Alois Knoll's research while affiliated with Technische Universität München and other places
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Publications (879)
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. Nevertheless, most existing learning-based approaches for event optical flow estimation directly remould the paradigm of conventional images by representing the...
Applying simulation-based optimization to city-scale traffic signal optimization can be challenging due to the large search space resulting in high computational complexity. A divide-and-conquer approach can be used to partition the problem and optimized separately, which leads to faster convergence. However, the lack of coordination among the part...
Object detection has been used in a wide range of industries. For example, in autonomous driving, the task of object detection is to accurately and efficiently identify and locate a large number of predefined classes of object instances (vehicles, pedestrians, traffic signs, etc.) from videos of roads. In robotics, the industry robot needs to recog...
Autonomous driving has been an active area of research and development, with various strategies being explored for decision-making in autonomous vehicles. Rule-based systems, decision trees, Markov decision processes, and Bayesian networks have been some of the popular methods used to tackle the complexities of traffic conditions and avoid collisio...
Several autonomous driving strategies have been applied to autonomous vehicles, especially in the collision avoidance area. The purpose of collision avoidance is achieved by adjusting the trajectory of autonomous vehicles (AV) to avoid intersection or overlap with the trajectory of surrounding vehicles. A large number of sophisticated vision algori...
State-of-the-art object detectors are commonly evaluated based on accuracy metrics such as mean Average Precision (mAP). In this paper, inspired by the fact that mAP is not a direct safety indicator, we propose a straightforward safety metric, especially for 3D object detectors in Autonomous Driving contexts, by combining the Intersection-over-Grou...
Chips pack ever more, ever smaller transistors. Fault rates increase in turn and become more concerning, particularly at the scale of
High-Performance Computing
(HPC) systems: on one hand, hardware fault protection is costly - more than 10% silicon area for floating-point units; on the other, HPC users expect correct application output after the...
Deployment of reinforcement learning algorithms for robotics applications in the real world requires ensuring the safety of the robot and its environment. Safe robot reinforcement learning (SRRL) is a crucial step towards achieving human-robot coexistence. In this paper, we envision a human-centered SRRL framework consisting of three stages: safe e...
Using real road testing to optimize autonomous driving algorithms is time-consuming and capital-intensive. To solve this problem, we propose a GAN-based model that is capable of generating high-quality images across different domains. We further leverage Contrastive Learning to train the model in a self-supervised way using image data acquired in t...
Attention Networks (ATNs) such as Transformers are used in many domains ranging from Natural Language Processing to Autonomous Driving. In this paper, we study the robustness problem of ATNs, a key characteristic where low robustness may cause safety concerns. Specifically, we focus on Sparsemax-based ATNs and reduce the finding of their maximum ro...
The intelligent transportation systems (ITS) are part of possible solutions to the problems in transportation. Current systems generate digital twins of traffic participants. The traffic can be interpreted, and control signals can be sent to vehicles. Malfunctions could have disastrous consequences. Therefore, we present a self-diagnosis functional...
Sensing sensitivity is one of the crucial parameters for quartz crystal microbalance (QCM) sensors. Herein, we study the overtone mass sensitivity of a QCM sensor with an asymmetric N-M type electrode configuration. In order to overcome the deficiency that the sensitivity of the QCM sensor with an asymmetric electrode cannot be calculated by Sauerb...
As machine learning applications are becoming increasingly more powerful and are deployed to an increasing number of different appliances, the need for energy-efficient implementations is rising. To meet this demand, a promising field of research is the adoption of spiking neural networks jointly used with neuromorphic hardware, as energy is solely...
Deep reinforcement learning (DRL) combines reinforcement learning algorithms with deep neural networks (DNNs). Spiking neural networks (SNNs) have been shown to be a biologically plausible and energy efficient alternative to DNNs. Since the introduction of surrogate gradient approaches that allowed to overcome the discontinuity in the spike functio...
Radar is among the most popular sensors in modern Intelligent Transportation Systems (ITSs), enabling weather-robust perception. The orientation and position of the traffic radar relative to the ITS coordinate system are necessary for the perception fusion in ITSs. However, due to the unknown target association, sparseness and noisiness of traffic...
There are many artificial intelligence algorithms for autonomous driving, but directly installing these algorithms on vehicles is unrealistic and expensive. At the same time, many of these algorithms need an environment to train and optimize. Simulation is a valuable and meaningful solution with training and testing functions, and it can say that s...
Federated learning enables cooperative training among massively distributed clients by sharing their learned local model parameters. However, with increasing model size, deploying federated learning requires a large communication bandwidth, which limits its deployment in wireless networks. To address this bottleneck, we introduce a residual-based f...
Spiking Neural Networks represent the third generation of biologically inspired systems for signal processing. They are associated with a particularly efficient and thus low-energy possibility of computing. However, this advantage can only be fully achieved if these networks utilize special neuromorphic circuits. In this work, an analog Spiking Neu...
To resemble the body flexibility of biological snakes, snake-like robots are designed as a chain of body modules, which gives them many degrees of freedom (DoF) on the one hand and leads to a challenging task to control them on the other. Compared with conventional model-based control methods, reinforcement learning (RL)-based ones provide promisin...
Due to the lack of training labels and the difficulty of annotating, dealing with adverse driving conditions such as nighttime has posed a huge challenge to the perception system of autonomous vehicles. Therefore, adapting knowledge from a labelled daytime domain to an unlabelled nighttime domain has been widely researched. In addition to labelled...
After several decades of continuously optimizing computing systems, the Moore's law is reaching its end. However, there is an increasing demand for fast and efficient processing systems that can handle large streams of data while decreasing system footprints. Neuromorphic computing answers this need by creating decentralized architectures that comm...
State-of-the-art lidar-based 3D object detection methods rely on supervised learning and large labeled datasets. However, annotating lidar data is resource-consuming, and depending only on supervised learning limits the applicability of trained models. Self-supervised training strategies can alleviate these issues by learning a general point cloud...
An important aspect of the perception system for intelligent vehicles is the detection and signal measurement of vehicle taillights. In this work, we present a novel vision-based measurement (VBM) system, using an event-based neuromorphic vision sensor, which is able to detect and measure the vehicle taillight signal robustly. To the best of our kn...
Meta-reinforcement learning (meta-RL) is a promising approach that enables the agent to learn new tasks quickly. However, most meta-RL algorithms show poor generalization in multiple-task scenarios due to the insufficient task information provided only by rewards. Language-conditioned meta-RL improves the generalization by matching language instruc...
Although deep reinforcement learning has recently been very successful at learning complex behaviors, it requires tremendous amount of data to learn a task, let alone being able to adapt to new tasks. One of the fundamental reasons causing this limitation lies in the nature of the trial-and-error learning paradigm of reinforcement learning, where t...
Object detection neural network models need to perform reliably in highly dynamic and safety-critical environments like automated driving or robotics. Therefore, it is paramount to verify the robustness of the detection under unexpected hardware faults like soft errors that can impact a systems perception module. Standard metrics based on average p...
In order to address the problem that uncertain environments may cause uncertain disturbances to the motion planning of unmanned surface vehicles (USV) due to the effects of wind and current, we propose the theoretical trajectory model (TM) and the algorithm of the motion planning bound, which will provide the reachable areas for USV navigation by c...
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. It achieves this by leveraging local dataset distillation instead of traditional learning approaches (i) to significantly...
Rule-based traditional motion planning methods usually perform well with prior knowledge of the macro-scale environments but encounter challenges in unknown and uncertain environments. Deep reinforcement learning (DRL) is a solution that can effectively deal with micro-scale unknown and uncertain environments. Nevertheless, DRL is unstable and lack...
Robot learning through kinesthetic teaching is a promising way of cloning human behaviors, but it has its limits in the performance of complex tasks with small amounts of data, due to compounding errors. In order to improve the robustness and adaptability of imitation learning, a hierarchical learning strategy is proposed: low-level learning compri...
Object detection neural network models need to perform reliably in highly dynamic and safety-critical environments like automated driving or robotics. Therefore, it is paramount to verify the robustness of the detection under unexpected hardware faults like soft errors that can impact a system’s perception module. Standard metrics based on average...
First-time-right printing is needed for extensive industrialization of Wire Arc Additive Manufacturing. However, due to process instabilities defects can occur even if suitable process parameter were chosen, resulting in production scrap due to an insufficient part quality. In this paper, we propose a smart manufacturing system which enables the co...
Although we can measure muscle activity and analyze their activation patterns, we understand little about how individual muscles affect the joint torque generated. It is known that they are controlled by circuits in the spinal cord, a system much less well-understood than the cortex. Knowing the contribution of the muscles toward a joint torque wou...
Motion planning for urban environments with numerous moving agents can be viewed as a combinatorial problem. With passing an obstacle before, after, right or left, there are multiple options an autonomous vehicle could choose to execute. These combinatorial aspects need to be taken into account in the planning framework. We address this problem by...
Motion planning for autonomous racing is a challenging task due to the safety requirement while driving aggressively. Most previous solutions utilize the prior information or depend on complex dynamics modeling. Classical model-free reinforcement learning methods are based on random sampling, which severely increases the training consumption and un...
In recent years, the subject of deep reinforcement learning (DRL) has developed very rapidly, and is now applied in various fields, such as decision making and control tasks. However, artificial agents trained with RL algorithms require great amounts of training data, unlike humans that are able to learn new skills from very few examples. The conce...
Sensor data sharing in vehicular networks can significantly improve the range and accuracy of environmental perception for connected automated vehicles. Different concepts and schemes for dissemination and fusion of sensor data have been developed. It is common to these schemes that measurement errors of the sensors impair the perception quality an...
Multiple object tracking (MOT) is an important aspect for autonomous robotic applications, such as autonomous driving. Current research regarding MOT is mainly based on 2D object detections. However, there is a recent shift towards 3D MOT based on 3D object detections. Nevertheless, most of the state-of-the-art 3D MOT methods still rely on a combin...
Randomization is currently a widely used approach in Sim2Real transfer for data-driven learning algorithms in robotics. Still, most Sim2Real studies report results for a specific randomization technique and often on a highly customized robotic system, making it difficult to evaluate different randomization approaches systematically. To address this...
![Figure 3: The framework of SAFER (Adapt the figure with permission [256]).](profile/Shangding-Gu/publication/360782013/figure/fig3/AS:1158735942692865@1653275585219/The-framework-of-SAFER-Adapt-the-figure-with-permission-256_Q320.jpg)
Reinforcement learning has achieved tremendous success in many complex decision making tasks. When it comes to deploying RL in the real world, safety concerns are usually raised, leading to a growing demand for safe reinforcement learning algorithms, such as in autonomous driving and robotics scenarios. While safety control has a long history, the...
State-of-the-art 3D detection methods rely on supervised learning and large labelled datasets. However, annotating lidar data is resource-consuming, and depending only on supervised learning limits the applicability of trained models. Against this backdrop, here we propose using a self-supervised training strategy to learn a general point cloud bac...
Data-intensive machine learning based techniques increasingly play a prominent role in the development of future mobility solutions - from driver assistance and automation functions in vehicles, to real-time traffic management systems realized through dedicated infrastructure. The availability of high quality real-world data is often an important p...
Robustly fitting a linear model from outlier-contaminated data is an important and basic task in many scientific fields, and it is often tackled by consensus set maximization. There have been several studies on globally optimal methods for consensus set maximization, but most of them are currently confined to problems with small number of input obs...
The more we investigate the principles of motion learning in biological systems, the more we reveal the central role that body morphology plays in motion execution. Not only does anatomy define the kinematics and therefore the complexity of possible movements, but it now becomes clear that part of the computation required for motion control is offl...
In this paper, we introduce a federated learning framework coping with Hierarchical Heterogeneity (H2-Fed), which can notably enhance the conventional pre-trained deep learning model. The framework exploits data from connected public traffic agents in vehicular networks without affecting user data privacy. By coordinating existing traffic infrastru...
