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2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS)最新文献

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Emotion-Awareness for Intelligent Vehicle Assistants: A Research Agenda 智能汽车助手的情感感知:一个研究议程
2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS) Pub Date : 2018-05-28 DOI: 10.1145/3194085.3194094
Hans-Jörg Vögel, Christian Süß, T. Hubregtsen, E. André, Björn Schuller, Jérôme Härri, J. Conradt, Asaf Adi, Alexander Zadorojniy, J. Terken, J. Beskow, A. Morrison, K. Eng, F. Eyben, S. Moubayed, Susanne Muller, N. Cummins, V. Ghaderi, Ronee Chadowitz, Raphael Troncy, B. Huet, Melek Önen, A. Ksentini
{"title":"Emotion-Awareness for Intelligent Vehicle Assistants: A Research Agenda","authors":"Hans-Jörg Vögel, Christian Süß, T. Hubregtsen, E. André, Björn Schuller, Jérôme Härri, J. Conradt, Asaf Adi, Alexander Zadorojniy, J. Terken, J. Beskow, A. Morrison, K. Eng, F. Eyben, S. Moubayed, Susanne Muller, N. Cummins, V. Ghaderi, Ronee Chadowitz, Raphael Troncy, B. Huet, Melek Önen, A. Ksentini","doi":"10.1145/3194085.3194094","DOIUrl":"https://doi.org/10.1145/3194085.3194094","url":null,"abstract":"EVA is describing a new class of emotion-aware autonomous systems delivering intelligent personal assistant functionalities. EVA requires a multi-disciplinary approach, combining a number of critical building blocks into a cybernetics systems/software architecture: emotion aware systems and algorithms, multimodal interaction design, cognitive modelling, decision making and recommender systems, emotion sensing as feedback for learning, and distributed (edge) computing delivering cognitive services.","PeriodicalId":360022,"journal":{"name":"2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121254904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 28
Toward a Methodology for Training with Synthetic Data on the Example of Pedestrian Detection in a Frame-by-Frame Semantic Segmentation Task 以逐帧语义分割任务中行人检测为例的综合数据训练方法研究
2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS) Pub Date : 2018-05-28 DOI: 10.1145/3194085.3194093
Atanas Poibrenski, J. Sprenger, Christian Müller
{"title":"Toward a Methodology for Training with Synthetic Data on the Example of Pedestrian Detection in a Frame-by-Frame Semantic Segmentation Task","authors":"Atanas Poibrenski, J. Sprenger, Christian Müller","doi":"10.1145/3194085.3194093","DOIUrl":"https://doi.org/10.1145/3194085.3194093","url":null,"abstract":"In order to make highly/fully automated driving safe, synthetic training and validation data will be required, because critical road situations are too divers and too rare. A few studies on using synthetic data have been published, reporting a general increase in accuracy. In this paper, we propose a novel method to gain more in-depth insights in the quality, performance, and influence of synthetic data during training phase in a bounded setting. We demonstrate this method for the example of pedestrian detection in a frame-by-frame semantic segmentation class.","PeriodicalId":360022,"journal":{"name":"2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133784897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
Deep Learning for Self-Driving Cars: Chances and Challenges 自动驾驶汽车的深度学习:机遇与挑战
2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS) Pub Date : 2018-05-28 DOI: 10.1145/3194085.3194087
Qing Rao, Jelena Frtunikj
{"title":"Deep Learning for Self-Driving Cars: Chances and Challenges","authors":"Qing Rao, Jelena Frtunikj","doi":"10.1145/3194085.3194087","DOIUrl":"https://doi.org/10.1145/3194085.3194087","url":null,"abstract":"Artificial Intelligence (AI) is revolutionizing the modern society. In the automotive industry, researchers and developers are actively pushing deep learning based approaches for autonomous driving. However, before a neural network finds its way into series production cars, it has to first undergo strict assessment concerning functional safety. The chances and challenges of incorporating deep learning for self-driving cars are presented in this paper.","PeriodicalId":360022,"journal":{"name":"2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129889502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 161
A Data-Driven Generative Model for GPS Sensors for Autonomous Driving 面向自动驾驶的GPS传感器数据驱动生成模型
2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS) Pub Date : 2018-05-28 DOI: 10.1145/3194085.3194089
Erik Karlsson, N. Mohammadiha
{"title":"A Data-Driven Generative Model for GPS Sensors for Autonomous Driving","authors":"Erik Karlsson, N. Mohammadiha","doi":"10.1145/3194085.3194089","DOIUrl":"https://doi.org/10.1145/3194085.3194089","url":null,"abstract":"Autonomous driving (AD) is envisioned to have a significant impact on people's life regarding safety and comfort. Positioning is one of the key challenges in realizing AD, where global navigation systems (GNSS) is traditionally used as an important source of information. The area of GNSS are well explored and the different sources of error are deeply investigated. However the existing modeling methods often have very comprehensive requirements for the training data where all affecting conditions such as ephemeris data should be well known. The main goal of this paper is to develop a solution to model GPS error that only requires information which is available in the vehicle without having access to detailed information about the conditions. We propose a statistical generative model using autoregression and Gaussian mixture models and develop a learning algorithm to estimate the parameters using the data collected in real traffic. The proposed model is evaluated by comparing the produced artificial data with the validation data collected at different traffic conditions and the results indicate that the model is successfully mimicking the sensor behavior.","PeriodicalId":360022,"journal":{"name":"2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129310454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Toward a Holistic Software Systems Engineering Approach for Dependable Autonomous Systems 面向可靠自治系统的整体软件系统工程方法
2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS) Pub Date : 2018-05-28 DOI: 10.1145/3194085.3194091
Adina Aniculaesei, J. Grieser, A. Rausch, Karina Rehfeldt, Tim Warnecke
{"title":"Toward a Holistic Software Systems Engineering Approach for Dependable Autonomous Systems","authors":"Adina Aniculaesei, J. Grieser, A. Rausch, Karina Rehfeldt, Tim Warnecke","doi":"10.1145/3194085.3194091","DOIUrl":"https://doi.org/10.1145/3194085.3194091","url":null,"abstract":"Autonomous systems are gaining momentum in various application domains, such as autonomous vehicles, autonomous transport robotics and self-adaptation in smart homes. Product liability regulations impose high standards on manufacturers of such systems with respect to dependability (safety, security and privacy). Today’s conventional engineering methods are not adequate for providing guarantees with respect to dependability requirements in a costefficient manner, e.g. road tests in the automotive industry sum up millions of miles before a system can be considered sufficiently safe. System engineers will no longer be able to test and respectively formally verify autonomous systems during development time in order to guarantee the dependability requirements in advance. In this vision paper, we introduce a new holistic software systems engineering approach for autonomous systems, which integrates development time methods as well as operation time techniques. With this approach, we aim to give the users a transparent view of the confidence level of the autonomous system under use with respect to the dependability requirements. We present already obtained results and point out research goals to be addressed in the future.","PeriodicalId":360022,"journal":{"name":"2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123261619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 40
Distributed Deep Reinforcement Learning on the Cloud for Autonomous Driving 自动驾驶云上的分布式深度强化学习
2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS) Pub Date : 2018-05-28 DOI: 10.1145/3194085.3194088
Mitchell Spryn, Aditya Sharma, Dhawal Parkar, Madhu Shrimal
{"title":"Distributed Deep Reinforcement Learning on the Cloud for Autonomous Driving","authors":"Mitchell Spryn, Aditya Sharma, Dhawal Parkar, Madhu Shrimal","doi":"10.1145/3194085.3194088","DOIUrl":"https://doi.org/10.1145/3194085.3194088","url":null,"abstract":"This paper proposes an architecture for leveraging cloud computing technology to reduce training time for deep reinforcement learning models for autonomous driving by distributing the training process across a pool of virtual machines. By parallelizing the training process, careful design of the reward function and use of techniques like transfer learning, we demonstrate a decrease in training time for our example autonomous driving problem from 140 hours to less than 1 hour. We go over our network architecture, job distribution paradigm, reward function design and report results from experiments on small sized cluster (1-6 training nodes) of machines. We also discuss the limitations of our approach when trying to scale up to massive clusters.","PeriodicalId":360022,"journal":{"name":"2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123752173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 12
Exploiting Learning and Scenario-Based Specification Languages for the Verification and Validation of Highly Automated Driving 利用学习和基于场景的规范语言进行高度自动驾驶的验证和验证
2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS) Pub Date : 2018-05-28 DOI: 10.1145/3194085.3194086
W. Damm, R. Galbas
{"title":"Exploiting Learning and Scenario-Based Specification Languages for the Verification and Validation of Highly Automated Driving","authors":"W. Damm, R. Galbas","doi":"10.1145/3194085.3194086","DOIUrl":"https://doi.org/10.1145/3194085.3194086","url":null,"abstract":"We propose a series of methods based on learning key structural properties from traffic data-basis and on statistical model checking, ultimately leading to the construction of a scenario catalogue capturing requirements for controlling criticality for highly autonomous vehicles. We sketch underlying mathematical foundations which allow to derive formal confidence levels that vehicles tested by such a scenario catalogue will maintain the required control of criticality in real traffic matching the probability distributions of key parameters of data recorded in the reference data base employed for this process.","PeriodicalId":360022,"journal":{"name":"2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS)","volume":"326 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122633129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 11
Automotive Safety and Machine Learning: Initial Results from a Study on How to Adapt the ISO 26262 Safety Standard 汽车安全和机器学习:如何适应ISO 26262安全标准研究的初步结果
2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS) Pub Date : 2018-05-28 DOI: 10.1145/3194085.3194090
Jens Henriksson, Markus Borg, Cristofer Englund
{"title":"Automotive Safety and Machine Learning: Initial Results from a Study on How to Adapt the ISO 26262 Safety Standard","authors":"Jens Henriksson, Markus Borg, Cristofer Englund","doi":"10.1145/3194085.3194090","DOIUrl":"https://doi.org/10.1145/3194085.3194090","url":null,"abstract":"Machine learning (ML) applications generate a continuous stream of success stories from various domains. ML enables many novel applications, also in safety-critical contexts. However, the functional safety standards such as ISO 26262 did not evolve to cover ML. We conduct an exploratory study on which parts of ISO 26262 represent the most critical gaps between safety engineering and ML development. While this paper only reports the first steps toward a larger research endeavor, we report three adaptations that are critically needed to allow ISO 26262 compliant engineering, and related suggestions on how to evolve the standard.","PeriodicalId":360022,"journal":{"name":"2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130315465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 39
How Machine Perception Relates to Human Perception: Visual Saliency and Distance in a Frame-by-Frame Semantic Segmentation Task for Highly/Fully Automated Driving 机器感知如何与人类感知相关:高度/全自动驾驶逐帧语义分割任务中的视觉显著性和距离
2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS) Pub Date : 2018-05-28 DOI: 10.1145/3194085.3194092
Nico Herbig, Frederik Wiehr, Atanas Poibrenski, J. Sprenger, Christian Müller
{"title":"How Machine Perception Relates to Human Perception: Visual Saliency and Distance in a Frame-by-Frame Semantic Segmentation Task for Highly/Fully Automated Driving","authors":"Nico Herbig, Frederik Wiehr, Atanas Poibrenski, J. Sprenger, Christian Müller","doi":"10.1145/3194085.3194092","DOIUrl":"https://doi.org/10.1145/3194085.3194092","url":null,"abstract":"In this paper, we investigate the link between machine perception and human perception for highly/fully automated driving. We compare the classification results of a camera-based frame-by-frame semantic segmentation model Machine with a well-established visual saliency model Human on the Cityscapes dataset. The results show that Machine classifies foreground objects better if they are more salient, indicating a similarity with the human visual system. For background objects, the accuracy drops when the saliency increases, giving evidence for the assumption that Machine has an implicit concept of saliency.","PeriodicalId":360022,"journal":{"name":"2018 IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116648525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
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