Radar and Camera Sensor Fusion with ROS for Autonomous Driving

Kumar, Rahul; Jayashankar, Sujay

doi:10.1109/iciip47207.2019.8985782

Cited by 8 publications

(4 citation statements)

References 21 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%

“…Finally, the target data are fused based on Bayesian estimation. In [27], a model implemented using the Robot Operating System (ROS) environment is proposed to handle radar and camera sensor synchronization and fusion for accurate object detection. In [28], a spatio-temporal synchronization method for roadside radar and camera is proposed to fuse targets and match vehicle trajectories.…”

Section: B Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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.

View full text Add to dashboard Cite

show abstract

“…Consequently, the camera and radar sensors complement each other in providing the most comprehensive view of the environment through the data they provide to the autonomous vehicle. In [2] and [3], it is stated that sensor fusion is the best approach when it comes to automated vehicles as it allows for a deeper understanding of the surrounding by accounting for different variables. Cameras are indispensable to autonomous vehicles through the visualization of the surrounding, thus facilitating many of the processes essential to self-driving.…”

Section: Extended Abstractmentioning

confidence: 99%

“…However, camera-only based solutions can be computationally taxing as most high-definition cameras produce millions of pixels per framework and require advanced software and hardware capabilities to process them. In [3], the authors discuss how radars can complement cameras by addressing the requirement for localization. However, they generate less angular accuracy and capture less data than the expensive Lidar.…”

Section: Extended Abstractmentioning

confidence: 99%