A Review on 3D Object Detection for Self-Driving Cars

Le, Huu-Sy; Le, Tan Duy; Huynh, Kha-Tu

doi:10.1109/rivf55975.2022.10013923

Cited by 1 publication

(1 citation statement)

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“…Furthermore, the prediction and decision-making processes in AVs that rely on a single sensor can be hampered by external factors such as bad weather, occlusion, or poor lighting conditions because cameras struggle in low-light environments, whereas radars cannot detect objects with rich visual features. This limitation in cameras and radar, and the potential consequences of reliance on a single sensor for object detection in AVs, has generated significant attention in the field of research toward the utilization of multi-modal based sensing in the automotive domain, especially in perception systems that fuse camera and radar inputs [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

NeXtFusion: Attention-Based Camera-Radar Fusion Network for Improved Three-Dimensional Object Detection and Tracking

Kalgaonkar,

El-Sharkawy

2024

Future Internet

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Accurate perception is crucial for autonomous vehicles (AVs) to navigate safely, especially in adverse weather and lighting conditions where single-sensor networks (e.g., cameras or radar) struggle with reduced maneuverability and unrecognizable targets. Deep camera–radar fusion neural networks offer a promising solution for reliable AV perception under any weather and lighting conditions. Cameras provide rich semantic information, while radars act like an X-ray vision, piercing through fog and darkness. This work proposes a novel, efficient camera–radar fusion network called NeXtFusion for robust AV perception with an improvement in object detection accuracy and tracking. Our proposed approach of utilizing an attention module enhances crucial feature representation for object detection while minimizing information loss from multi-modal data. Extensive experiments on the challenging nuScenes dataset demonstrate NeXtFusion’s superior performance in detecting small and distant objects compared to other methods. Notably, NeXtFusion achieves the highest mAP score (0.473) on the nuScenes validation set, outperforming competitors like OFT (35.1% improvement) and MonoDIS (9.5% improvement). Additionally, NeXtFusion demonstrates strong performance in other metrics like mATE (0.449) and mAOE (0.534), highlighting its overall effectiveness in 3D object detection. Furthermore, visualizations of nuScenes data processed by NeXtFusion further demonstrate its capability to handle diverse real-world scenarios. These results suggest that NeXtFusion is a promising deep fusion network for improving AV perception and safety for autonomous driving.

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Section: Introductionmentioning

confidence: 99%