2017 IEEE Intelligent Vehicles Symposium (IV)最新文献_第6页

Automatic extrinsic calibration methods for Surround View Systems 环视系统的外部自动标定方法

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995702

K. Natroshvili, Kay-Ulrich Scholl

引用次数: 8

Spatially-partitioned environmental representation and planning architecture for on-road autonomous driving 道路自动驾驶的空间分块环境表示与规划架构

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995789

W. Zhan, Jianyu Chen, Ching-yao Chan, Changliu Liu, M. Tomizuka

{"title":"Spatially-partitioned environmental representation and planning architecture for on-road autonomous driving","authors":"W. Zhan, Jianyu Chen, Ching-yao Chan, Changliu Liu, M. Tomizuka","doi":"10.1109/IVS.2017.7995789","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995789","url":null,"abstract":"Conventional layered planning architecture temporally partitions the spatiotemporal motion planning by the path and speed, which is not suitable for lane change and overtaking scenarios with moving obstacles. In this paper, we propose to spatially partition the motion planning by longitudinal and lateral motions along the rough reference path in the Frenét Frame, which makes it possible to create linearized safety constraints for each layer in a variety of on-road driving scenarios. A generic environmental representation methodology is proposed with three topological elements and corresponding longitudinal constraints to compose all driving scenarios mentioned in this paper according to the overlap between the potential path of the autonomous vehicle and predicted path of other road users. Planners combining A∗ search and quadratic programming (QP) are designed to plan both rough long-term longitudinal motions and short-term trajectories to exploit the advantages of both search-based and optimization-based methods. Limits of vehicle kinematics and dynamics are considered in the planners to handle extreme cases. Simulation results show that the proposed framework can plan collision-free motions with high driving quality under complicated scenarios and emergency situations.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133456734","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: 49

SPOT: A tool for set-based prediction of traffic participants SPOT:一个基于集的交通参与者预测工具

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995951

Markus Koschi, M. Althoff

{"title":"SPOT: A tool for set-based prediction of traffic participants","authors":"Markus Koschi, M. Althoff","doi":"10.1109/IVS.2017.7995951","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995951","url":null,"abstract":"Predicting the movement of other traffic participants is an integral part in the motion planning of most automated road vehicles. While simple predictions, e.g. based on assuming constant velocity, may suffice for deciding a driving strategy, predicting the set of all possible behaviors is required to ensure safe motion plans. In this work, we propose a novel tool for the latter problem based on reachability analysis: Set-Based Prediction Of Traffic Participants (SPOT). Our tool can predict the future occupancy of other traffic participants, including all possible maneuvers (e.g. full acceleration, full braking, and arbitrary lane changes), by considering physical constraints and assuming that the traffic participants abide by the traffic rules. However, we remove assumptions for each traffic participant individually as soon as a violation of a traffic rule is detected. Removal of assumptions automatically results in larger occupancies and thus a smaller drivable area for the ego vehicle, ensuring that the ego vehicle does not cause a collision during the time horizon of the prediction. Experimental results show that we obtain the set of future occupancies within a fraction of the prediction horizon. Our tool is available at spot.in.tum.de.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131554294","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: 68

A platoon-based intersection management system for autonomous vehicles 基于队列的自动驾驶车辆交叉口管理系统

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995794

M. Bashiri, C. Fleming

{"title":"A platoon-based intersection management system for autonomous vehicles","authors":"M. Bashiri, C. Fleming","doi":"10.1109/IVS.2017.7995794","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995794","url":null,"abstract":"Recent advancements in Intelligent Transportation Systems suggest that the roads will gradually be filled with autonomous vehicles that are able to drive themselves while communicating with each other and the infrastructure. Autonomous intersection management is among the more challenging traffic scenarios, which involves coordinating the movement of autonomous vehicles through a conflict zone. Intersection management is also potentially one of the more beneficial traffic scenarios in terms of mobility and environmental impact. In this paper we propose a platoon-based approach for the cooperative intersection management problem. We assert that leveraging the platooning capability of autonomous vehicles could improve the efficiency of any policy at an intersection, in terms of average delay time per vehicle and can reduce the communication overhead in the vicinity of intersections by a factor of up to the average platoon size. We also develop a new autonomous intersection management method that guarantees the safety of traffic by allowing one platoon in the conflict zone at any time. We examine the effects of platooning on a simple stop sign at a single 4-way intersection in a simulated environment and report the results in terms of average delay per vehicle and communication overhead. Moreover, we evaluate the performance of the proposed method in a simulated environment and compare the results in terms of average wait time per vehicle and variance in delay with that of a stop sign.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"286 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131680585","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: 48

Driver-automation indirect shared control of highly automated vehicles with intention-aware authority transition 具有意向感知权限转换的高度自动驾驶汽车驾驶员-自动化间接共享控制

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995694

Renjie Li, Yanan Li, S. Li, E. Burdet, B. Cheng

引用次数: 40

Increasing anthropomorphism and trust in automated driving functions by adding speech output 通过增加语音输出，增加对自动驾驶功能的拟人化和信任

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995746

Yannick Forster, Frederik Naujoks, A. Neukum

{"title":"Increasing anthropomorphism and trust in automated driving functions by adding speech output","authors":"Yannick Forster, Frederik Naujoks, A. Neukum","doi":"10.1109/IVS.2017.7995746","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995746","url":null,"abstract":"Conditionally Automated Driving (CAD) functions need to be carefully examined regarding related driver attitudes such as trust and usability to increase their acceptance among future system users. By adding speech output to an existing audio-visual Human-Machine Interface (HMI), the level of trust in automation was suspected to be increased due to semantic information and the application of anthropomorphic features such as voice and gender. To test this assumption, N = 17 drivers completed two simulator drives, once with ('Speech + generic') and once without additional speech output (‘Generic’). Having interacted with the automated system in different scenarios (i.e., letting the system execute a maneuver vs. taking over control from the vehicle), drivers completed comparative questionnaires on trust, anthropomorphism and usability. The applied questionnaire on trust in automation was structured after theoretical implications and derived from previous research on trust in automation. Results showed that the ‘Speech + generic’ system was rated as superior for all three attitude measures compared to the ‘Generic’ system. Furthermore, the present study describes a first approach on comprehensively examining trust in vehicle automation. We brought forth evidence that speech output is highly relevant in order to improve driver attitudes that affect acceptance of automated systems.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115301384","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: 63

Self-adapting part-based pedestrian detection using a fish-eye camera 基于自适应部件的鱼眼摄像机行人检测

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995695

Yeqiang Qian, Ming Yang, Chunxiang Wang, Bing Wang

引用次数: 14

Sensor-based road model estimation for autonomous driving 基于传感器的自动驾驶道路模型估计

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995962

Julian Thomas, R. Rojas

{"title":"Sensor-based road model estimation for autonomous driving","authors":"Julian Thomas, R. Rojas","doi":"10.1109/IVS.2017.7995962","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995962","url":null,"abstract":"In the course of the development and integration of the autonomous driving the knowledge about the current environment and especially the road is one of the basic requirements to fulfill the automated driving task. This information is often extracted from a precise map provided in the vehicle. Therefore the road course and the individual lanes are known in advance by using the map and a suitable ego position estimation. However, in some situations such a map may be invalid and therefore unusable. This can be a driving area which was never mapped or regions where the map is outdated because the environment has changed. The following paper addresses the problem of building a road model without using a map by only fusing measurements from different sensors mounted on the ego vehicle. As sensor measurements various information like lane markings painted on the ground, the position of other cars or occupancy grids can be used. They are transformed into a grid-based model and a geometrical description is extracted out of this model by the use of a novel path-planning based method. The proposed approach was tested with a vehicle equipped with sensors and real measurement data from German highways.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115095043","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: 5

Improving vehicle localization using semantic and pole-like landmarks 使用语义和杆状地标改进车辆定位

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995692

M. Sefati, Magnus Daum, Bjoern Sondermann, K. Kreisköther, A. Kampker

引用次数: 69

Domain-specific data augmentation for on-road object detection based on a deep neural network 基于深度神经网络的道路物体检测领域特定数据增强

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995705

Huien Kim, Youngwan Lee, Hakil Kim, X. Cui

{"title":"Domain-specific data augmentation for on-road object detection based on a deep neural network","authors":"Huien Kim, Youngwan Lee, Hakil Kim, X. Cui","doi":"10.1109/IVS.2017.7995705","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995705","url":null,"abstract":"This paper proposes a data augmentation strategy for improving on-road object detection based on a deep neural network. The method uses a single camera and detects objects based on an optimized deep neural network for a driving environment. The strategy also uses a single-shot multi-box detector (SSD) for object detection, which is a state-of-the-art deep-learning algorithm. The performance is improved by using data augmentation for an advanced driver assist system (ADAS) specific to on-road object recognition. The problem of object detection is first analyzed based on a deep neural network in the ADAS domain, and then representative object detection methods that use deep neural networks are surveyed. A restricted random crop process is suggested for detecting small objects in an image, and then a patch resampling strategy is proposed for solving the long tail property in an on-road dataset. The proposed ADAS domain-specific data augmentation method is adjusted for the original object detection method based on a deep neural network. The object detection results were evaluated using an embedded board on the KITTI benchmark dataset, and the suggested data augmentation method improves the average precision by 30%.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115443366","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: 5