical yet challenging due to occlusions, varying object sizes, and complex urban environments. This paper introduces the KAN-RCBEVDepth method, an innovative approach aimed at enhancing 3D object detection by fusing multimodal sensor data from cameras, LiDAR, and millimeter-wave radar. Our unique Bird's Eye View-based approach significantly improves detection accuracy and efficiency by seamlessly integrating diverse sensor inputs, refining spatial relationship understanding, and optimizing computational procedures. Experimental results show that the proposed method outperforms existing techniques across multiple detection metrics, achieving a higher Mean Distance AP (0.389, 23\\% improvement), a better ND Score (0.485, 17.1\\% improvement), and a faster Evaluation Time (71.28s, 8\\% faster). Additionally, the KAN-RCBEVDepth method significantly reduces errors compared to BEVDepth, with lower Transformation Error (0.6044, 13.8\\% improvement), Scale Error (0.2780, 2.6\\% improvement), Orientation Error (0.5830, 7.6\\% improvement), Velocity Error (0.4244, 28.3\\% improvement), and Attribute Error (0.2129, 3.2\\% improvement). These findings suggest that our method offers enhanced accuracy, reliability, and efficiency, making it well-suited for dynamic and demanding autonomous driving scenarios. The code will be released in \\url{https://github.com/laitiamo/RCBEVDepth-KAN}.

## 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/2408.02088>)

[PDF](<https://arxiv.org/pdf/2408.02088.pdf>)