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Juergen Dickmann

Juergen Dickmann
Mercedes-Benz AG · Radar and Radar Perception

Dr.-Ing.
https://orcid.org/0000-0002-4328-3368

About

220
Publications
72,724
Reads
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2,835
Citations
Additional affiliations
January 2016 - August 2016
DAIMLER AG/Mercedes-Benz
Position
  • Head of Department
Description
  • Dr.-Ing. Juergen Dickmann is head of Radar sensors and radar based perception for highly automated/autonomous driving, Mercedes-Benz AG. In this role he is responsible for the 6th generation at Mercedes-Benz AG and future radar solutions towards autonomous driving. With his group, he introduced AI concepts for automotive Radar and exploited Radar usage beyond ranging and detection for comprehensive environment perception.
March 1997 - present
Daimler
Position
  • Head of Department

Publications

Publications (220)
Article
SpringerOpen,AI perspectives https://aiperspectives.springeropen.com/articles/10.1186/s42467-021-00012-z Automotive radar perception is an integral part of automated driving systems. Radar sensors benefit from their excellent robustness against adverse weather conditions such as snow, fog, or heavy rain. Despite the fact that machine-learning-base...
Presentation
Full-text available
Future demands in Radar Technology and Radar based perception
Chapter
Radar sensors are a key component of automated vehicles. The requirements for radar perception modules are growing more demanding. At the same time, the radar sensors themselves are becoming increasingly sophisticated. Both developments lead to the progression of very complex algorithms. In the field of machine learning, increased task difficulty i...
Presentation
Full-text available
Automotive Radar and AI
Presentation
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Automotive Radar & AI concepts for automated driving
Presentation
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Radar and all weather possebilities and its benefit for highly automated vehicle
Presentation
Full-text available
ICRA 2021 Workshop on Radar Perception for All-Weather Autonomy Programme: https://sites.google.com/view/radar-robotics/home
Preprint
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Grid maps are widely established for the representation of static objects in robotics and automotive applications. Though, incorporating velocity information is still widely examined because of the increased complexity of dynamic grids concerning both velocity measurement models for radar sensors and the representation of velocity in a grid framewo...
Conference Paper
This paper presents the Online Adaptive Fuser: OA-Fuser, a novel method for online adaptive estimation of motion and measurement uncertainties for efficient tracking and fusion by applying a system of several estimators for ongoing noise along with the conventional state and state covariance estimation. In our system, process and measurement noises...
Preprint
This paper introduces BAAS, a new Extended Object Tracking (EOT) and fusion-based label annotation framework for radar detections in the context of autonomous driving. Our framework utilizes Bayesian based tracking, smoothing and eventually fusion methods to provide veritable and precise object trajectories along with shape estimation in order to p...
Preprint
Full-text available
Radar-based road user detection is an important topic in the context of autonomous driving applications. The resolution of conventional automotive radar sensors results in a sparse data representation which is tough to refine during subsequent signal processing. On the other hand, a new sensor generation is waiting in the wings for its application...
Preprint
Conventional sensor systems record information about directly visible objects, whereas occluded scene components are considered lost in the measurement process. Nonline-of-sight (NLOS) methods try to recover such hidden objects from their indirect reflections - faint signal components, traditionally treated as measurement noise. Existing NLOS appro...
Article
Extracting semantic information solely from automotive radar data is a relatively new topic in the radar community. We present a complete pipeline to obtain semantic information for each target measured by a network of radar sensors. Static and dynamic objects are treated in two separate branches: In the first branch, a convolutional neural network...
Preprint
Radar sensors provide a unique method for executing environmental perception tasks towards autonomous driving. Especially their capability to perform well in adverse weather conditions often makes them superior to other sensors such as cameras or lidar. Nevertheless, the high sparsity and low dimensionality of the commonly used detection data level...
Conference Paper
Baseline generation for tracking applications is a difficult task when working with real world radar data. Data sparsity usually only allows an indirect way of estimating the original tracks as most objects' centers are not represented in the data. This article proposes an automated way of acquiring reference trajectories by using a highly accurate...
Preprint
Radar-based road user classification is an important yet still challenging task towards autonomous driving applications. The resolution of conventional automotive radar sensors results in a sparse data representation which is tough to recover by subsequent signal processing. In this article, classifier ensembles originating from a one-vs-one binari...
Preprint
Full-text available
Baseline generation for tracking applications is a difficult task when working with real world radar data. Data sparsity usually only allows an indirect way of estimating the original tracks as most objects' centers are not represented in the data. This article proposes an automated way of acquiring reference trajectories by using a highly accurate...
Preprint
Annotating automotive radar data is a difficult task. This article presents an automated way of acquiring data labels which uses a highly accurate and portable global navigation satellite system (GNSS). The proposed system is discussed besides a revision of other label acquisitions techniques and a problem description of manual data annotation. The...
Preprint
The classification of individual traffic participants is a complex task, especially for challenging scenarios with multiple road users or under bad weather conditions. Radar sensors provide an - with respect to well established camera systems - orthogonal way of measuring such scenes. In order to gain accurate classification results, 50 different f...
Presentation
Full-text available
Interview_Tech.AD2019
Conference Paper
Annotating automotive radar data is a difficult task. This article presents an automated way of acquiring data labels which uses a highly accurate and portable global navigation satellite system (GNSS). The proposed system is discussed besides a revision of other label acquisitions techniques and a problem description of manual data annotation. The...
Chapter
The challenges for sensors and their correlated perception algorithms for driverless vehicles are tremendous. They have to provide more comprehensively than ever before a model of the complete static and dynamic surroundings of the ego-vehicle to understand the correlation of both with reference to the ego vehicle’s movement. For dynamic objects, t...
Conference Paper
The classification of individual traffic participants is a complex task, especially for challenging scenarios with multiple road users or under bad weather conditions. Radar sensors provide an - with respect to well established camera systems - orthogonal way of measuring such scenes. In order to gain accurate classification results, 50 different f...
Conference Paper
Full-text available
Current automotive radar sensors enhance the angular resolution using a multiple-input multiple-output approach. The often applied time-division multiplexing scheme has the drawback of a reduced unambiguous Doppler velocity proportional to the number of transmitters. In this paper, a signal processing scheme is proposed to regain the same unambiguo...
Conference Paper
Full-text available
For autonomous driving high-resolution radar sensors are key components, which have the drawback of high data rates. In order to reduce the amount of sampled data, random samples can be omitted and afterwards reconstructed using compressed sensing methods. A possible application is that not every receiving antenna element demands its own analog-to-...
Presentation
Full-text available
Desdription of requirement changes from to todays ADAS to future L5 (drvless) systems. Development guidline for Radar -HW and Radar perception algorithms. Examples of research today.
Conference Paper
Full-text available
MIMO radar systems create a large virtual aperture to enhance the angular resolution. As a multiplexing scheme often the time-division multiplexing (TDM) procedure is chosen. The drawbacks are a reduced maximal unambiguously detectable Doppler frequency and the need to correct a phase error in angle estimation for relative radial velocities. To ove...
Article
Full-text available
This paper introduces a novel target height estimation approach using a Frequency Modulation Continuous Wave (FMCW) automotive radar. The presented algorithm takes advantage of radar wave multipath propagation to measure the height of objects in the vehicle surroundings. A multipath propagation model is presented first, then a target height is form...
Conference Paper
Full-text available
In an automotive environment the presence of reflecting surfaces cannot be avoided. The electromagnetic wave returning from a target vehicle can get reflected on those surfaces causing a non existing so-called ghost target. For driver assistance systems ghost targets can lead to false decisions and, therefore, they should be detected and avoided. I...
Conference Paper
This paper presents a modified FastSLAM approach for the specific application of radar sensors using the Doppler information to increase the localization and map accuracy. The developed approach is based on the FastSLAM 2.0 algorithm. It is shown how the FastSLAM 2.0 approach can be significantly improved by taking the Doppler information into acco...
Conference Paper
This paper assesses the advantage of exploiting radar wave multipath propagation to measure the height of extended objects using a Frequency Modulation Continuous Wave (FMCW) automotive radar. The proposed height estimation algorithm utilizes the non-line-of-sight (NLOS) propagation from a target to calculate the height of objects in the vehicle's...
Conference Paper
Full-text available
Summary/Overview on demands - concepts and first achievements on Radar and Radar based perception for L5 vehicle.
Conference Paper
Full-text available
How fast does disruptive Technologies find there way into the market. Drvless driving at high Peak? What is technically needed from HW from a Radar developer´s Point of view
Article
For automotive applications, an accurate estimation of the ego-motion is required to make advanced driver assistant systems work reliably. The proposed framework for ego-motion estimation involves two components: The first component is the spatial registration of consecutive scans. In this paper, the reference scan is represented by a sparse Gaussi...
Conference Paper
Full-text available
For automotive radar sensors the Doppler resolution is a key parameter, since it is used to separate targets which are in the same radial distance. For future applications the Doppler signature of targets can be exploited for classification purpose, e.g. discrimination among road users like pedestrians or bicyclists. In this paper, a signal process...
Article
Full-text available
With new generations of high-resolution imaging radars, the orientation of vehicles can be estimated without temporal filtering. This enables time-critical systems to respond even faster. Based on a large data set, this paper compares three generic algorithms for the orientation estimation of a vehicle. An experimental MIMO imaging radar is used to...
Conference Paper
This paper presents new methods for the representation of a vehicle's contour by an oriented rectangle, also known as the bounding box. The parameters of this bounding box are originally modeled probabilistically by a single multivariate Gaussian distribution. This approach incorporates the sensor uncertainties, where the problem of estimating the...
Conference Paper
Based on exerience made ofer many years developing Radars, the requirenets over the entire value added chain in all respects is shown. From vehicle package to frequency Regulation over sensor - HW requirements to the different Options and challenges for Radar based Signal processing. The future Needs are elaborated.
Conference Paper
Full-text available