Radar Guided Camera Verification for Automatic Emergency Braking Rethinking Object Detection in Radar Camera Fusion

• URL: http://arxiv.org/abs/2606.27556v1
• Date: Thu, 25 Jun 2026 21:13:00 GMT
• Title: Radar Guided Camera Verification for Automatic Emergency Braking Rethinking Object Detection in Radar Camera Fusion
• Abstract summary: We propose a radar scoped edge density gate that performs obstacle verification within radar guided image regions of interest.<n>The proposed approach reduced the camera search space by up to 98.7 percentage.<n>Across 33 staged threat scenarios, the complete AEB system recorded zero missed brake events.
• Score: 1.8505741812953405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Radar camera fusion is widely used in Automatic Emergency Braking AEB systems because radar provides reliable range and velocity measurements while cameras provide a proper visual confirmation of the objects . Most of the deployed systems perform this confirmation using computationally intensive object detectors. However, if the radar has already localized a target, the camera may only need to verify the obstacles presence rather than solving a full problem by identifying the object. Our work proposes a radar scoped edge density gate that performs obstacle verification within radar guided image regions of interest. This method requires no training data, model weights, or GPU acceleration and was integrated into a complete radar camera fusion AEB system with brake by wire actuation. Evaluated on a real instrumented vehicle across 72 driving sessions and 131,603 camera frames, the proposed approach reduced the camera search space by up to 98.7 percentage, achieved a mean processing latency of 0.121 ms per ROI, an AUC of 0.898, and a recall of 0.994. Across 33 staged threat scenarios, the complete AEB system recorded zero missed brake events.

Related papers

• RAF: Reliability-Aware Fusion of Camera, LiDAR, and 4D RADAR for Robust 3D Object Detection in Adverse Weather [47.7622249773517]
  We introduce Reliability-Aware Fusion (RAF), which explicitly per-pixel reliability estimation and provides a direct learning signal for identifying and suppressing unreliable visual cues.<n>RAF consistently improves detection accuracy over LiDAR-RADAR baselines, achieving up to +6.5 $AP_BEV$ and +7.4 $AP_3D$ gains.
  arXiv  Detail & Related papers  (2026-07-06T01:24:35Z)
• RadarSim: Simulating Single-Chip Radar via Multimodal Neural Fields [49.41438378775056]
  Radars are an ideal complement to cameras, with cameras offering fine angular resolution and radars providing metric depth and robustness under adverse weather.<n>RadarSim produces sharper geometry and Doppler range frames than radar-only reconstructions.
  arXiv  Detail & Related papers  (2026-05-25T21:03:40Z)
• Revisiting Radar Camera Alignment by Contrastive Learning for 3D Object Detection [31.69508809666884]
  3D object detection algorithms based on radar and camera fusion have shown excellent performance.<n>We propose a new alignment model called Radar Camera Alignment (RCAlign)<n>Specifically, we design a Dual-Route Alignment (DRA) module based on contrastive learning to align and fuse the features between radar and camera.<n>Considering the sparsity of radar BEV features, a Radar Feature Enhancement (RFE) module is proposed to improve the densification of radar BEV features.
  arXiv  Detail & Related papers  (2025-04-23T02:41:43Z)
• RobuRCDet: Enhancing Robustness of Radar-Camera Fusion in Bird's Eye View for 3D Object Detection [68.99784784185019]
  Poor lighting or adverse weather conditions degrade camera performance.<n>Radar suffers from noise and positional ambiguity.<n>We propose RobuRCDet, a robust object detection model in BEV.
  arXiv  Detail & Related papers  (2025-02-18T17:17:38Z)
• Bootstrapping Autonomous Driving Radars with Self-Supervised Learning [13.13679517730015]
  Training radar models is hindered by the cost and difficulty of annotating large-scale radar data. We propose a self-supervised learning framework to leverage the large amount of unlabeled radar data to pre-train radar-only embeddings for self-driving perception tasks. When used for downstream object detection, we demonstrate that the proposed self-supervision framework can improve the accuracy of state-of-the-art supervised baselines by $5.8%$ in mAP.
  arXiv  Detail & Related papers  (2023-12-07T18:38:39Z)
• Echoes Beyond Points: Unleashing the Power of Raw Radar Data in Multi-modality Fusion [74.84019379368807]
  We propose a novel method named EchoFusion to skip the existing radar signal processing pipeline. Specifically, we first generate the Bird's Eye View (BEV) queries and then take corresponding spectrum features from radar to fuse with other sensors.
  arXiv  Detail & Related papers  (2023-07-31T09:53:50Z)
• CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception [20.824179713013734]
  We propose Camera Radar Net (CRN), a novel camera-radar fusion framework. CRN generates semantically rich and spatially accurate bird's-eye-view (BEV) feature map for various tasks. CRN with real-time setting operates at 20 FPS while achieving comparable performance to LiDAR detectors on nuScenes.
  arXiv  Detail & Related papers  (2023-04-03T00:47:37Z)
• Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics [77.34726150561087]
  This work aims to carry out a study on the current scenario of camera and radar-based perception for ADAS and autonomous vehicles. Concepts and characteristics related to both sensors, as well as to their fusion, are presented. We give an overview of the Deep Learning-based detection and segmentation tasks, and the main datasets, metrics, challenges, and open questions in vehicle perception.
  arXiv  Detail & Related papers  (2023-03-08T00:48:32Z)
• Scene-aware Learning Network for Radar Object Detection [29.869186047966945]
  Radar has been drawing more and more attention due to its robustness and low cost. In this paper, we propose a scene-aware radar learning framework for accurate and robust object detection.
  arXiv  Detail & Related papers  (2021-07-03T17:19:56Z)
• Rethinking of Radar's Role: A Camera-Radar Dataset and Systematic Annotator via Coordinate Alignment [38.24705460170415]
  We propose a new dataset, named CRUW, with a systematic annotator and performance evaluation system. CRUW aims to classify and localize the objects in 3D purely from radar's radio frequency (RF) images. To the best of our knowledge, CRUW is the first public large-scale dataset with a systematic annotation and evaluation system.
  arXiv  Detail & Related papers  (2021-05-11T17:13:45Z)
• LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion [52.59664614744447]
  We present LiRaNet, a novel end-to-end trajectory prediction method which utilizes radar sensor information along with widely used lidar and high definition (HD) maps. automotive radar provides rich, complementary information, allowing for longer range vehicle detection as well as instantaneous velocity measurements.
  arXiv  Detail & Related papers  (2020-10-02T00:13:00Z)
• RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects [73.80316195652493]
  We tackle the problem of exploiting Radar for perception in the context of self-driving cars. We propose a new solution that exploits both LiDAR and Radar sensors for perception. Our approach, dubbed RadarNet, features a voxel-based early fusion and an attention-based late fusion.
  arXiv  Detail & Related papers  (2020-07-28T17:15:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.