2022 IEEE Intelligent Vehicles Symposium (IV)最新文献_第9页

The exiD Dataset: A Real-World Trajectory Dataset of Highly Interactive Highway Scenarios in Germany exiD数据集:德国高速公路高度交互场景的真实轨迹数据集

2022 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2022-06-05 DOI: 10.1109/iv51971.2022.9827305

Tobias Moers, Lennart Vater, R. Krajewski, Julian Bock, A. Zlocki, L. Eckstein

{"title":"The exiD Dataset: A Real-World Trajectory Dataset of Highly Interactive Highway Scenarios in Germany","authors":"Tobias Moers, Lennart Vater, R. Krajewski, Julian Bock, A. Zlocki, L. Eckstein","doi":"10.1109/iv51971.2022.9827305","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827305","url":null,"abstract":"Development and safety validation of highly automated vehicles increasingly relies on data and data-driven methods. In processing sensor datasets for environment perception, it is common to use public and commercial datasets for training and evaluating machine learning based systems. For system-level evaluation and safety validation of an automated driving system, real-world trajectory datasets are of great value for several tasks in the process, i.a. for testing in simulation, scenario extraction or training of road user agent models. Ground-based recording methods such as sensor-equipped vehicles or infrastructure sensors are sometimes limited, for instance, due to their field of view. Camera-equipped drones, however, offer the ability to record road users without vehicle-to-vehicle occlusion and without influencing traffic. The highway drone dataset (highD) has shown that the recording method is efficient in terms of cumulative kilometers and has become a benchmark dataset for many research questions. It contains many vehicle interactions due to dense traffic, but lacks merging scenarios, which are challenging for highly automated vehicles. Therefore, we propose this highway drone dataset called exiD, recorded using camera-equipped drones at entries and exits on the German Autobahn. The dataset contains 69 172 road users classified as car, truck and vans and a total amount of more than 16 hours of measurement data. For non-commercial public research, the exiD dataset is available free of charge at https://www.exid-dataset.com.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130883785","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: 22

Vision Transformer for Learning Driving Policies in Complex and Dynamic Environments 在复杂和动态环境中学习驾驶策略的视觉转换器

2022 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2022-06-05 DOI: 10.1109/iv51971.2022.9827348

E. Kargar, V. Kyrki

引用次数: 2

Optimization-Based Coordination of Mixed Traffic at Unsignalized Intersections Based on Platooning Strategy 基于队列策略的无信号交叉口混合交通优化协调

2022 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2022-06-05 DOI: 10.1109/iv51971.2022.9827149

Muhammad Faris, P. Falcone, J. Sjöberg

引用次数: 5

Users’ Preferences for the Communication with Autonomous Micro-Vehicles 用户与自动驾驶微型车辆的通信偏好

2022 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2022-06-05 DOI: 10.1109/iv51971.2022.9827459

Vivian Lotz, Eva-Maria Schomakers, M. Ziefle

引用次数: 0

Scene Spatio-Temporal Graph Convolutional Network for Pedestrian Intention Estimation 行人意图估计的场景时空图卷积网络

2022 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2022-06-05 DOI: 10.1109/iv51971.2022.9827231

Abhilash Y. Naik, Ariyan Bighashdel, P. Jancura, Gijs Dubbelman

{"title":"Scene Spatio-Temporal Graph Convolutional Network for Pedestrian Intention Estimation","authors":"Abhilash Y. Naik, Ariyan Bighashdel, P. Jancura, Gijs Dubbelman","doi":"10.1109/iv51971.2022.9827231","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827231","url":null,"abstract":"For safe and comfortable navigation of autonomous vehicles, it is crucial to know the pedestrian’s intention of crossing the street. Generally, human drivers are aware of the traffic objects (e.g., crosswalks and traffic lights) in the environment while driving; likewise, these objects would play a crucial role for autonomous vehicles. In this research, we propose a novel pedestrian intention estimation method that not only takes into account the influence of traffic objects but also learns their contribution levels on the intention of the pedestrian. Our proposed method, referred to as Scene SpatioTemporal Graph Convolutional Network (Scene-STGCN), takes benefits from the strength of Graph Convolutional Networks and efficiently encodes the relationships between the pedestrian and the scene objects both spatially and temporally. We conduct several experiments on the Pedestrian Intention Estimation (PIE) dataset and illustrate the importance of scene objects and their contribution levels in the task of pedestrian intention estimation. Furthermore, we perform statistical analysis on the relevance of different traffic objects in the PIE dataset and carry out an ablation study on the effect of various information sources in the scene. Finally, we demonstrate the significance of the proposed Scene-STGCN through experimental comparisons with several baselines. The results indicate that our proposed Scene-STGCN outperforms the current state-of-the-art method by 0.03 in terms of ROC-AUC metric.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114215565","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: 4

Gaussian Process based Model Predictive Control for Overtaking Scenarios at Highway Curves 基于高斯过程的公路弯道超车模型预测控制

2022 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2022-06-05 DOI: 10.1109/iv51971.2022.9827233

Wenjun Liu, Yulin Zhai, Guang Chen, Alois Knoll

引用次数: 3

Optimization-based Resource Allocation for an Automotive Service-oriented Software Architecture 基于优化的汽车面向服务软件体系结构资源分配

2022 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2022-06-05 DOI: 10.1109/iv51971.2022.9827429

Alexandru Kampmann, Maximilian Lüer, S. Kowalewski, Bassam Alrifaee

引用次数: 1

A mobile application for resolving bicyclist and automated vehicle interactions at intersections* 解决自行车和自动车辆交互在十字路口的移动应用程序*

2022 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2022-06-05 DOI: 10.1109/iv51971.2022.9827439

J. Lindner, G. Grigoropoulos, A. Keler, Patrick Malcolm, Florian Denk, Pascal Brunner, K. Bogenberger

引用次数: 3

MPC-based Eco-Platooning for Homogeneous Connected Trucks Under Different Communication Topologies 不同通信拓扑下基于mpc的同构互联卡车生态队列

2022 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2022-06-05 DOI: 10.1109/iv51971.2022.9827236

Hao Long, Arash Khalatbarisoltani, Xiaosong Hu

{"title":"MPC-based Eco-Platooning for Homogeneous Connected Trucks Under Different Communication Topologies","authors":"Hao Long, Arash Khalatbarisoltani, Xiaosong Hu","doi":"10.1109/iv51971.2022.9827236","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827236","url":null,"abstract":"Advances in connected automated technology allow for more efficient driving in heavy-duty transportation. By well coordinating the longitudinal movements of multiple vehicles driving in a string, eco-platooning control can significantly improve the driving comfort and fuel economy. Moreover, benefitting from the short following distances of the platoon members, the aerodynamic effects are believed to further reduce the overall energy consumption in heavy-duty applications. In this paper, we develop an aerodynamically aware cooperative adaptive cruise control (CACC) strategy based on nonlinear model predictive control (NMPC). The proposed strategy is implemented under different communication topologies: 1) predecessor following (PF), 2) leader following (LF), and 3) predecessor-leader following (PLF). The performance of three communication topologies is evaluated through several indexes, and the simulation results indicate that when the information of the platoon leader is broadcast to the other platoon members, resulting in a so-called LF or PLF topology, the string stability would be guaranteed, and the proposed strategy can improve the driving comfort of all three trucks by eliminating unnecessary accelerations. On the other hand, a remarkable decrement on demanded power can be derived due to the effect of air-drag reduction.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133736431","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: 4

Evaluation of Vehicle Assignment Algorithms for Autonomous Mobility on Demand 基于需求的自主移动车辆分配算法评价

2022 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2022-06-05 DOI: 10.1109/iv51971.2022.9827456

Sadullah Goncu, Mehmet Ali Silgu, H. B. Çelikoglu

{"title":"Evaluation of Vehicle Assignment Algorithms for Autonomous Mobility on Demand","authors":"Sadullah Goncu, Mehmet Ali Silgu, H. B. Çelikoglu","doi":"10.1109/iv51971.2022.9827456","DOIUrl":"https://doi.org/10.1109/iv51971.2022.9827456","url":null,"abstract":"The term “Mobility” is gaining new perspectives. Due to the paradigm shift driven by information technologies and autonomous vehicles, on-demand mobility services have experienced significant growth. Operating such a service efficiently is a challenging task. Especially, assigning vehicles to customers plays a vital role in this regard. To meet a satisfactory level of service while keeping the operational costs to a minimum requires efficient assignment strategies. Work summarized in this paper utilizes several shared and non-shared assignment algorithms in order to propose a methodology to assess the effects on the overall system performance for an Autonomous Mobility on Demand system. Selected algorithms are tested in a theoretical network with real-world taxi data with the help of microscopic traffic simulation software Simulation of Urban Mobility. Simulation scenarios are generated for both varying demand levels and increasing fleet sizes. Results suggest that for high demand levels and small fleet sizes, shared algorithms outperform non-shared algorithms for every performance measure chosen: total vehicle kilometers traveled, the ratio of empty fleet kilometers, average passenger waiting time for pick up, and the number of customers served in a period.","PeriodicalId":184622,"journal":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133917185","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