Frontal object perception using radar and mono-vision

Chavéz-García, R. Omar; Burlet, J.; Vu, Trung-Dung; Aycard, Olivier

doi:10.1109/ivs.2012.6232307

Cited by 37 publications

(20 citation statements)

References 10 publications

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“…To take the advantages of both stereo cameras and radar, Wu et al [17] fused detection results from different sensors by extended Kalman filtering (EKF), which can addresses the problem of accurately estimating the location, size, pose, and motion information of a threat vehicle. Due to the poor radar signal in pedestrians, Chavez-Garcia et al [18] only fused the LiDAR and vison sensor for final decision. However, three kinds of sensors including radar, LiDAR and vision were used for vehicle detection in [18].…”

Section: Object Detection By Decision Level Fusionmentioning

confidence: 99%

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Spatial Attention Fusion for Obstacle Detection Using MmWave Radar and Vision Sensor

Chang

Zhang

et al. 2020

Sensors

108

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For autonomous driving, it is important to detect obstacles in all scales accurately for safety consideration. In this paper, we propose a new spatial attention fusion (SAF) method for obstacle detection using mmWave radar and vision sensor, where the sparsity of radar points are considered in the proposed SAF. The proposed fusion method can be embedded in the feature-extraction stage, which leverages the features of mmWave radar and vision sensor effectively. Based on the SAF, an attention weight matrix is generated to fuse the vision features, which is different from the concatenation fusion and element-wise add fusion. Moreover, the proposed SAF can be trained by an end-to-end manner incorporated with the recent deep learning object detection framework. In addition, we build a generation model, which converts radar points to radar images for neural network training. Numerical results suggest that the newly developed fusion method achieves superior performance in public benchmarking. In addition, the source code will be released in the GitHub.

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Section: Object Detection By Decision Level Fusionmentioning

confidence: 99%

“…The first fusion scheme works in decision level. The prediction results from radar and vision sensor are fused to generate the final results [12][13][14][15][16][17][18][19][20][21][22][23][24]. However, there are different kinds of detection noises involved in these two kinds of prediction results.…”

Section: Introductionmentioning

confidence: 99%

Spatial Attention Fusion for Obstacle Detection Using MmWave Radar and Vision Sensor

Chang

Zhang

et al. 2020

Sensors

108

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“…Radars have good longitudinal ranging coupled with crude lateral resolution; monocular vision can localize well in the camera's field of view but lacks ranging. The combination of the two can ameliorate the weakness of each sensor [160], [161]. In [162] and [163], information fusion between radar and vision sensors was used to probabilistically estimate the positions of vehicles and to propagate estimation uncertainty into decision making, for lane change recommendations on the highway.…”

Section: E Fusing Vision With Other Modalitiesmentioning

confidence: 99%

“…In [167], vehicles are detected with a boosted classifier using Haar and Gabor features and ranged using radar. In [160], camera and radar detections were projected into a common global occupancy grid; vehicles were tracked using Kalman filtering in a global frame of reference. In [168], potential vehicles were detected using saliency operations on the inverse perspective mapped image and combined with radar.…”

Section: E Fusing Vision With Other Modalitiesmentioning

confidence: 99%

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Sivaraman

Trivedi

2013

IEEE Trans. Intell. Transport. Syst.

802

322

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Abstract-This paper provides a review of the literature in on-road vision-based vehicle detection, tracking, and behavior understanding. Over the past decade, vision-based surround perception has progressed from its infancy into maturity. We provide a survey of recent works in the literature, placing vision-based vehicle detection in the context of sensor-based on-road surround analysis. We detail advances in vehicle detection, discussing monocular, stereo vision, and active sensor-vision fusion for on-road vehicle detection. We discuss vision-based vehicle tracking in the monocular and stereo-vision domains, analyzing filtering, estimation, and dynamical models. We discuss the nascent branch of intelligent vehicles research concerned with utilizing spatiotemporal measurements, trajectories, and various features to characterize on-road behavior. We provide a discussion on the state of the art, detail common performance metrics and benchmarks, and provide perspective on future research directions in the field.

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“…The authors in [56] used vision operations on the inverse perspective mapped image and ranged via radar. Camera and radar detections were projected into a common global occupancy grid; vehicles were tracked with Kalman filtering in a global frame of reference [52]. In [63], a radar-vision online learning framework was utilized for vehicle detection.…”

Section: Fusion Of Sensorsmentioning

confidence: 99%

Vehicle Detection Techniques for Collision Avoidance Systems: A Review

Mukhtar

Xia

Tang

2015

IEEE Trans. Intell. Transport. Syst.

375

202

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Over the past decade, vision-based vehicle detection techniques for road safety improvement have gained an increasing amount of attention. Unfortunately, the techniques suffer from robustness due to huge variability in vehicle shape (particularly for motorcycles), cluttered environment, various illumination conditions, and driving behavior. In this paper, we provide a comprehensive survey in a systematic approach about the state-of-the-art on-road vision-based vehicle detection and tracking systems for collision avoidance systems (CASs). This paper is structured based on a vehicle detection processes starting from sensor selection to vehicle detection and tracking. Techniques in each process/step are reviewed and analyzed individually. Two main contributions in this paper are the following: survey on motorcycle detection techniques and the sensor comparison in terms of cost and range parameters. Finally, the survey provides an optimal choice with a low cost and reliable CAS design in vehicle industries.

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Frontal object perception using radar and mono-vision

Cited by 37 publications

References 10 publications

Spatial Attention Fusion for Obstacle Detection Using MmWave Radar and Vision Sensor

Spatial Attention Fusion for Obstacle Detection Using MmWave Radar and Vision Sensor

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Vehicle Detection Techniques for Collision Avoidance Systems: A Review

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