3D object tracking in driving environment: A short review and a benchmark dataset

Girão, Pedro; Asvadi, Alireza; Peixoto, Paulo; Nunes, Urbano

doi:10.1109/itsc.2016.7795523

Cited by 7 publications

(5 citation statements)

References 38 publications

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“…These may include pedestrians, animals, and bicycles, whose behavior the vehicle may have limited knowledge about [69]. While effective methods for detecting, classifying, and tracking objects exist [47], many of these approaches make use of deep learning and probabilistic modeling in order to characterize the behavior of moving objects. Thus, there is an inherent uncertainty in the description of the vehicle's environment.…”

Section: Modeling Sensor Localization and Situational Uncertaintymentioning

confidence: 99%

An Empirical Analysis of the Use of Real-Time Reachability for the Safety Assurance of Autonomous Vehicles

Musau¹,

Hamilton²,

Lopez³

et al. 2022

Preprint

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Providing provable assertions of safety at runtime is crucial for autonomous systems.• Autonomous systems are tasked with operating in dynamic and uncertain environments.• Reachability analysis is adept in analyzing the correctness of uncertain systems.• The Simplex Architecture can be used to assure the safety of complex components.• Simplex approaches can be used to assure the safety of machine learning components.

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Section: Modeling Sensor Localization and Situational Uncertaintymentioning

confidence: 99%

An Empirical Analysis of the Use of Real-Time Reachability for the Safety Assurance of Autonomous Vehicles

Musau¹,

Hamilton²,

Lopez³

et al. 2022

Preprint

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“…Here, we present the most recent and relevant ones published in the last ten years. For earlier works, readers are referred to Petrovskaya et al (2012), Bernini et al (2014) and Girao et al (2016).…”

Section: Moving Objects Trackingmentioning

confidence: 99%

Self-Driving Cars: A Survey

Badué¹,

Guidolini²,

Carneiro³

et al. 2019

Preprint

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We survey research on self-driving cars published in the literature focusing on autonomous cars developed since the DARPA challenges, which are equipped with an autonomy system that can be categorized as SAE level 3 or higher. The architecture of the autonomy system of self-driving cars is typically organized into the perception system and the decision-making system. The perception system is generally divided into many subsystems responsible for tasks such as self-driving-car localization, static obstacles mapping, moving obstacles detection and tracking, road mapping, traffic signalization detection and recognition, among others. The decision-making system is commonly partitioned as well into many subsystems responsible for tasks such as route planning, path planning, behavior selection, motion planning, and control. In this survey, we present the typical architecture of the autonomy system of self-driving cars. We also review research on relevant methods for perception and decision making. Furthermore, we present a detailed description of the architecture of the autonomy system of the self-driving car developed at the Universidade Federal do Espírito Santo (UFES), named Intelligent Autonomous Robotics Automobile (IARA). Finally, we list prominent self-driving car research platforms developed by academia and technology companies, and reported in the media.

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“…In this paper, we offer a comprehensive review of change detection using 3D point clouds. We review the 3D CD methods used in remote sensing applications without integrating the 3D scene flow [48][49][50] or 3D object tracking [51,52] methods. We introduce distance-based methods and learning-based methods with a focus on deep learning-based ones.…”

Section: Introductionmentioning

confidence: 99%

Three Dimensional Change Detection Using Point Clouds: A Review

et al. 2022

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Change detection is an important step for the characterization of object dynamics at the earth’s surface. In multi-temporal point clouds, the main challenge is to detect true changes at different granularities in a scene subject to significant noise and occlusion. To better understand new research perspectives in this field, a deep review of recent advances in 3D change detection methods is needed. To this end, we present a comprehensive review of the state of the art of 3D change detection approaches, mainly those using 3D point clouds. We review standard methods and recent advances in the use of machine and deep learning for change detection. In addition, the paper presents a summary of 3D point cloud benchmark datasets from different sensors (aerial, mobile, and static), together with associated information. We also investigate representative evaluation metrics for this task. To finish, we present open questions and research perspectives. By reviewing the relevant papers in the field, we highlight the potential of bi- and multi-temporal point clouds for better monitoring analysis for various applications.

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3D object tracking in driving environment: A short review and a benchmark dataset

Cited by 7 publications

References 38 publications

An Empirical Analysis of the Use of Real-Time Reachability for the Safety Assurance of Autonomous Vehicles

An Empirical Analysis of the Use of Real-Time Reachability for the Safety Assurance of Autonomous Vehicles

Self-Driving Cars: A Survey

Three Dimensional Change Detection Using Point Clouds: A Review

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