Camera-Radar Data Fusion for Target Detection via Kalman Filter and Bayesian Estimation

Yu, Zhexiang; Bai, Jie; Chen, Sihan; Huang, Libo; Bi, Xin

doi:10.4271/2018-01-1608

Cited by 13 publications

(7 citation statements)

References 9 publications

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“…A number of fusion methods based on radar and camera sensors have been proposed [24], [25], [26], [27], [28], [29]. In [24], in order to improve the accuracy of target detection, the author proposed a Camera Radar Fusion-Net (CRF-NET) which is employed to fuse the data of camera and radar, pointing out a new direction for sensor data fusion work.…”

Section: B Related Workmentioning

confidence: 99%

“…The camera calibration algorithm is utilized to combine the radar targets and video vehicle detection targets, simultaneously measure the speed of multiple vehicles. In [26], the main idea of sensor fusion is to extract image patches according to regions of interest (ROIs), generated by radar points in camera coordinates. Finally, the target data are fused based on Bayesian estimation.…”

Section: B Related Workmentioning

confidence: 99%

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Mixed Traffic Flow State Detection: A Connected Vehicles-Assisted Roadside Radar and Video Data Fusion Scheme

Chen

Ning

Lei

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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An increasing number of connected vehicles (CVs) driving together with regular vehicles (RVs) on the road is an inevitable stage of future traffic development. As accurate traffic flow state detection is essential for ensuring safe and efficient traffic, the level of road intelligence is being enhanced by the mass deployment of roadside perception devices, which is capable of sensing the mixed traffic flow consisting of RVs and CVs. In this background, we propose a roadside radar and camera data fusion framework to improve the accuracy of traffic flow state detection, which utilizes relatively more accurate traffic parameters obtained from real-time communication between CVs and roadside unit (RSU) as calibration values for training the back propagation (BP) neural network. Then, with the perception data collected by roadside sensors, the BP neural network-based data fusion model is applied to all vehicles including RVs. Furthermore, considering the changes of road environments, a dynamic BP fusion method is proposed, which adopts dynamic training by updating samples conditionally, and are applied to fuse traffic flow, occupancy and RVs speed data. Simulation results demonstrate that for CVs data and all vehicles (including RVs) data, the proposed dynamic BP fusion method is more accurate than single sensor detection, entropy based Bayesian fusion method and traditional BP fusion without training by CVs. It can achieve smaller error, and the accuracies of vehicle speed, traffic flow, and occupancy are all above 95%.

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Section: B Related Workmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

Mixed Traffic Flow State Detection: A Connected Vehicles-Assisted Roadside Radar and Video Data Fusion Scheme

Chen

Ning

Lei

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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“…Secondly, the track data from each sensor decides whether fused track state and the new incoming track data is admissible inside gating using the Mahalanobis distance which is derived from χ 2 -distribution [25]. Finally, the track update process follows the work well accomplished by the use of the Kalman Filter [26]. In the track update process, there are three states which are track creation, standby, and confirmed.…”

Section: Vehicle Trackermentioning

confidence: 99%

Reliable Road Scene Interpretation Based on ITOM with the Integrated Fusion of Vehicle and Lane Tracker in Dense Traffic Situation

Jeong

Yoon

Park

2020

Sensors

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Lane detection and tracking in a complex road environment is one of the most important research areas in highly automated driving systems. Studies on lane detection cover a variety of difficulties, such as shadowy situations, dimmed lane painting, and obstacles that prohibit lane feature detection. There are several hard cases in which lane candidate features are not easily extracted from image frames captured by a driving vehicle. We have carefully selected typical scenarios in which the extraction of lane candidate features can be easily corrupted by road vehicles and road markers that lead to degradations in the understanding of road scenes, resulting in difficult decision making. We have introduced two main contributions to the interpretation of road scenes in dense traffic environments. First, to obtain robust road scene understanding, we have designed a novel framework combining a lane tracker method integrated with a camera and a radar forward vehicle tracker system, which is especially useful in dense traffic situations. We have introduced an image template occupancy matching method with the integrated vehicle tracker that makes it possible to avoid extracting irrelevant lane features caused by forward target vehicles and road markers. Second, we present a robust multi-lane detection by a tracking algorithm that incudes adjacent lanes as well as ego lanes. We verify a comprehensive experimental evaluation with a real dataset comprised of problematic road scenarios. Experimental result shows that the proposed method is very reliable for multi-lane detection at the presented difficult situations.

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“…The complementary nature of these sensors has inspired previous approaches [ 5 ]. Wang et al [ 6 ] utilized radar detections to guide object search in images, while Yu et al [ 7 ] presented a high-level fusion of camera and radar sensors, leveraging deep learning for object detection and Kalman filtering for tracking. Although recent research tends to favor the elimination of radar sensors in favor of relying solely on camera sensors for advanced and autonomous driving functions, as mentioned by the authors in their review [ 8 ], it is important to note that camera sensors still have significant limitations, particularly in the distance and velocity estimation.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensors System and Deep Learning Models for Object Tracking

El Natour,

Bresson,

Trichet

2023

Sensors

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Autonomous navigation relies on the crucial aspect of perceiving the environment to ensure the safe navigation of an autonomous platform, taking into consideration surrounding objects and their potential movements. Consequently, a fundamental requirement arises to accurately track and predict these objects’ trajectories. Three deep recurrent network architectures were defined to achieve this, fine-tuning their weights to optimize the tracking process. The effectiveness of this proposed pipeline has been assessed, with diverse tracking scenarios demonstrated in both sub-urban and highway environments. The evaluations have yielded promising results, affirming the potential of this approach in enhancing autonomous navigation capabilities.

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Camera-Radar Data Fusion for Target Detection via Kalman Filter and Bayesian Estimation

Cited by 13 publications

References 9 publications

Mixed Traffic Flow State Detection: A Connected Vehicles-Assisted Roadside Radar and Video Data Fusion Scheme

Mixed Traffic Flow State Detection: A Connected Vehicles-Assisted Roadside Radar and Video Data Fusion Scheme

Reliable Road Scene Interpretation Based on ITOM with the Integrated Fusion of Vehicle and Lane Tracker in Dense Traffic Situation

Multi-Sensors System and Deep Learning Models for Object Tracking

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