Search Results for author: Alois Knoll

Found 70 papers, 24 papers with code

TMA: Temporal Motion Aggregation for Event-based Optical Flow

no code implementations21 Mar 2023 Haotian Liu, Guang Chen, Sanqing Qu, Yanping Zhang, Zhijun Li, Alois Knoll, Changjun Jiang

Event cameras have the ability to record continuous and detailed trajectories of objects with high temporal resolution, thereby providing intuitive motion cues for optical flow estimation.

Event-based Optical Flow Optical Flow Estimation

Fast and Accurate Object Detection on Asymmetrical Receptive Field

no code implementations15 Mar 2023 Liguo Zhou, Tianhao Lin, Alois Knoll

To address the above challenges, based on extensive literature research, this paper analyzes methods for improving and optimizing mainstream object detection algorithms from the perspective of evolution of one-stage and two-stage object detection algorithms.

Autonomous Driving object-detection +1

Sequential Spatial Network for Collision Avoidance in Autonomous Driving

no code implementations12 Mar 2023 Haichuan Li, Liguo Zhou, Zhenshan Bing, Marzana Khatun, Rolf Jung, Alois Knoll

Several autonomous driving strategies have been applied to autonomous vehicles, especially in the collision avoidance area.

Autonomous Driving

BCSSN: Bi-direction Compact Spatial Separable Network for Collision Avoidance in Autonomous Driving

no code implementations12 Mar 2023 Haichuan Li, Liguo Zhou, Alois Knoll

In this paper, we propose a CNN-based method that overcomes the limitation by establishing feature correlations between regions in sequential images using variants of attention.

Autonomous Driving Decision Making

Autonomous Driving Simulator based on Neurorobotics Platform

no code implementations31 Dec 2022 Wei Cao, Liguo Zhou, Yuhong Huang, Alois Knoll

There are many artificial intelligence algorithms for autonomous driving, but directly installing these algorithms on vehicles is unrealistic and expensive.

Autonomous Driving object-detection +1

ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals

no code implementations11 Dec 2022 Rui Song, Liguo Zhou, Lingjuan Lyu, Andreas Festag, Alois Knoll

To address this bottleneck, we introduce a residual-based federated learning framework (ResFed), where residuals rather than model parameters are transmitted in communication networks for training.

Federated Learning Quantization

Learning from Symmetry: Meta-Reinforcement Learning with Symmetric Data and Language Instructions

no code implementations21 Sep 2022 Xiangtong Yao, Zhenshan Bing, Genghang Zhuang, KeJia Chen, Hongkuan Zhou, Kai Huang, Alois Knoll

We thus propose a dual-MDP meta-reinforcement learning method that enables learning new tasks efficiently with symmetric data and language instructions.

Meta Reinforcement Learning reinforcement-learning +1

Hardware faults that matter: Understanding and Estimating the safety impact of hardware faults on object detection DNNs

1 code implementation7 Sep 2022 Syed Qutub, Florian Geissler, Yang Peng, Ralf Grafe, Michael Paulitsch, Gereon Hinz, Alois Knoll

The evaluation of several representative object detection models shows that even a single bit flip can lead to a severe silent data corruption event with potentially critical safety implications, with e. g., up to (much greater than) 100 FPs generated, or up to approx.

object-detection Object Detection

Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments

1 code implementation24 Aug 2022 Rui Song, Dai Liu, Dave Zhenyu Chen, Andreas Festag, Carsten Trinitis, Martin Schulz, Alois Knoll

We introduce a novel federated learning framework, FedD3, which reduces the overall communication volume and with that opens up the concept of federated learning to more application scenarios in network-constrained environments.

Federated Learning

Accurate and Real-time Pseudo Lidar Detection: Is Stereo Neural Network Really Necessary?

no code implementations28 Jun 2022 Haitao Meng, Changcai Li, Gang Chen, Alois Knoll

In the experiments, we develop a system with a less powerful stereo matching predictor and adopt the proposed refinement schemes to improve the accuracy.

3D Object Detection object-detection +2

Edge-Aided Sensor Data Sharing in Vehicular Communication Networks

no code implementations17 Jun 2022 Rui Song, Anupama Hegde, Numan Senel, Alois Knoll, Andreas Festag

Specifically, when the measurement error from the sensors (also referred as measurement noise) is unknown and time varying, the performance of the data fusion process is restricted, which represents a major challenge in the calibration of sensors.

Noise Estimation

A Review of Safe Reinforcement Learning: Methods, Theory and Applications

1 code implementation20 May 2022 Shangding Gu, Long Yang, Yali Du, Guang Chen, Florian Walter, Jun Wang, Yaodong Yang, Alois Knoll

To establish a good foundation for future research in this thread, in this paper, we provide a review for safe RL from the perspectives of methods, theory and applications.

Autonomous Driving Decision Making +3

3D Object Detection with a Self-supervised Lidar Scene Flow Backbone

1 code implementation2 May 2022 Ekim Yurtsever, Emeç Erçelik, MingYu Liu, Zhijie Yang, Hanzhen Zhang, Pınar Topçam, Maximilian Listl, Yılmaz Kaan Çaylı, Alois Knoll

Our main contribution leverages learned flow and motion representations and combines a self-supervised backbone with a supervised 3D detection head.

3D Object Detection object-detection +2

A9-Dataset: Multi-Sensor Infrastructure-Based Dataset for Mobility Research

no code implementations13 Apr 2022 Christian Creß, Walter Zimmer, Leah Strand, Venkatnarayanan Lakshminarasimhan, Maximilian Fortkord, Siyi Dai, Alois Knoll

As part of the first set of data, which we describe in this paper, we provide camera and LiDAR frames from two overhead gantry bridges on the A9 autobahn with the corresponding objects labeled with 3D bounding boxes.


Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITS

1 code implementation1 Apr 2022 Rui Song, Liguo Zhou, Venkatnarayanan Lakshminarasimhan, Andreas Festag, Alois Knoll

Considering the individual heterogeneity of data distribution, computational and communication capabilities across traffic agents and roadside units, we employ a novel method that addresses the heterogeneity of different aggregation layers of the framework architecture, i. e., aggregation in layers of roadside units and cloud.

Autonomous Vehicles Federated Learning

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

no code implementations31 Mar 2022 Walter Zimmer, Emec Ercelik, Xingcheng Zhou, Xavier Jair Diaz Ortiz, Alois Knoll

The purpose of this work is to review the state-of-the-art LiDAR-based 3D object detection methods, datasets, and challenges.

3D Object Detection Autonomous Driving +2

Graph Neural Networks for Relational Inductive Bias in Vision-based Deep Reinforcement Learning of Robot Control

no code implementations11 Mar 2022 Marco Oliva, Soubarna Banik, Josip Josifovski, Alois Knoll

We derive a graph representation that models the physical structure of the manipulator and combines the robot's internal state with a low-dimensional description of the visual scene generated by an image encoding network.

Inductive Bias reinforcement-learning +1

Are Attention Networks More Robust? Towards Exact Robustness Verification for Attention Networks

no code implementations8 Feb 2022 Hsuan-Cheng Liao, Chih-Hong Cheng, Hasan Esen, Alois Knoll

Attention Networks (ATNs) such as Transformers are used in many domains ranging from Natural Language Processing to Autonomous Driving.

Autonomous Driving Object Recognition

Evaluating Muscle Synergies with EMG Data and Physics Simulation in the Neurorobotics Platform

no code implementations14 Jan 2022 Benedikt Feldotto, Cristian Soare, Alois Knoll, Piyanee Sriya, Sarah Astill, Marc de Kamps, Samit Chakrabarty

We use our framework to analyze raw EMG data collected during an isometric knee extension study to identify synergies that drive a musculoskeletal lower limb model.

Electromyography (EMG)

Multi-Agent Constrained Policy Optimisation

3 code implementations6 Oct 2021 Shangding Gu, Jakub Grudzien Kuba, Munning Wen, Ruiqing Chen, Ziyan Wang, Zheng Tian, Jun Wang, Alois Knoll, Yaodong Yang

To fill these gaps, in this work, we formulate the safe MARL problem as a constrained Markov game and solve it with policy optimisation methods.

Multi-agent Reinforcement Learning reinforcement-learning +1

Towards Cognitive Navigation: Design and Implementation of a Biologically Inspired Head Direction Cell Network

no code implementations22 Sep 2021 Zhenshan Bing, Amir EI Sewisy, Genghang Zhuang, Florian Walter, Fabrice O. Morin, Kai Huang, Alois Knoll

As a vital cognitive function of animals, the navigation skill is first built on the accurate perception of the directional heading in the environment.

Vogtareuth Rehab Depth Datasets: Benchmark for Marker-less Posture Estimation in Rehabilitation

no code implementations23 Aug 2021 Soubarna Banik, Alejandro Mendoza Garcia, Lorenz Kiwull, Steffen Berweck, Alois Knoll

We evaluate it on our rehab datasets, and observe that the performance degrades significantly from non-rehab to rehab, highlighting the need for these datasets.

Meta-Reinforcement Learning in Broad and Non-Parametric Environments

1 code implementation8 Aug 2021 Zhenshan Bing, Lukas Knak, Fabrice Oliver Robin, Kai Huang, Alois Knoll

Recent state-of-the-art artificial agents lack the ability to adapt rapidly to new tasks, as they are trained exclusively for specific objectives and require massive amounts of interaction to learn new skills.

Meta Reinforcement Learning reinforcement-learning +1

3D Human Pose Regression using Graph Convolutional Network

no code implementations21 May 2021 Soubarna Banik, Alejandro Mendoza Gracia, Alois Knoll

We propose one such graph convolutional network named PoseGraphNet for 3D human pose regression from 2D poses.

3D Pose Estimation Pose Prediction +1

Safety Metrics for Semantic Segmentation in Autonomous Driving

no code implementations21 May 2021 Chih-Hong Cheng, Alois Knoll, Hsuan-Cheng Liao

Within the context of autonomous driving, safety-related metrics for deep neural networks have been widely studied for image classification and object detection.

Autonomous Driving Image Classification +3

Temp-Frustum Net: 3D Object Detection with Temporal Fusion

1 code implementation25 Apr 2021 Emeç Erçelik, Ekim Yurtsever, Alois Knoll

Furthermore, ablation studies reinforce that the subject of improvement is temporal fusion and show the effects of different placements of TFM in the object detection pipeline.

3D Object Detection object-detection +1

A Hierarchical State-Machine-Based Framework for Platoon Manoeuvre Descriptions

no code implementations12 Apr 2021 Corvin Deboeser, Jordan Ivanchev, Thomas Braud, Alois Knoll, David Eckhoff, Alberto Sangiovanni-Vincentelli

This paper introduces the SEAD framework that simplifies the process of designing and describing autonomous vehicle platooning manoeuvres.

Non-Holonomic RRT & MPC: Path and Trajectory Planning for an Autonomous Cycle Rickshaw

no code implementations10 Mar 2021 Damir Bojadžić, Julian Kunze, Dinko Osmanković, Mohammadhossein Malmir, Alois Knoll

Therefore, the algorithm presented in this paper needs to anticipate and avoid dynamic obstacles, such as pedestrians or bicycles, but also be fast enough in order to work in real-time so that it can adapt to changes in the environment.

Motion Planning Trajectory Planning Robotics

FloMo: Tractable Motion Prediction with Normalizing Flows

no code implementations5 Mar 2021 Christoph Schöller, Alois Knoll

In our work, we model motion prediction directly as a density estimation problem with a normalizing flow between a noise distribution and the future motion distribution.

Data Augmentation Density Estimation +1

NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series Forecasting

1 code implementation10 Feb 2021 Kai Chen, Guang Chen, Dan Xu, Lijun Zhang, Yuyao Huang, Alois Knoll

Although Transformer has made breakthrough success in widespread domains especially in Natural Language Processing (NLP), applying it to time series forecasting is still a great challenge.

Time Series Forecasting

Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning

no code implementations5 Feb 2021 Julian Bernhard, Robert Gieselmann, Klemens Esterle, Alois Knoll

With Deep Reinforcement Learning, optimal driving strategies for such problems can be derived also for higher-dimensional problems.

Autonomous Driving Motion Planning +3

Lightweight Convolutional Neural Network with Gaussian-based Grasping Representation for Robotic Grasping Detection

no code implementations25 Jan 2021 Hu Cao, Guang Chen, Zhijun Li, Jianjie Lin, Alois Knoll

Extensive experiments on two public grasping datasets, Cornell and Jacquard demonstrate the state-of-the-art performance of our method in balancing accuracy and inference speed.

object-detection Robotic Grasping

PointINet: Point Cloud Frame Interpolation Network

1 code implementation18 Dec 2020 Fan Lu, Guang Chen, Sanqing Qu, Zhijun Li, Yinlong Liu, Alois Knoll

Generally, the frame rates of mechanical LiDAR sensors are 10 to 20 Hz, which is much lower than other commonly used sensors like cameras.

3D Point Cloud Interpolation

LAP-Net: Adaptive Features Sampling via Learning Action Progression for Online Action Detection

no code implementations16 Nov 2020 Sanqing Qu, Guang Chen, Dan Xu, Jinhu Dong, Fan Lu, Alois Knoll

At each time step, this sampling strategy first estimates current action progression and then decide what temporal ranges should be used to aggregate the optimal supplementary features.

Online Action Detection

RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor

1 code implementation NeurIPS 2020 Fan Lu, Guang Chen, Yinlong Liu, Zhongnan Qu, Alois Knoll

To tackle the information loss of random sampling, we exploit a novel random dilation cluster strategy to enlarge the receptive field of each sampled point and an attention mechanism to aggregate the positions and features of neighbor points.

Point Cloud Registration Saliency Prediction

Neuron Activation Analysis for Multi-Joint Robot Reinforcement Learning

no code implementations28 Sep 2020 Benedikt Feldotto, Heiko Lengenfelder, Alois Knoll

We analyze the individual neuron activity distribution in the network, introduce a pruning algorithm to reduce network size keeping the performance, and with these dense network representations we spot correlations of neuron activity patterns among networks trained for robot manipulators with different joint number.

reinforcement-learning Reinforcement Learning (RL)

Complex Robotic Manipulation via Graph-Based Hindsight Goal Generation

1 code implementation27 Jul 2020 Zhenshan Bing, Matthias Brucker, Fabrice O. Morin, Kai Huang, Alois Knoll

In this paper, we propose graph-based hindsight goal generation (G-HGG), an extension of HGG selecting hindsight goals based on shortest distances in an obstacle-avoiding graph, which is a discrete representation of the environment.

Formalizing Traffic Rules for Machine Interpretability

2 code implementations1 Jul 2020 Klemens Esterle, Luis Gressenbuch, Alois Knoll

We contribute a formalized set of traffic rules for single-direction carriageways, such as on highways.


AerialMPTNet: Multi-Pedestrian Tracking in Aerial Imagery Using Temporal and Graphical Features

no code implementations27 Jun 2020 Maximilian Kraus, Seyed Majid Azimi, Emec Ercelik, Reza Bahmanyar, Peter Reinartz, Alois Knoll

Due to the challenges such as the large number and the tiny size of the pedestrians (e. g., 4 x 4 pixels) with their similar appearances as well as different scales and atmospheric conditions of the images with their extremely low frame rates (e. g., 2 fps), current state-of-the-art algorithms including the deep learning-based ones are unable to perform well.


Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environments

2 code implementations22 Jun 2020 Patrick Hart, Alois Knoll

We show that graph neural networks are capable of handling scenarios with a varying number and order of vehicles during training and application.

reinforcement-learning Reinforcement Learning (RL)

Counterfactual Policy Evaluation for Decision-Making in Autonomous Driving

2 code implementations20 Mar 2020 Patrick Hart, Alois Knoll

If a policy can handle all counterfactual worlds well, it either has seen similar situations during training or it generalizes well and is deemed to be fit enough to be executed in the actual world.

Autonomous Driving Imitation Learning

Task-Independent Spiking Central Pattern Generator: A Learning-Based Approach

no code implementations17 Mar 2020 Elie Aljalbout, Florian Walter, Florian Röhrbein, Alois Knoll

This model is the main focus of this work, as its contribution is not limited to engineering but also applicable to neuroscience.

Indirect and Direct Training of Spiking Neural Networks for End-to-End Control of a Lane-Keeping Vehicle

no code implementations10 Mar 2020 Zhenshan Bing, Claus Meschede, Guang Chen, Alois Knoll, Kai Huang

Building spiking neural networks (SNNs) based on biological synaptic plasticities holds a promising potential for accomplishing fast and energy-efficient computing, which is beneficial to mobile robotic applications.


Identity Recognition in Intelligent Cars with Behavioral Data and LSTM-ResNet Classifier

no code implementations2 Mar 2020 Michael Hammann, Maximilian Kraus, Sina Shafaei, Alois Knoll

Identity recognition in a car cabin is a critical task nowadays and offers a great field of applications ranging from personalizing intelligent cars to suit drivers physical and behavioral needs to increasing safety and security.

General Classification Time Series Analysis +1

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

no code implementations16 Jun 2019 Annkathrin Krämmer, Christoph Schöller, Dhiraj Gulati, Venkatnarayanan Lakshminarasimhan, Franz Kurz, Dominik Rosenbaum, Claus Lenz, Alois Knoll

An Intelligent Infrastructure System can fill in the gaps in a vehicle's perception and extend its field of view by providing additional detailed information about its surroundings, in the form of a digital model of the current traffic situation, i. e. a digital twin.

Autonomous Vehicles

Copy and Paste: A Simple But Effective Initialization Method for Black-Box Adversarial Attacks

1 code implementation14 Jun 2019 Thomas Brunner, Frederik Diehl, Alois Knoll

Many optimization methods for generating black-box adversarial examples have been proposed, but the aspect of initializing said optimizers has not been considered in much detail.

Leveraging Semantic Embeddings for Safety-Critical Applications

no code implementations19 May 2019 Thomas Brunner, Frederik Diehl, Michael Truong Le, Alois Knoll

Semantic Embeddings are a popular way to represent knowledge in the field of zero-shot learning.

Zero-Shot Learning

Globally optimal vertical direction estimation in Atlanta World

1 code implementation29 Apr 2019 Yinlong Liu, Alois Knoll, Guang Chen

Accordingly, we propose a vertical direction estimation method by considering the relationship between the vertical frame and horizontal frames.

A Novel Method for the Absolute Pose Problem with Pairwise Constraints

no code implementations25 Mar 2019 Yinlong Liu, Xuechen Li, Manning Wang, Guang Chen, Zhijian Song, Alois Knoll

In this paper, we consider pairwise constraints and propose a globally optimal algorithm for solving the absolute pose estimation problem.

Pose Estimation Translation

What the Constant Velocity Model Can Teach Us About Pedestrian Motion Prediction

2 code implementations19 Mar 2019 Christoph Schöller, Vincent Aravantinos, Florian Lay, Alois Knoll

Our work shows how neural networks for pedestrian motion prediction can be thoroughly evaluated and our results indicate which research directions for neural motion prediction are promising in future.

motion prediction

Graph Neural Networks for Modelling Traffic Participant Interaction

no code implementations4 Mar 2019 Frederik Diehl, Thomas Brunner, Michael Truong Le, Alois Knoll

We show that prediction error in scenarios with much interaction decreases by 30% compared to a model that does not take interactions into account.

Traffic Prediction

Deep Anticipation: Light Weight Intelligent Mobile Sensing in IoT by Recurrent Architecture

no code implementations6 Dec 2017 Guang Chen, Shu Liu, Kejia Ren, Zhongnan Qu, Changhong Fu, Gereon Hinz, Alois Knoll

However, the mobile sensing perception brings new challenges for how to efficiently analyze and intelligently interpret the deluge of IoT data in mission- critical services.

Variational PatchMatch MultiView Reconstruction and Refinement

no code implementations ICCV 2015 Philipp Heise, Brian Jensen, Sebastian Klose, Alois Knoll

We formulate the combined multi-view stereo reconstruction and refinement as a variational optimization problem.

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