Stuttgart, Baden-Württemberg, Deutschland
Kontaktinformationen
1830 Follower:innen
500+ Kontakte
Info
Artikel von Fabian Flohr
-
IEEE Workshop on Unsupervised Learning for Automated Driving
IEEE Workshop on Unsupervised Learning for Automated Driving
Von Fabian Flohr
Aktivitäten
-
📢 Final 4 days (!) to apply for 2 #PhD #vacancies on *Acoustic Perception for Intelligent Vehicles* at Delft University of Technology together with…
📢 Final 4 days (!) to apply for 2 #PhD #vacancies on *Acoustic Perception for Intelligent Vehicles* at Delft University of Technology together with…
Beliebt bei Fabian Flohr
-
Yesterday I successfully defended my doctoral thesis. Thanks to all the people making this possible. Thanks to Fergus Neville for his interest in my…
Yesterday I successfully defended my doctoral thesis. Thanks to all the people making this possible. Thanks to Fergus Neville for his interest in my…
Beliebt bei Fabian Flohr
-
Looking forward to meet you next week at the next #EmbodiedAI Meetup in #Munich organized by DeepScenario!
Looking forward to meet you next week at the next #EmbodiedAI Meetup in #Munich organized by DeepScenario!
Geteilt von Fabian Flohr
Berufserfahrung und Ausbildung
Veröffentlichungen
-
Context-based path prediction for targets with switching dynamics
Springer US
Anticipating future situations from streaming sensor data is a key perception challenge for mobile robotics and automated vehicles. We address the problem of predicting the path of objects with multiple dynamic modes. The dynamics of such targets can be described by a Switching Linear Dynamical System (SLDS). However, predictions from this probabilistic model cannot anticipate when a change in dynamic mode will occur. We propose to extract various types of cues with computer vision to provide…
Anticipating future situations from streaming sensor data is a key perception challenge for mobile robotics and automated vehicles. We address the problem of predicting the path of objects with multiple dynamic modes. The dynamics of such targets can be described by a Switching Linear Dynamical System (SLDS). However, predictions from this probabilistic model cannot anticipate when a change in dynamic mode will occur. We propose to extract various types of cues with computer vision to provide context on the target’s behavior, and incorporate these in a Dynamic Bayesian Network (DBN). The DBN extends the SLDS by conditioning the mode transition probabilities on additional context states. We describe efficient online inference in this DBN for probabilistic path prediction, accounting for uncertainty in both measurements and target behavior. Our approach is illustrated on two scenarios in the …
-
The EuroCity Persons Dataset: A Novel Benchmark for Object Detection
IEEE
Big data has had a great share in the success of deep learning in computer vision. Recent works suggest that there is significant further potential to increase object detection performance by utilizing even bigger datasets. In this paper, we introduce the EuroCity Persons dataset, which provides a large number of highly diverse, accurate and detailed annotations of pedestrians, cyclists and other riders in urban traffic scenes. The images for this dataset were collected on-board a moving…
Big data has had a great share in the success of deep learning in computer vision. Recent works suggest that there is significant further potential to increase object detection performance by utilizing even bigger datasets. In this paper, we introduce the EuroCity Persons dataset, which provides a large number of highly diverse, accurate and detailed annotations of pedestrians, cyclists and other riders in urban traffic scenes. The images for this dataset were collected on-board a moving vehicle in 31 cities of 12 European countries. With over 238200 person instances manually labeled in over 47300 images, EuroCity Persons is nearly one order of magnitude larger than person datasets used previously for benchmarking. The dataset furthermore contains a large number of person orientation annotations (over 211200). We optimize four state-of-the-art deep learning approaches (Faster R-CNN, R-FCN, SSD and YOLOv3) to serve as baselines for the new object detection benchmark. In experiments with previous datasets we analyze the generalization capabilities of these detectors when trained with the new dataset. We furthermore study the effect of the training set size, the dataset diversity (day-vs. night-time, geographical region), the dataset detail (ie availability of object orientation information) and the annotation quality on the detector performance. Finally, we analyze error sources and discuss the road ahead.
-
Vulnerable road user detection and orientation estimation for context-aware automated driving
Amsterdam University
This thesis addresses the detection, segmentation and orientation estimation of persons in visual data. In particular, the aim of this work is to establish an accurate machine representation of the Vulnerable Road Users (VRU, eg pedestrians, cyclists) by using image-based cues to support context-aware automated driving.
-
A survey on leveraging deep neural networks for object tracking
IEEE
Object tracking is the task of estimating over time the state of a single or multiple objects based on noisy measurements received from one or several sensors. The field of object tracking spans over several application domains ranging from military radar systems and sensor fusion approaches, to today's computer vision tracking methods employed in consumer electronics and surveillance systems. It also plays a substantial role in autonomous driving. In recent years, the use of deep neural…
Object tracking is the task of estimating over time the state of a single or multiple objects based on noisy measurements received from one or several sensors. The field of object tracking spans over several application domains ranging from military radar systems and sensor fusion approaches, to today's computer vision tracking methods employed in consumer electronics and surveillance systems. It also plays a substantial role in autonomous driving. In recent years, the use of deep neural networks has spiked in various fields, due to their impressive performance in detection and classification tasks. This aspect also makes these methods applicable to object tracking. Therefore, the aim of this survey is to give the reader a brief yet comprehensive start into the widespread field of object tracking with a focus on the latest deep-learning based extensions and approaches. At first, traditional non-deep tracking systems are …
-
Advancing active safety towards the protection of vulnerable road users: the PROSPECT project
National Highway Traffic Safety Administration
Accidents involving Vulnerable Road Users (VRU) are still a very significant issue for road safety. According to the World Health Organisation, pedestrian and cyclist deaths account for more than 25% of all road traffic deaths worldwide [1]. Autonomous Emergency Braking Systems have the potential to improve safety for these VRU groups.
The PROSPECT project (Proactive Safety for Pedestrians and Cyclists) aims to significantly improve the effectiveness of active VRU safety systems compared to…Accidents involving Vulnerable Road Users (VRU) are still a very significant issue for road safety. According to the World Health Organisation, pedestrian and cyclist deaths account for more than 25% of all road traffic deaths worldwide [1]. Autonomous Emergency Braking Systems have the potential to improve safety for these VRU groups.
The PROSPECT project (Proactive Safety for Pedestrians and Cyclists) aims to significantly improve the effectiveness of active VRU safety systems compared to those currently on the market by expanding the scope of scenarios addressed by the systems and improving the overall system performance. The project pursues an integrated approach: Newest available accident data combined with naturalistic observations and HMI guidelines represent key inputs for the system specifications, which form the basis for the system development. For system development, two main aspects are considered: advanced sensor processing with situation analysis, and intervention strategies including braking and steering. All these concepts are implemented in several vehicle prototypes. Special emphasis is put on balancing system performance in critical scenarios and avoiding undesired system activations. -
A new benchmark for vision-based cyclist detection
IEEE
Significant progress has been achieved over the past decade on vision-based pedestrian detection; this has led to active pedestrian safety systems being deployed in most mid- to high-range cars on the market. Comparatively little effort has been spent on vision-based cyclist detection, especially when it concerns quantitative performance analysis on large datasets. We present a large-scale experimental study on cyclist detection where we examine the currently most promising object detection…
Significant progress has been achieved over the past decade on vision-based pedestrian detection; this has led to active pedestrian safety systems being deployed in most mid- to high-range cars on the market. Comparatively little effort has been spent on vision-based cyclist detection, especially when it concerns quantitative performance analysis on large datasets. We present a large-scale experimental study on cyclist detection where we examine the currently most promising object detection methods; we consider Aggregated Channel Features, Deformable Part Models and Region-based Convolutional Neural Networks. We also introduce a new method called Stereo-Proposal based Fast R-CNN (SP-FRCN) to detect cyclists based on stereo proposals and Fast R-CNN (FRCN) framework. Experiments are performed on a dataset containing 22161 annotated cyclist instances in over 30000 images, recorded from a moving vehicle in the urban traffic of Beijing. Results indicate that all the three solution families can reach top performance around 0.89 average precision on the easy case, but the performance drops gradually with the difficulty increasing. The dataset including rich annotations, stereo images and evaluation scripts (termed “Tsinghua-Daimler Cyclist Benchmark”) is made public to the scientific community, to serve as a common point of reference for future research.
-
A new benchmark for vision-based cyclist detection
IEEE
Significant progress has been achieved over the past decade on vision-based pedestrian detection; this has led to active pedestrian safety systems being deployed in most mid- to high-range cars on the market. Comparatively little effort has been spent on vision-based cyclist detection, especially when it concerns quantitative performance analysis on large datasets. We present a large-scale experimental study on cyclist detection where we examine the currently most promising object detection…
Significant progress has been achieved over the past decade on vision-based pedestrian detection; this has led to active pedestrian safety systems being deployed in most mid- to high-range cars on the market. Comparatively little effort has been spent on vision-based cyclist detection, especially when it concerns quantitative performance analysis on large datasets. We present a large-scale experimental study on cyclist detection where we examine the currently most promising object detection methods; we consider Aggregated Channel Features, Deformable Part Models and Region-based Convolutional Neural Networks. We also introduce a new method called Stereo-Proposal based Fast R-CNN (SP-FRCN) to detect cyclists based on stereo proposals and Fast R-CNN (FRCN) framework. Experiments are performed on a dataset containing 22161 annotated cyclist instances in over 30000 images, recorded from …
-
A unified framework for concurrent pedestrian and cyclist detection
IEEE
Extensive research interest has been focused on protecting vulnerable road users in recent years, particularly pedestrians and cyclists, due to their attributes of vulnerability. However, comparatively little effort has been spent on detecting pedestrian and cyclist together, particularly when it concerns quantitative performance analysis on large datasets. In this paper, we present a unified framework for concurrent pedestrian and cyclist detection, which includes a novel detection proposal…
Extensive research interest has been focused on protecting vulnerable road users in recent years, particularly pedestrians and cyclists, due to their attributes of vulnerability. However, comparatively little effort has been spent on detecting pedestrian and cyclist together, particularly when it concerns quantitative performance analysis on large datasets. In this paper, we present a unified framework for concurrent pedestrian and cyclist detection, which includes a novel detection proposal method (termed UB-MPR) to output a set of object candidates, a discriminative deep model based on Fast R-CNN for classification and localization, and a specific postprocessing step to further improve detection performance. Experiments are performed on a new pedestrian and cyclist dataset containing 30 490 annotated pedestrian and 26 771 cyclist instances in over 50 000 images, recorded from a moving vehicle in the urban traffic …
-
A Unified Framework for Concurrent Pedestrian and Cyclist Detection
IEEE
Extensive research interest has been focused on protecting vulnerable road users in recent years, particularly pedestrians and cyclists, due to their attributes of vulnerability. However, comparatively little effort has been spent on detecting pedestrian and cyclist together, particularly when it concerns quantitative performance analysis on large datasets. In this paper, we present a unified framework for concurrent pedestrian and cyclist detection, which includes a novel detection proposal…
Extensive research interest has been focused on protecting vulnerable road users in recent years, particularly pedestrians and cyclists, due to their attributes of vulnerability. However, comparatively little effort has been spent on detecting pedestrian and cyclist together, particularly when it concerns quantitative performance analysis on large datasets. In this paper, we present a unified framework for concurrent pedestrian and cyclist detection, which includes a novel detection proposal method (termed UB-MPR) to output a set of object candidates, a discriminative deep model based on Fast R-CNN for classification and localization, and a specific postprocessing step to further improve detection performance. Experiments are performed on a new pedestrian and cyclist dataset containing 30 490 annotated pedestrian and 26 771 cyclist instances in over 50 000 images, recorded from a moving vehicle in the urban traffic of Beijing. Experimental results indicate that the proposed method outperforms other state-of-the-art methods significantly.
-
Driver and pedestrian awareness-based collision risk analysis
IEEE
We present a novel approach for vehicle-pedestrian collision risk analysis that incorporates mutual situational awareness, a degree of potential motion coupling and the spatial layout of the environment. The approach uses a Dynamic Bayesian Network (DBN) for modeling the individual object paths; collision risk is subsequently computed by an intersection operation. More specifically, the proposed DBN consists of two subgraphs for modeling pedestrian and vehicle path, respectively. They consist…
We present a novel approach for vehicle-pedestrian collision risk analysis that incorporates mutual situational awareness, a degree of potential motion coupling and the spatial layout of the environment. The approach uses a Dynamic Bayesian Network (DBN) for modeling the individual object paths; collision risk is subsequently computed by an intersection operation. More specifically, the proposed DBN consists of two subgraphs for modeling pedestrian and vehicle path, respectively. They consist of latent states on top of Switching Linear Dynamical Systems (SLDSs) to anticipate changes in object dynamics. The pedestrian and vehicle-related sub-graphs contain latent states to model whether the pedestrian has seen the oncoming vehicle, and conversely, whether the driver has seen the pedestrian (associated measurements involve the respective head orientations). The pedestrian-related sub-graph furthermore contains a latent state modeling whether the pedestrian is at the curbside or not. Finally, a latent state is shared by the two sub-graphs, which models the potential motion coupling (i.e. at full awareness of the other traffic participant). We consider the scenario of a crossing pedestrian, who might stop or continue walking at the curb, in combination with an approaching vehicle, that might stop or continue driving. In experiments we illustrate that with the proposed approach, a more anticipatory driver warning and/or vehicle control strategy can be implemented.
-
Pose-RCNN: Joint object detection and pose estimation using 3D object proposals
IEEE
This paper presents a novel approach for joint object detection and orientation estimation in a single deep convolutional neural network utilizing proposals calculated from 3D data. For orientation estimation, we extend a R-CNN like architecture by several carefully designed layers. Two new object proposal methods are introduced, to make use of stereo as well as lidar data. Our experiments on the KITTI dataset show that by combining proposals of both domains, high recall can be achieved while…
This paper presents a novel approach for joint object detection and orientation estimation in a single deep convolutional neural network utilizing proposals calculated from 3D data. For orientation estimation, we extend a R-CNN like architecture by several carefully designed layers. Two new object proposal methods are introduced, to make use of stereo as well as lidar data. Our experiments on the KITTI dataset show that by combining proposals of both domains, high recall can be achieved while keeping the number of proposals low. Furthermore, our method for joint detection and orientation estimation outperforms state of the art approaches for cyclists on the easy test scenario of the KITTI test dataset.
-
A probabilistic framework for joint pedestrian head and body orientation estimation
IEEE
We present a probabilistic framework for the joint estimation of pedestrian head and body orientation from a mobile stereo vision platform. For both head and body parts, we convert the responses of a set of orientation-specific detectors into a (continuous) probability density function. The parts are localized by means of a pictorial structure approach, which balances part-based detector responses with spatial constraints. Head and body orientations are estimated jointly to account for…
We present a probabilistic framework for the joint estimation of pedestrian head and body orientation from a mobile stereo vision platform. For both head and body parts, we convert the responses of a set of orientation-specific detectors into a (continuous) probability density function. The parts are localized by means of a pictorial structure approach, which balances part-based detector responses with spatial constraints. Head and body orientations are estimated jointly to account for anatomical constraints. The joint single-frame orientation estimates are integrated over time by particle filtering. The experiments involved data from a vehicle-mounted stereo vision camera in a realistic traffic setting; 65 pedestrian tracks were supplied by a state-of-the-art pedestrian tracker. We show that the proposed joint probabilistic orientation estimation framework reduces the mean absolute head and body orientation error up to 15° …
-
Context-based pedestrian path prediction
Springer
We present a novel Dynamic Bayesian Network for pedestrian path prediction in the intelligent vehicle domain. The model incorporates the pedestrian situational awareness, situation criticality and spatial layout of the environment as latent states on top of a Switching Linear Dynamical System (SLDS) to anticipate changes in the pedestrian dynamics. Using computer vision, situational awareness is assessed by the pedestrian head orientation, situation criticality by the distance between vehicle…
We present a novel Dynamic Bayesian Network for pedestrian path prediction in the intelligent vehicle domain. The model incorporates the pedestrian situational awareness, situation criticality and spatial layout of the environment as latent states on top of a Switching Linear Dynamical System (SLDS) to anticipate changes in the pedestrian dynamics. Using computer vision, situational awareness is assessed by the pedestrian head orientation, situation criticality by the distance between vehicle and pedestrian at the expected point of closest approach, and spatial layout by the distance of the pedestrian to the curbside. Our particular scenario is that of a crossing pedestrian, who might stop or continue walking at the curb. In experiments using stereo vision data obtained from a vehicle, we demonstrate that the proposed approach results in more accurate path prediction than only SLDS, at the relevant short time horizon (1 s), and slightly outperforms a computationally more demanding state-of-the-art method.
-
Joint probabilistic pedestrian head and body orientation estimation
IEEE
We present an approach for the joint probabilistic estimation of pedestrian head and body orientation in the context of intelligent vehicles. For both, head and body, we convert the output of a set of orientation-specific detectors into a full (continuous) probability density function. The parts are localized with a pictorial structure approach which balances part-based detector output with spatial constraints. Head and body orientation estimates are furthermore coupled probabilistically to…
We present an approach for the joint probabilistic estimation of pedestrian head and body orientation in the context of intelligent vehicles. For both, head and body, we convert the output of a set of orientation-specific detectors into a full (continuous) probability density function. The parts are localized with a pictorial structure approach which balances part-based detector output with spatial constraints. Head and body orientation estimates are furthermore coupled probabilistically to account for anatomical constraints. Finally, the coupled single-frame orientation estimates are integrated over time by particle filtering. The experiments involve 37 pedestrian tracks obtained from an external stereo vision-based pedestrian detector in realistic traffic settings. We show that the proposed joint probabilistic orientation estimation approach reduces the mean head and body orientation error by 10 degrees and more.
-
PedCut: an iterative framework for pedestrian segmentation combining shape models and multiple data cues
BMVA Press
This paper presents an iterative, EM-like framework for accurate pedestrian segmentation, combining generative shape models and multiple data cues. In the E-step, shape priors are introduced in the unary terms of a Conditional Random Field (CRF) formulation, joining other data terms derived from color, texture and disparity cues. In the M-step, the resulting segmentation is used to adapt an Active Shape Model (ASM), after which the EM process alternates.
Experiments on the public Penn-Fudan…This paper presents an iterative, EM-like framework for accurate pedestrian segmentation, combining generative shape models and multiple data cues. In the E-step, shape priors are introduced in the unary terms of a Conditional Random Field (CRF) formulation, joining other data terms derived from color, texture and disparity cues. In the M-step, the resulting segmentation is used to adapt an Active Shape Model (ASM), after which the EM process alternates.
Experiments on the public Penn-Fudan pedestrian dataset suggest that our method outperforms the state-of-the-art. We further provide results on a new Daimler pedestrian dataset, captured from on-board a vehicle, which includes disparity data. This dataset is made public to facilitate benchmarking. -
Evaluation of tracking methods for maritime surveillance
International Society for Optics and Photonics
In this article, we present an evaluation of several multi-target tracking methods based on simulated scenarios in the maritime domain. In particular, we consider variations of the Joint Integrated Probabilistic Data Association (JIPDA) algorithm, namely the Linear Multi-Target IPDA (LMIPDA), Linear Joint IPDA (LJIPDA), and Markov Chain Monte Carlo Data Association (MCMCDA). The algorithms are compared with respect to an extension of the Optimal Subpattern Assignment (OSPA) metric, the…
In this article, we present an evaluation of several multi-target tracking methods based on simulated scenarios in the maritime domain. In particular, we consider variations of the Joint Integrated Probabilistic Data Association (JIPDA) algorithm, namely the Linear Multi-Target IPDA (LMIPDA), Linear Joint IPDA (LJIPDA), and Markov Chain Monte Carlo Data Association (MCMCDA). The algorithms are compared with respect to an extension of the Optimal Subpattern Assignment (OSPA) metric, the Hellinger distance and further performance measures. As no single algorithm is equally well fitted to all tested scenarios, our results show which algorithms fits best for specific scenarios.
Projekte
-
Project Athena (Urban Automated Driving)
–Heute
Bosch and Daimler are joining forces to advance the development of fully automated and driverless driving. The two companies have entered into a development agreement to bring fully automated (SAE Level 4) and driverless (SAE Level 5) driving to urban roads by the beginning of the next decade. The objective is to develop software and algorithms for an autonomous driving system. The project combines the total vehicle expertise of the world's leading premium manufacturer with the system and…
Bosch and Daimler are joining forces to advance the development of fully automated and driverless driving. The two companies have entered into a development agreement to bring fully automated (SAE Level 4) and driverless (SAE Level 5) driving to urban roads by the beginning of the next decade. The objective is to develop software and algorithms for an autonomous driving system. The project combines the total vehicle expertise of the world's leading premium manufacturer with the system and hardware expertise of the world's biggest supplier. The ensuing synergies should ensure the earliest possible series introduction of the secure technology.
-
PROSPECT - Proactive Safety for Pedestrian and Cyclists
–
The overall PROSPECT objective is to provide a better understanding of VRU-related accidents and to develop, demonstrate and test innovative, (pro) active safety systems for protecting VRUs.
I am work package leader and working closely together with a big consortium of well known OEMs, suppliers and universities.
-
Mercedes-Benz Future Bus
–
Project Manager, Vulnerable Road User Protection using a stereo camera system.
The autonomes bus reacts on any kind of vulnerable road user. -
UR:BAN, Schutz von schwächeren Verkehrsteilnehmern (SVT)
–
The public project UR:BAN has been supported by the Federal Ministry for Economic Affairs and Energy.
Main focus of my work was the protection of Vulnerable Road User in urban areas.
I worked as a research and development engineer together with other universities, OEMs and suppliers.
Auszeichnungen/Preise
-
Best absolvent Media and Communication Informatics, SS 2008
Reutlingen University
Sprachen
-
Deutsch
Muttersprache oder zweisprachig
-
Englisch
Verhandlungssicher
-
Spanisch
Grundkenntnisse
-
Französisch
Grundkenntnisse
Weitere Aktivitäten von Fabian Flohr
-
Unsere Forschungsgruppe des automatisiertes Fahrens steht bei der langen Nacht der Wissenschaft der Universität Ulm für euch zur Verfügung. Bei uns…
Unsere Forschungsgruppe des automatisiertes Fahrens steht bei der langen Nacht der Wissenschaft der Universität Ulm für euch zur Verfügung. Bei uns…
Beliebt bei Fabian Flohr
-
🌟 We are glad to announce that the Intelligent Vehicles Lab were at #IV2024 presenting our research in the category - Simulation and Real-World…
🌟 We are glad to announce that the Intelligent Vehicles Lab were at #IV2024 presenting our research in the category - Simulation and Real-World…
Beliebt bei Fabian Flohr
-
This year our team is represented by Yancong LIN at CVPR in Seattle. Some highlights from our team's work: - (Challenge) Winner of the…
This year our team is represented by Yancong LIN at CVPR in Seattle. Some highlights from our team's work: - (Challenge) Winner of the…
Beliebt bei Fabian Flohr
-
This Tuesday, Philipp Renner and I are going to present this paper on ambiguous annotations at the #CVPR2024 VLADR workshop! It was authored by…
This Tuesday, Philipp Renner and I are going to present this paper on ambiguous annotations at the #CVPR2024 VLADR workshop! It was authored by…
Beliebt bei Fabian Flohr
-
I recently attended IEEE Intelligent Vehicles Symposium #IV2024 on Jeju Island 🏝 in South Korea and presented our poster called - "Walk-the-Talk:…
I recently attended IEEE Intelligent Vehicles Symposium #IV2024 on Jeju Island 🏝 in South Korea and presented our poster called - "Walk-the-Talk:…
Beliebt bei Fabian Flohr
-
I am excited to be at #CVPR in Seattle next week. Let me know if you want to connect. Edit: Open to any party invites 😉 👉 I am joining Simon Doll…
I am excited to be at #CVPR in Seattle next week. Let me know if you want to connect. Edit: Open to any party invites 😉 👉 I am joining Simon Doll…
Beliebt bei Fabian Flohr
-
We are looking for a Principal Machine Learning Researcher to join our fantastic Qualcomm AI Research team in Amsterdam, NL! You will conduct…
We are looking for a Principal Machine Learning Researcher to join our fantastic Qualcomm AI Research team in Amsterdam, NL! You will conduct…
Beliebt bei Fabian Flohr
-
The nxtAIM project received 1.35 million core-hours in the Jülich Supercomputing Centre at Forschungszentrum Jülich. nxtAIM got an awesome reviewer’s…
The nxtAIM project received 1.35 million core-hours in the Jülich Supercomputing Centre at Forschungszentrum Jülich. nxtAIM got an awesome reviewer’s…
Beliebt bei Fabian Flohr
-
Let us meet in Karlsruhe! We will talk TACHELES about Generative Ai for Autonomous Driving! Get your registration now before it is sold out!
Let us meet in Karlsruhe! We will talk TACHELES about Generative Ai for Autonomous Driving! Get your registration now before it is sold out!
Beliebt bei Fabian Flohr
-
Meet Christoph Schroeder, 👋 one of the Co-Founders and CEO. Christoph is a software leader with decade long leadership experience in high tech…
Meet Christoph Schroeder, 👋 one of the Co-Founders and CEO. Christoph is a software leader with decade long leadership experience in high tech…
Beliebt bei Fabian Flohr
-
The UK has been an incredibly supportive environment to pioneer our Embodied AI. Today, we were honoured to welcome the PM Rishi Sunak and SoS…
The UK has been an incredibly supportive environment to pioneer our Embodied AI. Today, we were honoured to welcome the PM Rishi Sunak and SoS…
Beliebt bei Fabian Flohr
-
ein mehr als beeindruckendes Video - ich empfehle jeden sich einfach mal die 2 Minuten zunehmen und das einfach sacken zu lassen.
ein mehr als beeindruckendes Video - ich empfehle jeden sich einfach mal die 2 Minuten zunehmen und das einfach sacken zu lassen.
Beliebt bei Fabian Flohr
-
It's a wrap! Last week, I successfully defended my PhD thesis titled "Behavior Prediction for Autonomous Driving Using Graph Neural Networks" at…
It's a wrap! Last week, I successfully defended my PhD thesis titled "Behavior Prediction for Autonomous Driving Using Graph Neural Networks" at…
Beliebt bei Fabian Flohr
Weitere ähnliche Profile
Weitere Mitglieder namens Fabian Flohr in Deutschland
-
Fabian Flohr
-
Fabian Flohr
Head of Digital Sales Channels bei uvex group
-
Fabian Flohr
Rechtsanwalt bei meyerhuber rechtsanwälte partnerschaft
-
Fabian Flohr
Professor bei Hochschule München
Es gibt auf LinkedIn 7 weitere Personen namens Fabian Flohr, die sich in Deutschland befinden.
Weitere Mitglieder anzeigen, die Fabian Flohr heißen