HydraFusion: Context-Aware Selective Sensor Fusion for Robust and Efficient Autonomous Vehicle Perception

@article{Malawade2022HydraFusionCS,
  title={HydraFusion: Context-Aware Selective Sensor Fusion for Robust and Efficient Autonomous Vehicle Perception},
  author={Arnav Vaibhav Malawade and Trier Mortlock and Mohammad Abdullah Al Faruque},
  journal={2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)},
  year={2022},
  pages={68-79}
}
Although autonomous vehicles (AVs) are expected to revolutionize transportation, robust perception across a wide range of driving contexts remains a significant challenge. Techniques to fuse sensor data from camera, radar, and lidar sensors have been proposed to improve AV perception. However, existing methods are insufficiently robust in difficult driving contexts (e.g., bad weather, low light, sensor obstruction) due to rigidity in their fusion implementations. These methods fall into two… 

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References

SHOWING 1-10 OF 31 REFERENCES
SelectFusion: A Generic Framework to Selectively Learn Multisensory Fusion
TLDR
This work proposes SelectFusion, an end-to-end selective sensor fusion module which can be applied to useful pairs of sensor modalities such as monocular images and inertial measurements, depth images and LIDAR point clouds, and investigates the effectiveness of the different fusion strategies in attending the most reliable features.
Selective Sensor Fusion for Neural Visual-Inertial Odometry
TLDR
A novel end-to-end selective sensor fusion framework for monocular VIO is proposed, which fuses monocular images and inertial measurements in order to estimate the trajectory whilst improving robustness to real-life issues, such as missing and corrupted data or bad sensor synchronization.
Real-Time Hybrid Multi-Sensor Fusion Framework for Perception in Autonomous Vehicles
TLDR
A new hybrid multi-sensor fusion pipeline configuration that performs environment perception for autonomous vehicles such as road segmentation, obstacle detection, and tracking using a proposed encoder-decoder based Fully Convolutional Neural Network and a traditional Extended Kalman Filter nonlinear state estimator method.
Context-Aided Sensor Fusion for Enhanced Urban Navigation
TLDR
This article details an advanced GNSS/IMU fusion system based on a context-aided Unscented Kalman filter for navigation in urban conditions that suits the use of fusion algorithms for deploying Intelligent Transport Systems in urban environments.
A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection
TLDR
The proposed CameraRadarFusion Net (CRF-Net) automatically learns at which level the fusion of the sensor data is most beneficial for the detection result, and is able to outperform a state-of-the-art image-only network for two different datasets.
A Survey on 3D Object Detection Methods for Autonomous Driving Applications
TLDR
This paper presents an overview of 3D object detection methods and prevalently used sensors and datasets in AVs, and discusses and categorizes the recent works based on sensors modalities into monocular, point cloud-based, and fusion methods.
FEEL: Fast, Energy-Efficient Localization for Autonomous Indoor Vehicles
TLDR
FEEL is proposed – an indoor localization system that uses a fusion of three low-energy sensors: IMU, UWB, and radar that provides a localization accuracy of sub-7 cm with an ultra-low latency of around 3 ms and yields up to 20% energy savings with only a marginal trade off in accuracy.
RADIATE: A Radar Dataset for Automotive Perception
TLDR
This paper presents the RAdar Dataset In Adverse weather (RADIATE), aiming to facilitate research on object detection, tracking and scene understanding using radar sensing for safe autonomous driving, and is the first public radar dataset which provides high-resolution radar images on public roads with a large amount of road actors labelled.
PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation
TLDR
This work evaluates PointFusion on two distinctive datasets: the KITTI dataset that features driving scenes captured with a lidar-camera setup, and the SUN-RGBD dataset that captures indoor environments with RGB-D cameras.
A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research
TLDR
The physical fundamentals, principle functioning, and electromagnetic spectrum used to operate the most common sensors used in perception systems (ultrasonic, RADAR, LiDAR, cameras, IMU, GNSS, RTK, etc.) are presented.
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