Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radar

Author:

Ulrich Michael1,Braun Sascha2,Kohler Daniel2,Niederlohner Daniel2,Faion Florian1,Glaser Claudius1,Blume Holger3

Affiliation:

1. Robert Bosch GmbH, Corporate Research,Germany

2. Robert Bosch GmbH, Cross-Domain Computing Solutions,Germany

3. Leibniz University,Hannover,Germany

Publisher

IEEE

Cited by 16 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Deep Learning-Driven State Correction: A Hybrid Architecture for Radar-Based Dynamic Occupancy Grid Mapping;2024 IEEE Intelligent Vehicles Symposium (IV);2024-06-02

2. LiRaFusion: Deep Adaptive LiDAR-Radar Fusion for 3D Object Detection;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

3. RC-BEVFusion: A Plug-In Module for Radar-Camera Bird’s Eye View Feature Fusion;Lecture Notes in Computer Science;2024

4. Reinforcement Learning for SAR Target Orientation Inference With the Differentiable SAR Renderer;IEEE Transactions on Geoscience and Remote Sensing;2024

5. RPC-Pillars: Radar Point Correction with Radar-PointPillars;Lecture Notes in Networks and Systems;2024

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