A multi-sensor fusion system for moving object detection and tracking in urban driving environments

By Hyunggi Cho, Young-Woo Seo, B. Kumar, R. Rajkumar. IEEE International Conference on Robotics and Automation, 2014

401 citations recorded by the source corpus.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/0574c83b9582ba184ac7078b9bdd75d65c92cc93

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In Automotive Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

An Empirical Evaluation of Deep Learning on Highway Driving (2015, background): “Cho et al. [3] uses multiple sensors, such as LIDAR, radar, and computer vision for object detection.”
Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather (2019, methodology): “Existing fusion methods have been proposed mostly for lidar-camera setups [65, 11, 43, 36, 12], as a result of the limited sensor inputs in existing training datasets [66, 19, 59].”
Computer vision and deep learning techniques for pedestrian detection and tracking: A survey (2018, background): “Moreover, as will be discussed in the following section ( Section 4 ), motion information, which is generally extracted using a 3D vision sys-tem, could be obtained following a bi-dimensional approach [3,18,19,21,44,52,53,55,65,76,92,95,135,136] .”
Internet of vehicles in big data era (2018, background): “The usage of classic computer vision technologies in autonomous driving can be found in [118]−[120].”
Precise and efficient model-based vehicle tracking method using Rao-Blackwellized and scaling series particle filters (2016, background): “Some LiDAR-based DATMO approaches have been developed over the past decade [16], [17], [18], [19], [6], [7], [20].”
Research Advances and Challenges of Autonomous and Connected Ground Vehicles (2019, methodology): “In radar-lidar-camera fusion, the object detection and classiﬁcation results from the camera are utilized to improve the tracking model selection, data association and movement classiﬁcation in L OCALIZATION AND M APPING M ETHODS U SED BY S OME R ESEARCH V EHICLES [1] radar and lidar [322].”
F-cooper: feature based cooperative perception for autonomous vehicle edge computing system using 3D point clouds (2019, background): “Among related works on cooperative perception for autonomous vehicles [6, 26], we find that their main focus is on improving the individual vehicle’s precision, overlooking benefits from cooperative perception.”
Self-Driving Cars: A Survey (2019, methodology): “Cho et al. [CHO14] describe the new MOT subsystem used by the new experimental autonomous vehicle of the Carnegie Mellon University.”
MulRan: Multimodal Range Dataset for Urban Place Recognition (2020, methodology): “On a mobile platform, radar has been used for dynamic object detection [18, 19] as an auxiliary sensor and has not been studied much in SLAM research because the major challenge of using radar arises from sparse, noisy sensor characteristics compared to LiDAR.”
Lidar for Autonomous Driving: The Principles, Challenges, and Trends for Automotive Lidar and Perception Systems (2020, methodology): “As a non-linear version of KF, EKF is utilized for LiDAR object tracking in [33].”
An End-to-End Deep Neural Network for Autonomous Driving Designed for Embedded Automotive Platforms (2019, methodology): “This recommends the J-Net model for deployment on low-performing embedded automotive platforms.”
StarNet: Targeted Computation for Object Detection in Point Clouds (2019, background): “As a result, self-driving cars (SDC) are equipped with a variety of sensors such as cameras, LiDARs, and radars [8, 39], which the perception system must use to create an accurate 3D representation of the world.”