Reliable Orientation Estimation of Vehicles in High-Resolution Radar Images

Roos, Fabian; Kellner, Dominik; Dickmann, Jürgen; Waldschmidt, Christian

doi:10.1109/tmtt.2016.2586476

Cited by 35 publications

(24 citation statements)

References 13 publications

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“…Several of such approaches have been proposed for radar-based tracking. These include clustering and extraction of reference points as in [1], [2], or fitting bounding boxes and L-shapes [3], [4], reflection center models [5], or velocity profiles [6]- [8] to the data. While preprocessing routines are oftentimes effective, computationally fast, and lead to clearly separable system architectures, they face difficulties if the data from a single time step is ambiguous and the correct meta-measurement cannot be easily extracted.…”

Section: Introductionmentioning

confidence: 99%

Tracking Multiple Vehicles Using a Variational Radar Model

Scheel

Dietmayer

2019

IEEE Trans. Intell. Transport. Syst.

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High-resolution radar sensors are able to resolve multiple detections per object and therefore provide valuable information for vehicle environment perception. For instance, multiple detections allow to infer the size of an object or to more precisely measure the object's motion. Yet, the increased amount of data raises the demands on tracking modules: measurement models that are able to process multiple detections for an object are necessary and measurement-to-object associations become more complex. This paper presents a new variational radar model for tracking vehicles using radar detections and demonstrates how this model can be incorporated into a Random-Finite-Setbased multi-object filter. The measurement model is learned from actual data using variational Gaussian mixtures and avoids excessive manual engineering. In combination with the multiobject tracker, the entire process chain from the raw measurements to the resulting tracks is formulated probabilistically. The presented approach is evaluated on experimental data and it is demonstrated that the data-driven measurement model outperforms a manually designed model.

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Section: Introductionmentioning

confidence: 99%

Tracking Multiple Vehicles Using a Variational Radar Model

Scheel

Dietmayer

2019

IEEE Trans. Intell. Transport. Syst.

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“…Bounding Box Estimation in Radar Data: Instead of classifying objects, another task is to estimate a bounding box, i.e., position, orientation and dimension of an object. Roos et al [11] present an approach to estimate the orientation as well as the dimension of a vehicle using highresolution radar. For this purpose, single measurements of two radars are collected and enhanced versions of orientated bounding box algorithms and the L-fit algorithm are applied.…”

Section: Related Workmentioning

confidence: 99%

2D Car Detection in Radar Data with PointNets

Danzer

Griebel

Bach

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

123

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For many automated driving functions, a highly accurate perception of the vehicle environment is a crucial prerequisite. Modern high-resolution radar sensors generate multiple radar targets per object, which makes these sensors particularly suitable for the 2D object detection task. This work presents an approach to detect 2D objects solely depending on sparse radar data using PointNets. In literature, only methods are presented so far which perform either object classification or bounding box estimation for objects. In contrast, this method facilitates a classification together with a bounding box estimation of objects using a single radar sensor. To this end, PointNets are adjusted for radar data performing 2D object classification with segmentation, and 2D bounding box regression in order to estimate an amodal 2D bounding box. The algorithm is evaluated using an automatically created dataset which consist of various realistic driving maneuvers. The results show the great potential of object detection in high-resolution radar data using PointNets.

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“…While sensor fusion and tracking approaches are advanced and theoretically supported, object detection or initialization is reasonably engineered. Hand engineered object detection based on box fitting with Lshapes in laser [11], [13] or radar measurements [12], suffers from limiting assumptions and simplifications regarding sensor, object, and environment features. Commonly, heuristic parameter tuning is required, e.g.…”

Section: Related Workmentioning

confidence: 99%

Object Detection on Dynamic Occupancy Grid Maps Using Deep Learning and Automatic Label Generation

Hoermann¹,

Henzler²,

Bach³

et al. 2018

2018 IEEE Intelligent Vehicles Symposium (IV)

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Fig. 1: Object detection using deep learning and grid fusion.Abstract-We tackle the problem of object detection and pose estimation in a shared space downtown environment. For perception multiple laser scanners with 360 • coverage were fused in a dynamic occupancy grid map (DOGMa). A singlestage deep convolutional neural network is trained to provide object hypotheses comprising of shape, position, orientation and an existence score from a single input DOGMa. Furthermore, an algorithm for offline object extraction was developed to automatically label several hours of training data. The algorithm is based on a two-pass trajectory extraction, forward and backward in time. Typical for engineered algorithms, the automatic label generation suffers from misdetections, which makes hard negative mining impractical. Therefore, we propose a loss function counteracting the high imbalance between mostly static background and extremely rare dynamic grid cells. Experiments indicate, that the trained network has good generalization capabilities since it detects objects occasionally lost by the label algorithm. Evaluation reaches an average precision (AP) of 75.9 %.

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Reliable Orientation Estimation of Vehicles in High-Resolution Radar Images

Cited by 35 publications

References 13 publications

Tracking Multiple Vehicles Using a Variational Radar Model

Tracking Multiple Vehicles Using a Variational Radar Model

2D Car Detection in Radar Data with PointNets

Object Detection on Dynamic Occupancy Grid Maps Using Deep Learning and Automatic Label Generation

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