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A gamification-based system of driving training and its evaluation

Multimedia Tools and Applications 2025年第18期84卷 20145-20160页

作者： Li, Xu Huang, Yanqun Chen, Pengfei Zhang, Peng Kang, Zhiyu School of Mechanical Engineering Tiangong University Tianjin300387 China Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education Tianjin University Tianjin300354 China Tianjin Ren’ai College Tianjin301636 China School of Automobile NCO University of Army Military Transportation Anhui Bengbu233011 China Shanghai Institute of Aerospace System Engineering Shanghai201109 China

Driving training is essential to ensure road safety. Gamification is effective in improving training outcomes. This study explored the system design and development of a gamification-based driving training system. In the system, feedback from the human–machine interaction was enhanced playfully, game elements and mechanics were incorporated, and a software and hardware gamification system architecture was built and connected;thus, the trainee, software, and environment were integrated into a virtual game. To evaluate the developed gamification-based training system, 24 participants without driving experience were enrolled a manual transmission vehicle driving training experiment. The results showed that both the training efficiency and overall satisfaction of the gamification group were significantly higher than those of the control group, with a significance of p © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Efficiency

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A Comparative Study on the Potential of Unsupervised Deep Learning-based Feature Selection in Radiomics 44

A Comparative Study on the Potential of Unsupervised Deep Le...

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44th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2022

作者： Haueise, Tobias Liebgott, Annika Yang, Bin University Hospital Tuebingen Section on Experimental Radiology Tuebingen Germany Institute of Diabetes Research and Metabolic Diseases at Helmholtz Munich Tuebingen Germany Institute of Signal Processing and System Theory University of Stuttgart Stuttgart Germany

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

In Radiomics, deep learning-based systems for medical image analysis play an increasing role. However, due to the better explainability, feature-based systems are still preferred, especially by physicians. Often, high-dimensional data and low sample size pose different challenges (e.g. increased risk of overfitting) to machine learning systems. By removing irrelevant and redundant features from the data, feature selection is an effective way of pre-processing. The research in this study is focused on unsupervised deep learning-based methods for feature selection. Five recently proposed algorithms are compared regarding their applicability and efficiency on seven data sets in three different sample applications. It was found that deep learning-based feature selection leads to improved classification results compared to conventional methods, especially for small feature subsets. Clinical Relevance - The exploration of distinctive features and the ability to rank their importance without the need for outcome information is a potential field of application for unsupervised feature selection methods. Especially in multiparametric radiology, the number of features is increasing. The identification of new potential biomarkers is important both for treatment and prevention. © 2022 IEEE.

关键词： Feature Selection

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Optimization-Based Comparative system Evaluation of Single and Dual Traction Inverters with Focus on Partial Load Efficiency and Chip Area

Optimization-Based Comparative System Evaluation of Single a...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Sachs, Christoph Stamer, Fabian Allgeier, Jan Thrimawithana, Duleepa Neuburger, Martin Esslingen University Faculty of Mobility and Technology Germany University of Stuttgart Institute for System Dynamics Germany Robert Bosch GmbH Germany The University of Auckland Dept. of Electrical Computer and Software Engineering New Zealand

ISBN: (纸本)9798350376067

The transition to electric transportation demands efficient and cost-effective powertrains. Optimizing energy use is crucial for extending range and reducing expenses. However, comparing inverter and motor efficiency based on inverter topologies is challenging due to biased methodologies that favor certain designs over others. This document introduces a novel optimization-based approach for enhancing partial load efficiency and minimizing chip area of single and dual traction inverters, indicating potential energy savings and cost reduction. Recent publications of both industry [1], [2] and academia [3] underscore the importance of these design goals achieved by either novel inverter topologies or enhanced control methods. Two promising topologies with the inherent capability of partial load optimization are evaluated regarding chip area and system efficiency to find the most suitable concept for future electric vehicle power trains. © 2024 IEEE.

关键词： Energy utilization

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EEG motor imagery decoding: A framework for comparative analysis with channel attention mechanisms

arXiv

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arXiv 2023年

作者： Wimpff, Martin Gizzi, Leonardo Zerfowski, Jan Yang, Bin Institute of Signal Processing and System Theory University of Stuttgart Germany Fraunhofer Institute for Manufacturing Engineering and Automation IPA Germany Germany

Objective The objective of this study is to investigate the application of various channel attention mechanisms within the domain of brain-computer interface (BCI) for motor imagery decoding. Channel attention mechanisms can be seen as a powerful evolution of spatial filters traditionally used for motor imagery decoding. This study systematically compares such mechanisms by integrating them into a lightweight architecture framework to evaluate their impact. Approach We carefully construct a straightforward and lightweight baseline architecture designed to seamlessly integrate different channel attention mechanisms. This approach is contrary to previous works which only investigate one attention mechanism and usually build a very complex, sometimes nested architecture. Our framework allows us to evaluate and compare the impact of different attention mechanisms under the same circumstances. The easy integration of different channel attention mechanisms as well as the low computational complexity enables us to conduct a wide range of experiments on four datasets to thoroughly assess the effectiveness of the baseline model and the attention mechanisms. Results Our experiments demonstrate the strength and generalizability of our architecture framework as well as how channel attention mechanisms can improve the performance while maintaining the small memory footprint and low computational complexity of our baseline architecture. Significance Our architecture emphasizes simplicity, offering easy integration of channel attention mechanisms, while maintaining a high degree of generalizability across datasets, making it a versatile and efficient solution for EEG motor imagery decoding within brain-computer interfaces. © 2023, CC BY.

关键词： Decoding

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Benchmark on deep reinforcement learning-based placing using a robot arm 56

Benchmark on deep reinforcement learning-based placing using...

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56th International Symposium on Robotics, ISR Europe 2023

作者： Kernbach, Andreas Hoffmann, Kathrin Sawodny, Oliver Eivazi, Shahram Institute of Industrial Manufacturing and Management IFF University of Stuttgart Department Cyber Cognitive Intelligence CCI Fraunhofer Institute for Manufacturing Engineering and Automation IPA Germany University of Stuttgart Institute for System Dynamics Germany Festo SE & Co. KG University of Tübingen Germany

ISBN: (纸本)9783800761418

Deep reinforcement learning (DRL) is an approach by which an agent can learn to solve complex sequential tasks in a try-and-error manner. As such, it has been widely explored in robotic domain for solving manipulation tasks. In this paper we benchmark DRL approaches utilizing pure image-based observations and simulation-based numerical ground truth (oracle) observations. The underlying benchmark task is inspired by an industrial application where a robotic arm is required to position up to nine boxes of random sizes into a container with variable pose. Central for the comparison is the robustness of the task execution and the duration of DRL training. We first simulate the task based on a real world logistic setup and parameters for a vision based reinforcement learning agent that operates in real time. Second, we compare DRL algorithms and observation types with policies that decide the desired end-effector pose of the to-be-placed box. Our results show the Proximal Policy Optimization (PPO) algorithm has the highest performance with satisfactory robustness and learning time. Moreover, we successfully perform the placing task by relying only upon image-based input in which the robot arm robustly places 8.8+0−0.261 of nine objects on average into the container, increasing the required learning time by 233 % compared to oracle observations. © VDE VERLAG GMBH Berlin Offenbach.

关键词： AI in Robotics Image-based Reinforcement learning

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Exploring Domain Shift on Radar-Based 3D Object Detection Amidst Diverse Environmental Conditions

Exploring Domain Shift on Radar-Based 3D Object Detection Am...

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International Conference on Intelligent Transportation

作者： Miao Zhang Sherif Abdulatif Benedikt Loesch Marco Altmann Marius Schwarz Bin Yang Robert Bosch GmbH Corporate Research Germany University of Stuttgart Institute of Signal Processing and System Theory Germany

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

The rapid evolution of deep learning and its integration with autonomous driving systems have led to substantial advancements in 3D perception using multimodal sensors. Notably, radar sensors show greater robustness compared to cameras and lidar under adverse weather and varying illumination conditions. This study delves into the often-overlooked yet crucial issue of domain shift in 4D radar-based object detection, examining how varying environmental conditions, such as different weather patterns and road types, impact 3D object detection performance. Our findings highlight distinct domain shifts across various weather scenarios, revealing unique dataset sensitivities that underscore the critical role of radar point cloud generation. Additionally, we demonstrate that transitioning between different road types, especially from highways to urban settings, introduces notable domain shifts, emphasizing the necessity for diverse data collection across varied road environments. To the best of our knowledge, this is the first comprehensive analysis of domain shift effects on 4D radar-based object detection. We believe this empirical study contributes to understanding the complex nature of domain shifts in radar data and suggests paths forward for data collection strategy in the face of environmental variability.

关键词： Point cloud compression Meteorological radar Three-dimensional displays Sensitivity Roads Urban areas Object detection Data collection Robustness Meteorology

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Class-Aware PillarMix: Can Mixed Sample Data Augmentation Enhance 3D Object Detection with Radar Point Clouds?

arXiv

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arXiv 2025年

作者： Zhang, Miao Abdulatif, Sherif Loesch, Benedikt Altmann, Marco Yang, Bin Robert Bosch GmbH Corporate Research 71272 Germany University of Stuttgart Institute of Signal Processing and System Theory 70569 Germany

Due to the significant effort required for data collection and annotation in 3D perception tasks, mixed sample data augmentation (MSDA) has been widely studied to generate diverse training samples by mixing existing data. Among these methods, MixUp is a prominent approach that generates new samples by linearly combining two existing ones, using a mix ratio sampled from a β distribution. This simple yet powerful method has inspired numerous variations and applications in 2D and 3D data domains. Recently, many MSDA techniques have been developed for point clouds, but they mainly target LiDAR data, leaving their application to radar point clouds largely unexplored. In this paper, we examine the feasibility of applying existing MSDA methods to radar point clouds and identify several challenges in adapting these techniques. These obstacles stem from the radar’s irregular angular distribution, deviations from a single-sensor polar layout in multi-radar setups, and point sparsity. To address these issues, we propose Class-Aware PillarMix (CAPMix), a novel MSDA approach that applies MixUp at the pillar level in 3D point clouds, guided by class labels. Unlike methods that rely a single mix ratio to the entire sample, CAPMix assigns an independent ratio to each pillar, boosting sample diversity. To account for the density of different classes, we use class-specific distributions: for dense objects (e.g., large vehicles), we skew ratios to favor points from another sample, while for sparse objects (e.g., pedestrians), we sample more points from the original. This class-aware mixing retains critical details and enriches each sample with new information, ultimately generating more diverse training data. Experimental results demonstrate that our method not only significantly boosts performance but also outperforms existing MSDA approaches across two datasets (Bosch Street and K-Radar). We believe that this straightforward yet effective approach will spark further investigation in

关键词： Object detection

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Towards Pragmatic Semantic Image Synthesis for Urban Scenes

Towards Pragmatic Semantic Image Synthesis for Urban Scenes

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IEEE Symposium on Intelligent Vehicle

作者： George Eskandar Diandian Guo Karim Guirguis Bin Yang Institute of Signal Processing and System Theory University of Stuttgart Stuttgart Germany Bosch Center for Artificial Intelligence Renningen Germany

The need for large amounts of training and validation data is a huge concern in scaling AI algorithms for autonomous driving. Semantic Image Synthesis (SIS), or label-to-image translation, promises to address this issue by translating semantic layouts to images, providing a controllable generation of photorealistic data. However, they require a large amount of paired data, incurring extra costs. In this work, we present a new task: given a dataset with synthetic images and labels and a dataset with unlabeled real images, our goal is to learn a model that can generate images with the content of the input mask and the appearance of real images. This new task reframes the well-known unsupervised SIS task in a more practical setting, where we leverage cheaply available synthetic data from a driving simulator to learn how to generate photorealistic images of urban scenes. This stands in contrast to previous works, which assume that labels and images come from the same domain but are unpaired during training. We find that previous unsupervised works underperform on this task, as they do not handle distribution shifts between two different domains. To bypass these problems, we propose a novel framework with two main contributions. First, we leverage the synthetic image as a guide to the content of the generated image by penalizing the difference between their high-level features on a patch level. Second, in contrast to previous works which employ one discriminator that overfits the target domain semantic distribution, we employ a discriminator for the whole image and multiscale discriminators on the image patches. Extensive comparisons on the benchmarks benchmarks GTA-V → Cityscapes and GTA-V → Mapillary show the superior performance of the proposed model against state-of-the-art on this task.

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Deep Regression for Biological Age Estimation in Multiple Organs: Investigations on 40,000 Subjects of the UK Biobank

Deep Regression for Biological Age Estimation in Multiple Or...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Veronika Ecker Marcel Früh Bin Yang Sergios Gatidis Thomas Küstner Institute of Signal Processing and System Theory University of Stuttgart Stuttgart Germany Department of Radiology MIDAS.lab University Hospital of Tübingen Tübingen Germany

Age plays an important role in shaping medical decisions, but the biological changes associated with aging do not solely depend on the chronological age. Genetics, lifestyle, and environment cause variations in age-related characteristics, even within the same chronological age group. Biological age (BA) was introduced to better capture an individual’s actual aging process, although its imprecise definition remains a challenge. Organ systems can age at different rates, necessitating organ-specific BA evaluation. To our knowledge, there have been no studies assessing age regionally across multiple organ systems. These age estimations, reflecting various body parts, enable a comprehensive patient-based analysis. We conducted brain, heart, kidney, liver, spleen, pancreas, and retinal fundus age estimations using MRI and OCT scans in 40,000 subjects of the UK Biobank with an uncertainty-aware ResNet-based network. Our results demonstrate the feasibility of organ-specific age estimation with cross-organ correlations of age-related changes. We achieve a mean age difference between predicted and chronological age of 2.72 years across all organs and an averaged Pearson correlation coefficient of 0.87.

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Towards Pragmatic Semantic Image Synthesis for Urban Scenes

arXiv

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arXiv 2023年

作者： Eskandar, George Guo, Diandian Guirguis, Karim Yang, Bin Institute of Signal Processing and System Theory University of Stuttgart Stuttgart Germany Bosch Center for Artificial Intelligence Renningen Germany

The need for large amounts of training and validation data is a huge concern in scaling AI algorithms for autonomous driving. Semantic Image Synthesis (SIS), or label-to-image translation, promises to address this issue by translating semantic layouts to images, providing a controllable generation of photorealistic data. However, they require a large amount of paired data, incurring extra costs. In this work, we present a new task: given a dataset with synthetic images and labels and a dataset with unlabeled real images, our goal is to learn a model that can generate images with the content of the input mask and the appearance of real images. This new task reframes the well-known unsupervised SIS task in a more practical setting, where we leverage cheaply available synthetic data from a driving simulator to learn how to generate photorealistic images of urban scenes. This stands in contrast to previous works, which assume that labels and images come from the same domain but are unpaired during training. We find that previous unsupervised works underperform on this task, as they do not handle distribution shifts between two different domains. To bypass these problems, we propose a novel framework with two main contributions. First, we leverage the synthetic image as a guide to the content of the generated image by penalizing the difference between their high-level features on a patch level. Second, in contrast to previous works which employ one discriminator that overfits the target domain semantic distribution, we employ a discriminator for the whole image and multiscale discriminators on the image patches. Extensive comparisons on the benchmarks GTA-V → Cityscapes and GTA-V → Mapillary show the superior performance of the proposed model against state-of-the-art on this task. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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