2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)最新文献_第6页

Human-Vehicle Cooperation on Prediction-Level: Enhancing Automated Driving with Human Foresight 预测层面的人车合作:用人类的远见增强自动驾驶

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-04-07 DOI: 10.1109/IVWorkshops54471.2021.9669247

Chao Wang, Thomas H. Weisswange, Matti Krüger, Christiane B. Wiebel-Herboth

{"title":"Human-Vehicle Cooperation on Prediction-Level: Enhancing Automated Driving with Human Foresight","authors":"Chao Wang, Thomas H. Weisswange, Matti Krüger, Christiane B. Wiebel-Herboth","doi":"10.1109/IVWorkshops54471.2021.9669247","DOIUrl":"https://doi.org/10.1109/IVWorkshops54471.2021.9669247","url":null,"abstract":"To maximize safety and driving comfort, autonomous driving systems can benefit from implementing foresighted action choices that take different potential scenario developments into account. While artificial scene prediction methods are making fast progress, an attentive human driver may still be able to identify relevant contextual features which are not adequately considered by the system or for which the human driver may have a lack of trust into the system’s capabilities to treat them appropriately. We implement an approach that lets a human driver quickly and intuitively supplement scene predictions to an autonomous driving system by gaze. We illustrate the feasibility of this approach in an existing autonomous driving system running a variety of scenarios in a simulator. Furthermore, a Graphical User Interface (GUI) was designed and integrated to enhance the trust and explainability of the system. The utilization of such cooperatively augmented scenario predictions has the potential to improve a system’s foresighted driving abilities and make autonomous driving more trustable, comfortable and personalized.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120958739","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

The ConScenD Dataset: Concrete Scenarios from the highD Dataset According to ALKS Regulation UNECE R157 in OpenX ConScenD数据集:根据OpenX中ALKS法规UNECE R157的high - d数据集的具体场景

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-03-17 DOI: 10.1109/ivworkshops54471.2021.9669219

A. Tenbrock, A. Koenig, Thomas Keutgens, Julian Bock, Hendrik Weber, R. Krajewski, A. Zlocki

{"title":"The ConScenD Dataset: Concrete Scenarios from the highD Dataset According to ALKS Regulation UNECE R157 in OpenX","authors":"A. Tenbrock, A. Koenig, Thomas Keutgens, Julian Bock, Hendrik Weber, R. Krajewski, A. Zlocki","doi":"10.1109/ivworkshops54471.2021.9669219","DOIUrl":"https://doi.org/10.1109/ivworkshops54471.2021.9669219","url":null,"abstract":"With Regulation UNECE R157 on Automated Lane-Keeping Systems, the first framework for the introduction of passenger cars with Level 3 systems has become available in 2020. In accordance with recent research projects including academia and the automotive industry, the Regulation utilizes scenario based testing for the safety assessment. The complexity of safety validation of automated driving systems necessitates system-level simulations. The Regulation, however, is missing the required parameterization necessary for test case generation. To overcome this problem, we incorporate the exposure and consider the heterogeneous behavior of the traffic participants by extracting concrete scenarios according to Regulation's scenario definition from the established naturalistic highway dataset highD. We present a methodology to find the scenarios in real-world data, extract the parameters for modeling the scenarios and transfer them to simulation. In this process, more than 340 scenarios were extracted. OpenSCENARIO files were generated to enable an exemplary transfer of the scenarios to CARLA and esmini. We compare the trajectories to examine the similarity of the scenarios in the simulation to the recorded scenarios. In order to foster research, we publish the resulting dataset called ConScenD together with instructions for usage with both simulation tools. The dataset is available online at https://www.levelXdata.com/scenarios.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"339 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123317469","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

Investigating Value of Curriculum Reinforcement Learning in Autonomous Driving Under Diverse Road and Weather Conditions 课程强化学习在不同道路和天气条件下自动驾驶中的价值研究

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-03-14 DOI: 10.1109/ivworkshops54471.2021.9669203

Anil Öztürk, Mustafa Burak Gunel, Resul Dagdanov, Mirac Ekim Vural, Ferhat Yurdakul, Melih Dal, N. K. Ure

{"title":"Investigating Value of Curriculum Reinforcement Learning in Autonomous Driving Under Diverse Road and Weather Conditions","authors":"Anil Öztürk, Mustafa Burak Gunel, Resul Dagdanov, Mirac Ekim Vural, Ferhat Yurdakul, Melih Dal, N. K. Ure","doi":"10.1109/ivworkshops54471.2021.9669203","DOIUrl":"https://doi.org/10.1109/ivworkshops54471.2021.9669203","url":null,"abstract":"Applications of reinforcement learning (RL) are popular in autonomous driving tasks. That being said, tuning the performance of an RL agent and guaranteeing the generalization performance across variety of different driving scenarios is still largely an open problem. In particular, getting good performance on complex road and weather conditions require exhaustive tuning and computation time. Curriculum RL, which focuses on solving simpler automation tasks in order to transfer knowledge to complex tasks, is attracting attention in RL community. The main contribution of this paper is a systematic study for investigating the value of curriculum reinforcement learning in autonomous driving applications. For this purpose, we setup several different driving scenarios in a realistic driving simulator, with varying road complexity and weather conditions. Next, we train and evaluate performance of RL agents on different sequences of task combinations and curricula. Results show that curriculum RL can yield significant gains in complex driving tasks, both in terms of driving performance and sample complexity. Results also demonstrate that different curricula might enable different benefits, which hints future research directions for automated curriculum training.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126643179","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: 3

Cybersecurity Threats in Connected and Automated Vehicles based Federated Learning Systems 基于联邦学习系统的联网和自动驾驶汽车的网络安全威胁

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-02-26 DOI: 10.1109/ivworkshops54471.2021.9669214

Ranwa Al Mallah, Godwin Badu-Marfo, B. Farooq

{"title":"Cybersecurity Threats in Connected and Automated Vehicles based Federated Learning Systems","authors":"Ranwa Al Mallah, Godwin Badu-Marfo, B. Farooq","doi":"10.1109/ivworkshops54471.2021.9669214","DOIUrl":"https://doi.org/10.1109/ivworkshops54471.2021.9669214","url":null,"abstract":"Federated learning (FL) is a machine learning technique that aims at training an algorithm across decentralized entities holding their local data private. Wireless mobile networks allow users to communicate with other fixed or mobile users. The road traffic network represents an infrastructure-based configuration of a wireless mobile network where the Connected and Automated Vehicles (CAV) represent the communicating entities. Applying FL in a wireless mobile network setting gives rise to a new threat in the mobile environment that is very different from the traditional fixed networks. The threat is due to the intrinsic characteristics of the wireless medium and is caused by the characteristics of the vehicular networks such as high node-mobility and rapidly changing topology. Most cyber defense techniques depend on highly reliable and connected networks. This paper explores falsified information attacks, which target the FL process that is ongoing at the RSU. We identified a number of attack strategies conducted by the malicious CAVs to disrupt the training of the global model in vehicular networks. We show that the attacks were able to increase the convergence time and decrease the accuracy of the model. We demonstrate that our attacks bypass FL defense strategies in their primary form and highlight the need for novel poisoning resilience defense mechanisms in the wireless mobile setting of the future road networks.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126401959","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

Adaptive Video Configuration and Bitrate Allocation for Teleoperated Vehicles 遥控车辆的自适应视频配置和比特率分配

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-02-22 DOI: 10.1109/ivworkshops54471.2021.9669258

Andreas Schimpe, S. Hoffmann, Frank Diermeyer

{"title":"Adaptive Video Configuration and Bitrate Allocation for Teleoperated Vehicles","authors":"Andreas Schimpe, S. Hoffmann, Frank Diermeyer","doi":"10.1109/ivworkshops54471.2021.9669258","DOIUrl":"https://doi.org/10.1109/ivworkshops54471.2021.9669258","url":null,"abstract":"Vehicles with autonomous driving capabilities are present on public streets. However, edge cases remain that still require a human in-vehicle driver. Assuming the vehicle manages to come to a safe state in an automated fashion, teleoperated driving technology enables a human to resolve the situation remotely by a control interface connected via a mobile network. While this is a promising solution, it also introduces technical challenges, one of them being the necessity to transmit video data of multiple cameras from the vehicle to the human operator. In this paper, an adaptive video streaming framework specifically designed for teleoperated vehicles is proposed and demonstrated. The framework enables automatic reconfiguration of the video streams of the multi-camera system at runtime. Predictions of variable transmission service quality are taken into account. With the objective to improve visual quality, the framework uses so-called rate-quality models to dynamically allocate bitrates and select resolution scaling factors. Results from deploying the proposed framework on an actual teleoperated driving system are presented.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131059314","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: 7

Numerically Stable Dynamic Bicycle Model for Discrete-time Control 离散时间控制的数值稳定动态自行车模型

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2020-11-19 DOI: 10.1109/ivworkshops54471.2021.9669260

Qiang Ge, S. Li, Qi Sun, Sifa Zheng

引用次数: 26