2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)最新文献_第2页

Probabilistic VRU Trajectory Forecasting for Model-Predictive Planning A Case Study: Overtaking Cyclists 基于模型预测规划的概率VRU轨迹预测——以超车为例

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669208

Jan Schneegans, Jan Eilbrecht, Stefan Zernetsch, Maarten Bieshaar, Konrad Doll, O. Stursberg, B. Sick

引用次数: 1

OpenPlanner 2.0: The Portable Open Source Planner for Autonomous Driving Applications OpenPlanner 2.0:用于自动驾驶应用的便携式开源规划器

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669253

H. Darweesh, E. Takeuchi, K. Takeda

{"title":"OpenPlanner 2.0: The Portable Open Source Planner for Autonomous Driving Applications","authors":"H. Darweesh, E. Takeuchi, K. Takeda","doi":"10.1109/ivworkshops54471.2021.9669253","DOIUrl":"https://doi.org/10.1109/ivworkshops54471.2021.9669253","url":null,"abstract":"There are few open source autonomous driving planners that are general enough to be used directly, or which could be easily customized to suit a particular application. OpenPlanner 1.0 was introduced back in 2017 to fill this gap. It was developed and integrated with the open source autonomous driving framework Autoware. Since then, many improvements have been introduced, following the original design goals. In this paper, the basic design will be re-introduced along with the latest developed technologies. The new planner is called OpenPlanner 2.0 and includes several new techniques such as multiple HD road network map formats support, trajectory and behavior estimation, planning based HMI support, path generation using kinematics based motion simulation and lane change behavior. OpenPlanner 2.0 is already attracting attention from the autonomous driving research and development communities; universities and companies. Several projects are using and contributing to its code base. Some of these applications will be discussed in this paper as well. A comparison between OpenPlanner and other open source planners showing the aspects where it is superior is also presented.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"423 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131751974","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

Towards Knowledge-based Road Modeling for Automated Vehicles: Analysis and Concept for Incorporating Prior Knowledge 面向自动驾驶车辆的基于知识的道路建模:融合先验知识的分析与概念

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669220

Jenny Fricke, Christopher Plachetka, B. Rech

{"title":"Towards Knowledge-based Road Modeling for Automated Vehicles: Analysis and Concept for Incorporating Prior Knowledge","authors":"Jenny Fricke, Christopher Plachetka, B. Rech","doi":"10.1109/ivworkshops54471.2021.9669220","DOIUrl":"https://doi.org/10.1109/ivworkshops54471.2021.9669220","url":null,"abstract":"Typically, automated driving functions rely on high-definition maps for modeling the stationary environment (SE). However, outdated or erroneous maps pose a risk to both safety and performance of such a driving function. To address the issue of false map data provided to the vehicle, deviations ahead of the vehicle must be detected and corrected, preferably within the vehicle. To enable the continued operation of the driving function, a SE model as input to the driving function has to be generated on the fly. Moreover, to reduce the probability to encounter deviations in the first place, map update hypotheses have to be provided, e.g., to compute an update in an external server. In this paper, we present a concept for integrating prior knowledge, e.g., regarding rule-compliant lane configurations, into the generation of the SE model. Prior knowledge enables the evaluation of undetected elements, the interpretation of connections between elements, and an overall plausibility check. Last, we provide an example for SE modeling for which we demonstrate the benefit of incorporating prior knowledge. The main novelity of this work is to show a way of deriving and representing required knowledge for SE modeling. Instead of focussing on individual infrastructure entities (e.g., intersection) as typically discussed in related works, we establish our derivation by analyzing traffic regulations and exemplary critical scenarios that arise due to the presence of map deviations.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128503882","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

Trajectory Planning with Comfort and Safety in Dynamic Traffic Scenarios for Autonomous Driving 基于舒适性和安全性的自动驾驶动态交通路径规划

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669202

Jiahui Zhang, Zhiqiang Jian, Jiawei Fu, Zhixiong Nan, J. Xin, N. Zheng

{"title":"Trajectory Planning with Comfort and Safety in Dynamic Traffic Scenarios for Autonomous Driving","authors":"Jiahui Zhang, Zhiqiang Jian, Jiawei Fu, Zhixiong Nan, J. Xin, N. Zheng","doi":"10.1109/ivworkshops54471.2021.9669202","DOIUrl":"https://doi.org/10.1109/ivworkshops54471.2021.9669202","url":null,"abstract":"Trajectory planning is one of the most important modules of the Autonomous Driving Systems (ADSs), which aims to achieve a safe and comfortable interaction between the ADSs and obstacles. Currently, it remains a challenging issue to simultaneously ensure the comfort and safety of the planned trajectory, especially in dynamic traffic scenarios. In this paper, a trajectory planning method is proposed for autonomous vehicles to drive in dynamic traffic scenarios considering both comfort and safety. First, trajectory candidates are generated through the separation of path generation and velocity generation, and then some cost functions are constructed to evaluate each trajectory candidate to obtain the final trajectory. The proposed trajectory generation method guarantees the continuity of generated trajectory in both curvature and jerk, and the cost functions are proposed with the Trajectory Comfort Evaluation Model (TCEM) and Trajectory Safety Evaluation Model (TSEM), which balance the comfort and safety of a trajectory. Experiments prove the effectiveness of the proposed trajectory planner and its robustness in dynamic traffic scenarios.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114137772","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

An adaptive cooperative adaptive cruise control against varying vehicle loads* 一种针对不同车辆负载的自适应协同自适应巡航控制方法*

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669236

Yiming Zhang, Jia Hu, Haoran Wang, Zhizhou Wu

引用次数: 0

Regulating Road Vehicle Teleoperation: Back to the Near Future 规范道路车辆远程操作:回到不久的将来

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669226

P. Linné, Jeanette Andersson

{"title":"Regulating Road Vehicle Teleoperation: Back to the Near Future","authors":"P. Linné, Jeanette Andersson","doi":"10.1109/ivworkshops54471.2021.9669226","DOIUrl":"https://doi.org/10.1109/ivworkshops54471.2021.9669226","url":null,"abstract":"Due to the many remaining obstacles before reliability and safety can sufficiently be guaranteed for high-level automated vehicles (AVs), teleoperation or remote operation of partially automated vehicles by a human driver has become increasingly interesting to consider. However, remote operation, including remote driving, has so far only received little attention in legal scientific and transportation literature. This paper aims to establish some basic legal matters for remote driving by examining its regulatory development in three different jurisdictions. A combination of methods is employed including an examination of literature regarding AVs and their regulation. The main result is that current regulation in the examined jurisdictions intentionally addresses a future with high-level AVs, but to a large extent excludes regulatory details for remote operation. In conclusion, this paper argues that both present and coming regulation for automated vehicles ought to be more near future-oriented and address the concept of remote operation more explicitly. This, for regulation to be better in touch with current technology, for the benefit of a wider acceptance in society, for legal certainty, but also for innovation support and stability for investments in technology.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127586269","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

Improving Object Distance Estimation in Automated Driving Systems Using Camera Images, LiDAR Point Clouds and Hierarchical Clustering 利用相机图像、激光雷达点云和分层聚类改进自动驾驶系统中的目标距离估计

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669206

William C. Tamayo, N. E. Chelbi, D. Gingras, Frédéric Faulconnier

引用次数: 2

Observer design with performance guarantees for vehicle control purposes via the integration of learning-based and LPV approaches 通过基于学习和LPV方法的集成，为车辆控制目的提供性能保证的观测器设计

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669224

Dániel Fényes, T. Hegedüs, B. Németh, P. Gáspár

{"title":"Observer design with performance guarantees for vehicle control purposes via the integration of learning-based and LPV approaches","authors":"Dániel Fényes, T. Hegedüs, B. Németh, P. Gáspár","doi":"10.1109/ivworkshops54471.2021.9669224","DOIUrl":"https://doi.org/10.1109/ivworkshops54471.2021.9669224","url":null,"abstract":"The paper proposes an enhanced observer design method for autonomous vehicles, with which the unmeasurable states in vehicle and chassis motion can be estimated. The novelty of the method is that the learning-based observer and the linear parameter varying (LPV) observer in a joint observer design structure are incorporated, which results in an improved performance level on the estimation error. Nevertheless, the proposed design method is able to guarantee the limitation of the estimation error, even if the error of the learning-based observer under all scenarios cannot be verified. Thus, the proposed method handles the main disadvantage of the learning-based approaches, i.e. the lack of performance guarantees, while their advantages, i.e. the improved observation performance in the operation of the observer are taken. The proposed method is applied on a lateral path following control problem, where the goal of the observer is to provide an accurate lateral velocity signal for the vehicle. The effectiveness of the method is illustrated through simulation examples on high- fidelity vehicle dynamic simulator CarSim.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132707176","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

Identification of Vehicle Dynamics Parameters Using Simulation-based Inference 基于仿真推理的车辆动力学参数辨识

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669252

Ali Boyali, S. Thompson, D. Wong

引用次数: 0

Detection of Collective Anomalies in Images for Automated Driving Using an Earth Mover’s Deviation (EMDEV) Measure 使用推土机偏差(EMDEV)测量自动驾驶图像中的集体异常检测

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669217

Jasmin Breitenstein, Andreas Bär, Daniel Lipinski, T. Fingscheidt

{"title":"Detection of Collective Anomalies in Images for Automated Driving Using an Earth Mover’s Deviation (EMDEV) Measure","authors":"Jasmin Breitenstein, Andreas Bär, Daniel Lipinski, T. Fingscheidt","doi":"10.1109/ivworkshops54471.2021.9669217","DOIUrl":"https://doi.org/10.1109/ivworkshops54471.2021.9669217","url":null,"abstract":"For visual perception in automated driving, a reliable detection of so-called corner cases is important. Corner cases appear in many different forms and can be image frame- or sequence-related. In this work, we consider a specific type of corner case: collective anomalies. These are instances that appear in unusually large amounts in an image. We propose a detection method for collective anomalies based on a comparison of a test (sub-)set instance distribution to a training (i.e., reference) instance distribution, both distributions obtained by an instance-based semantic segmentation. For this comparison, we propose a novel so-called earth mover’s deviation (EMDEV) measure, which is able to provide signed deviations of instance distributions. Further, we propose a sliding window approach to allow the comparison of instance distributions in an online application in the vehicle. With our approach, we are able to identify collective anomalies by the proposed EMDEV measure, and to detect deviations from the instance distribution of the reference dataset.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127488842","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