Object-Level Fusion for Surround Environment Perception in Automated Driving Applications

Aeberhard, Michael

doi:10.51202/9783186804129

Cited by 22 publications

(12 citation statements)

References 183 publications

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“…In this work, the reference point can be either at a corner, an edge center, or the center of the box, see Fig. 3, as also used in [15], [52]. The position in terms of a reference point is thus defined as…”

Section: Position Measurements With Reference Point Selectionmentioning

confidence: 99%

Grid-Based Object Tracking With Nonlinear Dynamic State and Shape Estimation

Steyer

Lenk

Kellner

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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Object tracking is crucial for planning safe maneuvers of mobile robots in dynamic environments, in particular for autonomous driving with surrounding traffic participants. Multistage processing of sensor measurement data is thereby required to obtain abstracted high-level objects, such as vehicles. This also includes sensor fusion, data association, and temporal filtering. Often, an early-stage object abstraction is performed, which, however, is critical, as it results in information loss regarding the subsequent processing steps. We present a new grid-based object tracking approach that, in contrast, is based on already fused measurement data. The input is thereby pre-processed, without abstracting objects, by the spatial grid cell discretization of a dynamic occupancy grid, which enables a generic multi-sensor detection of moving objects. On the basis of already associated occupied cells, presented in our previous work, this paper investigates the subsequent object state estimation. The object pose and shape estimation thereby benefit from the freespace information contained in the input grid, which is evaluated to determine the current visibility of extracted object parts. An integrated object classification concept further enhances the assumed object size. For a precise dynamic motion state estimation, radar Doppler velocity measurements are integrated into the input data and processed directly on the object-level. Our approach is evaluated with real sensor data in the context of autonomous driving in challenging urban scenarios.

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Section: Position Measurements With Reference Point Selectionmentioning

confidence: 99%

Grid-Based Object Tracking With Nonlinear Dynamic State and Shape Estimation

Steyer

Lenk

Kellner

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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“…Cells behind a measurement are modeled as uncertain. The methodology described in [18] is adopted to obtain evidence masses from occupancy probabilities in the frame of discernment (1). One cell i of the resulting measurement grid contains masses m t,z ({S, D} i ), m t,z (F i ), and m t,z (Ω i ).…”

Section: B Measurement Gridmentioning

confidence: 99%

“…Sensor fusion can be done on different levels of information processing. Often detection of objects is performed per sensor, and then the hypotheses are fused on a highlevel abstraction like in [1]. Occupancy grids, introduced in [2], provide a possibility for low-level data fusion with less information loss and solve the data association problem implicitly by a spatial mapping into a cell-discretized map.…”

Section: Introductionmentioning

confidence: 99%

Radar-based Dynamic Occupancy Grid Mapping and Object Detection

Diehl

Feicho

Schwambach

et al. 2020

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)

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Environment modeling utilizing sensor data fusion and object tracking is crucial for safe automated driving. In recent years, the classical occupancy grid map approach, which assumes a static environment, has been extended to dynamic occupancy grid maps, which maintain the possibility of a lowlevel data fusion while also estimating the position and velocity distribution of the dynamic local environment. This paper presents the further development of a previous approach. To the best of the author's knowledge, there is no publication about dynamic occupancy grid mapping with subsequent analysis based only on radar data. Therefore in this work, the data of multiple radar sensors are fused, and a grid-based object tracking and mapping method is applied. Subsequently, the clustering of dynamic areas provides high-level object information. For comparison, also a lidar-based method is developed. The approach is evaluated qualitatively and quantitatively with real-world data from a moving vehicle in urban environments. The evaluation illustrates the advantages of the radar-based dynamic occupancy grid map, considering different comparison metrics.

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“…In the domain of safety for automated driving, plausibility checking methods have been applied to verify the environment perception of (overlapping) in-vehicle sensors -including camera, Lidar, and Radar modalities -and to infer a probability of existence metric [11], [21], [22]. Most of the applied checks there are single-signal and redundancy checks.…”

Section: Related Workmentioning

confidence: 99%

A Plausibility-based Fault Detection Method for High-level Fusion Perception Systems

Geissler,

Unnervik,

Paulitsch

2020

Preprint

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Trustworthy environment perception is the fundamental basis for the safe deployment of automated agents such as self-driving vehicles or intelligent robots. The problem remains that such trust is notoriously difficult to guarantee in the presence of systematic faults, e.g. non-traceable errors caused by machine learning functions. One way to tackle this issue without making rather specific assumptions about the perception process is plausibility checking. Similar to the reasoning of human intuition, the final outcome of a complex black-box procedure is verified against given expectations of an object's behavior. In this article, we apply and evaluate collaborative, sensor-generic plausibility checking as a mean to detect empirical perception faults from their statistical fingerprints. Our real use case is next-generation automated driving that uses a roadside sensor infrastructure for perception augmentation, represented here by test scenarios at a German highway and a city intersection. The plausibilization analysis is integrated naturally in the object fusion process, and helps to diagnose known and possibly yet unknown faults in distributed sensing systems.

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Object-Level Fusion for Surround Environment Perception in Automated Driving Applications

Cited by 22 publications

References 183 publications

Grid-Based Object Tracking With Nonlinear Dynamic State and Shape Estimation

Grid-Based Object Tracking With Nonlinear Dynamic State and Shape Estimation

Radar-based Dynamic Occupancy Grid Mapping and Object Detection

A Plausibility-based Fault Detection Method for High-level Fusion Perception Systems

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