2023 IEEE Intelligent Vehicles Symposium (IV)最新文献_第6页

Literature Review on Maneuver-Based Scenario Description for Automated Driving Simulations 基于机动的自动驾驶仿真场景描述研究综述

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186545

Nicole Neis, Juergen Beyerer

引用次数: 0

Multi-layer Edge Computing for Cooperative Driving Control Optimization in Smart Cities 智慧城市协同驾驶控制优化的多层边缘计算

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186775

Y. Inagaki, A. Nakao

{"title":"Multi-layer Edge Computing for Cooperative Driving Control Optimization in Smart Cities","authors":"Y. Inagaki, A. Nakao","doi":"10.1109/IV55152.2023.10186775","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186775","url":null,"abstract":"Recently, \"cooperative driving\" in which multiple vehicles acquire, coordinate, and control their position information and drive cooperatively at intersections and merging points in urban areas, has been attracting attention. In cooperative driving, there is a trade-off between the amount of information collected at a control point and the latency in information collection to achieve optimal real-time control. This trade-off makes it difficult to process the information required for each cooperative driving control at the optimum position, hard to satisfy both information and latency requirements in control, and to implement multiple types of cooperative driving controls simultaneously. In light of this observation, there is a problem that control by a single-layer Edge Server (ES) cannot solve those events and cannot optimize the cooperative driving control. To solve the problem, we propose a \"multi-layer ES\" for selecting the optimal layer of computation depending on the nature of the information to be collected by the Intelligent Transport System (ITS). This multi-layer ES enables multiple types of cooperative driving control simultaneously while satisfying the requirements and optimizing the control. In this paper, we use an urban expressway as a use case and perform simulations using real traffic data. We show that the cooperative driving control using our proposed multi-layer ES reduces natural and accidental traffic congestion, and reduces the average travel time per vehicle by 55.76% compared to the case without multi-layer ES, thus shown to be an effective approach for realizing a smart city.","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114890923","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

Using the Transferable Belief Model for Object Classification in LiDAR Data With Geometry, Motion and Context Features 基于可转移信念模型的具有几何、运动和背景特征的激光雷达目标分类

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186578

Juan D. González, Michael Kusenbach, Hans-Joachim Wuensche

引用次数: 0

Real-Time Traffic Prediction Considering Lane Changing Maneuvers with Application to Eco-Driving Control of Electric Vehicles 考虑变道机动的实时交通预测及其在电动汽车生态驾驶控制中的应用

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186645

Suiyi He, Shian Wang, Y. Shao, Zongxuan Sun, M. Levin

{"title":"Real-Time Traffic Prediction Considering Lane Changing Maneuvers with Application to Eco-Driving Control of Electric Vehicles","authors":"Suiyi He, Shian Wang, Y. Shao, Zongxuan Sun, M. Levin","doi":"10.1109/IV55152.2023.10186645","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186645","url":null,"abstract":"Emerging vehicle sensing and communication technologies allow for real-time information exchange between connected vehicles (CVs) and intelligent infrastructure. This presents a unique opportunity for predicting traffic states such as speed and density. A promising application of traffic prediction is eco-driving speed control of CVs, which requires future traffic information along the look-ahead time horizon. However, it is challenging to obtain accurate real-time traffic prediction for the next 10-15 s, particularly for mixed traffic involving both CVs and human-driven vehicles (HVs), complicated further by the presence of lane changing maneuvers. In this article, we address this pressing problem by integrating a macroscopic traffic flow model for prediction with a microscopic vehicle model for speed control. Specifically, we modify the well-known second-order Payne-Whitham (PW) model to account for the impacts of lane changing on traffic state evolution, based on which we develop a traffic prediction framework capable of handling mixed traffic. CVs provide partial measurements of traffic states, while the unknown states are estimated using an unscented Kalman filter (UKF). Consequently, future traffic states are obtained by propagating the PW model forward in time, and optimal eco-driving speed controls are obtained for electric vehicles (EVs) using the prediction results. The proposed approach is evaluated using ample traffic data collected from Simulation of Urban MObility (SUMO). The results show an average energy benefit of 6.6% for the ego vehicle considering all the simulated scenarios, among which the maximum energy benefit is about 16.18%.","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128358280","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: 0

Attack Simulation and Adaptation in CAN for Training and Evaluation of IDS 针对入侵防御系统训练与评估的CAN攻击仿真与自适应

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186799

J. Laufenberg, Susanne Throner, T. Kropf, O. Bringmann

引用次数: 0

3D Multi-Object Tracking based on Two-Stage Data Association for Collaborative Perception Scenarios 协同感知场景下基于两阶段数据关联的三维多目标跟踪

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186777

Hao Su, S. Arakawa, Masayuki Murata

{"title":"3D Multi-Object Tracking based on Two-Stage Data Association for Collaborative Perception Scenarios","authors":"Hao Su, S. Arakawa, Masayuki Murata","doi":"10.1109/IV55152.2023.10186777","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186777","url":null,"abstract":"This paper proposes a 3D multi-object tracker suitable for collaborative perception scenarios. Our tracker aims to associate detection candidates obtained from the ego-vehicle after performing collaborative perception over time. It considers the temporal asynchronous information exchanged among connected vehicles and focuses on dealing with objects that fail to track due to missed detection. To achieve this, we propose a two-stage data association module with a supplementary mechanism. It adapts the association strategy to track objects according to their state robustly. Specifically, the first stage works on most general objects. The second stage aims to associate spatiotemporal asynchronous detection candidates or tracked objects consecutively missed multiple times. A supplementary mechanism is applied to temporarily missed objects by the detector. We conduct experiments on the DAIR-V2X dataset and use the detection candidates generated by a collaborative detection module. Experimental results demonstrate that the proposed method outperforms baselines in tracking performance while achieving comparable speed.","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132932678","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

Structured Natural Language for expressing Rules of the Road for Automated Driving Systems 表达自动驾驶系统道路规则的结构化自然语言

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186664

Patrick Irvine, A. B. D. Costa, Xizhe Zhang, S. Khastgir, P. Jennings

{"title":"Structured Natural Language for expressing Rules of the Road for Automated Driving Systems","authors":"Patrick Irvine, A. B. D. Costa, Xizhe Zhang, S. Khastgir, P. Jennings","doi":"10.1109/IV55152.2023.10186664","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186664","url":null,"abstract":"Automated Driving Systems (ADSs), like human drivers, must be compliant with the rules of the road. However, current rules of the road are not well defined. They use inconsistent and ambiguous language. As a result, they are not sufficiently formal for machine interpretability, a necessity for applications of verification and validation (V&V) of ADSs. Rules must be defined in a way that make them usable to a variety of stakeholders. While first-order and temporal logic forms of rules of the road are needed for monitoring and verification during simulation and testing, a structured natural language for these rules is necessary for consistent definition. They must also adhering to standard vocabulary taxonomies of Operational Design Domain (ODD) and behaviour. This paper contributes a structured natural language based on formal logic, that allows rules of the road to be defined in a natural, yet precise manner, using concepts of ODD and behaviour, making them usable in the V&V of ADSs. We evaluate the effectiveness of the language on a selection of rules from the Vienna Convention on Road Traffic and the UK Highway Code.","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133466838","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: 0

Improving Infrastructure and Community Resilience with Shared Autonomous Electric Vehicles (SAEV-R) 共享自动驾驶电动汽车(SAEV-R)改善基础设施和社区弹性

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186785

J. Yu, Michael F. Hyland, A. Chen

引用次数: 0

Biased Target-tree * Algorithm with RRT * for Reducing Parking Path Planning Time 减少停车路径规划时间的RRT有偏目标树*算法

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186712

Joonwoo Ahn, Minsoo Kim, Jaeheung Park

引用次数: 0

Seatbelt Segmentation Using Synthetic Images 使用合成图像的安全带分割

2023 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2023-06-04 DOI: 10.1109/IV55152.2023.10186571

S. Jha, Isaac Brooks, Soumitry J. Ray, R. Narasimha, N. Al-Dhahir, Carlos Busso

{"title":"Seatbelt Segmentation Using Synthetic Images","authors":"S. Jha, Isaac Brooks, Soumitry J. Ray, R. Narasimha, N. Al-Dhahir, Carlos Busso","doi":"10.1109/IV55152.2023.10186571","DOIUrl":"https://doi.org/10.1109/IV55152.2023.10186571","url":null,"abstract":"Recent advancement in deep learning has led to an increased interest in image processing and computer vision applications for driver monitoring systems. One of the applications where these techniques can be useful is in segmenting and tracking seatbelts. A seatbelt is an important safety feature in the vehicle that if properly used can save lives. Efficient segmentation of the seatbelts in an image provides important information about the correct use of seatbelts. The challenge in developing deep learning algorithms for seatbelt detection and segmentation is the manual annotations required for this task, which is cumbersome. This paper explores a novel formulation to efficiently train a seatbelt model with minimal supervision. We exploit the textureless and shape characteristics of the seatbelts to programmatically synthesize images. Our proposed method synthetically creates images that resemble seatbelt patterns. After training a model exclusively with synthetic images, we iteratively fine-tune it using naturalistic images extracted from online video-sharing websites. The labels for these images are pseudo-labels assigned by the model to confident predictions. Fine-tuning helps adapt the model to better work on real naturalistic images, improving the performance of the system. We obtain an F1-score of 0.55 in segmenting the seatbelt with this approach. We also experiment with fine-tuning the model with a small number of naturalistic images with annotated labels. After pretraining on synthetic samples and pseudo-labeled naturalistic images, we achieve an F1-score of 0.67 using only 200 annotated images.","PeriodicalId":195148,"journal":{"name":"2023 IEEE Intelligent Vehicles Symposium (IV)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114411189","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