Providentia -- A Large-Scale Sensor System for the Assistance of Autonomous Vehicles and Its Evaluation

Krämmer, Annkathrin; Schöller, Christoph; Gulati, Dhiraj; Lakshminarasimhan, Venkatnarayanan; Kurz, Franz; Rosenbaum, Dominik; Lenz, Claus; Knoll, Alois

doi:10.48550/arxiv.1906.06789

Cited by 4 publications

(5 citation statements)

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“…The center estimation enhances the relation of object points by learning to estimate object-specific centers from point-specific features. They also propose a feature map warping module to relieve the inconsistency between the positions of the refined anchors 1 and the classification scores by calculating classification as a consensus of object-related regions from the regression branch. With the given extensions and the ability of taking the auxiliary network out during inference, the SA-SSD network reaches 25 Hz, while improving the best scores of the single-stage detectors and reaching the accuracy of two-stage detectors.…”

Section: A 3d Object Detection Architecturesmentioning

confidence: 99%

A Survey of Robust 3D Object Detection Methods in Point Clouds

Zimmer¹,

Erçelik²,

Zhou³

et al. 2022

Preprint

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The purpose of this work is to review the stateof-the-art LiDAR-based 3D object detection methods, datasets, and challenges. We describe novel data augmentation methods, sampling strategies, activation functions, attention mechanisms, and regularization methods. Furthermore, we list recently introduced normalization methods, learning rate schedules and loss functions. Moreover, we also cover advantages and limitations of 10 novel autonomous driving datasets. We evaluate novel 3D object detectors on the KITTI, nuScenes, and Waymo dataset and show their accuracy, speed, and robustness. Finally, we mention the current challenges in 3D object detection in LiDAR point clouds and list some open issues. I. INTRODUCTIONAutonomous driving (AD) is increasingly gaining attention worldwide and can lead to many advantages to the population. The potential of this technology is clear, and it is predicted that it will dramatically change the transportation sector. The application of robust 3D Object Detection in autonomous driving is vital. In this context, robustness means to cope with detection errors (e.g., occlusions) and erroneous input (e.g., sensor noise). Sometimes, the environment is not optimal for detecting objects (e.g., in rain, snow, fog or bright sunlight). Robust detection stands for a good generalization to detect unseen objects in a different environment.Besides, infrastructure sensors (like the ones in the Providentia [1] system) support the perception of the environment, improve traffic safety, and offer higher robustness and performance through different mounting positions. Multiple view points help to better detect objects in 3D (position, orientation, and size), and to increase the robustness against sunlight, snow, dust, and fog.The aim of this survey paper is to provide an overview of novel 3D object detection methods and tricks. The best LiDAR-based 3D object detection algorithm on the KITTI dataset is 68.63% more accurate (on the 3D car class) than the best camera-only 3D object detection algorithm on that dataset. At night, the LiDAR performs better and is able to detect objects within a range of 50-60 m (Ouster OS1-64 gen. 2 LiDAR).Our contribution is partitioned into the following:• We provide state-of-the-art LiDAR-based 3D object detectors and show their accuracy, speed, and robustness.

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Section: A 3d Object Detection Architecturesmentioning

confidence: 99%

A Survey of Robust 3D Object Detection Methods in Point Clouds

Zimmer¹,

Erçelik²,

Zhou³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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“…They are used in urban locations for example in the Ko-PER dataset [48], in the test area autonomous driving Baden-Württemberg [71], and at the AIM test site [156]. Infrastructure sensors at highways or motorways are used in [157] and at the Providentia sensor system [158], where the latter source assesses its performance using a helicopterbased system, which is covered in Sec. VIII-C3.…”

Section: Reference Data From Non-road Usersmentioning

confidence: 99%

“…A previous publication from the same project [165] describes how such a helicopter-based system can be used to validate the behavior of ADAS. Furthermore, a helicopter-based reference data generation is also described and used in [158] to assess a static infrastructure sensor system.…”

Section: Reference Data From Non-road Usersmentioning

confidence: 99%

A Review of Testing Object-Based Environment Perception for Safe Automated Driving

Hoss,

Scholtes,

Eckstein

2021

Preprint

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Safety assurance of automated driving systems must consider uncertain environment perception. This paper reviews literature addressing how perception testing is realized as part of safety assurance. We focus on testing for verification and validation purposes at the interface between perception and planning, and structure our analysis along the three axes 1) test criteria and metrics, 2) test scenarios, and 3) reference data. Furthermore, the analyzed literature includes related safety standards, safetyindependent perception algorithm benchmarking, and sensor modeling. We find that the realization of safety-aware perception testing remains an open issue since challenges concerning the three testing axes and their interdependencies currently do not appear to be sufficiently solved.

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“…Recent studies have introduced machine learning techniques for the analysis and mitigation of radar interference [8,9]. These methods, while sometimes useful, frequently suffer from flaws like a high tendency for false positives and negatives and an inability to adjust to real-time variations in blockage performance [10]. These restrictions served as the driving force behind our efforts to create an adaptive radar blockage detection method and intelligent computing-based blockage prevention techniques that were especially suited for radar-based automatic driving systems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive blockage detection and prevention for radar chip-based autonomous driving systems

Chen,

Dai,

Liu

et al. 2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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This paper presents an radar blockage recognition approach along with blindness prevention measures utilizing intelligent computing methods to address the challenges in radar chip-based autonomous driving systems. A process and algorithm are initially created for detection, enabling real-time and precise identification. The adaptive blockage performance controller and the automatic diagnosis and recovery strategy are then proposed as part of the adaptive blockage prevention strategy, which enables the system to quickly return to its normal operating state by intelligently adjusting parameters when the sensor blockage occurs. The conventional linear threshold methods are replaced with nonlinear machine learning to deal with complex performance variations, with the aim of lowering false positives and negatives through real-time optimization. A comparative experiment is conducted to show the effectiveness.

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Providentia -- A Large-Scale Sensor System for the Assistance of Autonomous Vehicles and Its Evaluation

Cited by 4 publications

References 0 publications

A Survey of Robust 3D Object Detection Methods in Point Clouds

A Survey of Robust 3D Object Detection Methods in Point Clouds

A Review of Testing Object-Based Environment Perception for Safe Automated Driving

Adaptive blockage detection and prevention for radar chip-based autonomous driving systems

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