2017 IEEE International Conference on Vehicular Electronics and Safety (ICVES)最新文献

Safety assessment of automated vehicle functions by simulation-based fault injection 基于仿真的故障注入自动驾驶车辆功能安全评估

2017 IEEE International Conference on Vehicular Electronics and Safety (ICVES) Pub Date : 2017-07-27 DOI: 10.1109/ICVES.2017.7991928

Garazi Juez, Estibaliz Amparan Calonge, Ray Lattarulo, Joshué Pérez, Alejandra Ruiz López, H. Espinoza

引用次数: 7

Towards a parameter tuning approach for a map-matching algorithm 一种映射匹配算法的参数调整方法

2017 IEEE International Conference on Vehicular Electronics and Safety (ICVES) Pub Date : 2017-07-25 DOI: 10.1109/ICVES.2017.7991906

Carola A. Blazquez, Jana Ries, P. Miranda

引用次数: 8

Deterministic path planning for non-holonomic vehicles including friction and steer rate limitations 包含摩擦和转向率限制的非完整车辆的确定性路径规划

2017 IEEE International Conference on Vehicular Electronics and Safety (ICVES) Pub Date : 2017-06-27 DOI: 10.1109/ICVES.2017.7991892

Mahdi Morsali, F. Mohseni, E. Frisk

{"title":"Deterministic path planning for non-holonomic vehicles including friction and steer rate limitations","authors":"Mahdi Morsali, F. Mohseni, E. Frisk","doi":"10.1109/ICVES.2017.7991892","DOIUrl":"https://doi.org/10.1109/ICVES.2017.7991892","url":null,"abstract":"Path planning algorithms have evolved during decades to become computationally less expensive and optimal. In this paper a deterministic approach is used to find a path near to the shortest path using motion primitives. The motion primitives are constructed using a non-holonomic vehicle model. The physical model enables the algorithm to use a friction map and calculate paths with lower lateral slip forces. Furthermore the algorithm takes into account the steer rate using steer angles assigned for motion primitives. The algorithm is an A∗ based search method along with a heuristic to find a near optimal solution. The performance and calculation time of the algorithm is tunable by adjusting motion primitive size and discretization steps. In order to compare the algorithm output to optimal solution a direct multiple shooting method is used. The algorithm is simulated in different scenarios that shows the properties of the algorithm. The results attained from search method is compared with optimal solution in two different test scenarios and the comparison shows consistency of search method to the optimal solution.","PeriodicalId":303389,"journal":{"name":"2017 IEEE International Conference on Vehicular Electronics and Safety (ICVES)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114735477","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

Parametric trajectory prediction of surrounding vehicles 周边车辆参数轨迹预测

2017 IEEE International Conference on Vehicular Electronics and Safety (ICVES) Pub Date : 2017-06-27 DOI: 10.1109/ICVES.2017.7991896

C. Kang, S. Jeon, Seung-Hi Lee, C. Chung

引用次数: 5

Pedestrian recognition through different cross-modality deep learning methods 行人识别通过不同的跨模态深度学习方法

2017 IEEE International Conference on Vehicular Electronics and Safety (ICVES) Pub Date : 2017-06-27 DOI: 10.1109/ICVES.2017.7991914

D. Pop, A. Rogozan, F. Nashashibi, A. Bensrhair

{"title":"Pedestrian recognition through different cross-modality deep learning methods","authors":"D. Pop, A. Rogozan, F. Nashashibi, A. Bensrhair","doi":"10.1109/ICVES.2017.7991914","DOIUrl":"https://doi.org/10.1109/ICVES.2017.7991914","url":null,"abstract":"A wide variety of approaches have been proposed for pedestrian detection in the last decay and it still remains an open challenge due to its outstanding importance in the field of automotive. In recent years, deep learning classification methods, in particular convolutional neural networks, combined with multi-modality images applied on different fusion schemes have achieved great performances in computer vision tasks. For the pedestrian recognition task, the late-fusion scheme outperforms the early and intermediate integration of modalities. In this paper, we focus on improving and optimizing the late-fusion scheme for pedestrian classification on the Daimler stereo vision data set. We propose different training methods based on Cross-Modality deep learning of Convolutional Neural Networks (CNNs): (1) a correlated model, (2) an incremental model and, (3) a particular cross-modality model, where each CNN is trained on one modality, but tested on a different one. The experiments show that the incremental cross-modality deep learning of CNNs achieves the best performances. It improves the classification performances not only for each modality classifier, but also for the multi-modality late-fusion scheme. The particular cross-modality model is a promising idea for automated annotation of modality images with a classifier trained on a different modality and/or for cross-dataset training.","PeriodicalId":303389,"journal":{"name":"2017 IEEE International Conference on Vehicular Electronics and Safety (ICVES)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125254285","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

A virtual vehicle based coordination framework for Autonomous Vehicles in heterogeneous scenarios 基于虚拟车辆的异构场景自动驾驶车辆协调框架

2017 IEEE International Conference on Vehicular Electronics and Safety (ICVES) Pub Date : 2017-06-27 DOI: 10.1109/ICVES.2017.7991900

Ezequiel Debada, L. Makarem, D. Gillet

引用次数: 9

Fast stochastic non-linear model predictive control for electric vehicle advanced driver assistance systems 电动汽车高级驾驶辅助系统的快速随机非线性模型预测控制

2017 IEEE International Conference on Vehicular Electronics and Safety (ICVES) Pub Date : 2017-06-27 DOI: 10.1109/ICVES.2017.7991907

Seyed Amin Sajadi-Alamdari, H. Voos, M. Darouach

引用次数: 2

Analyzing the effects of V2V and ADAS-ACC penetration rates on the level of road safety in intersections: Evaluating simulation platforms SUMO and scene suite 分析V2V和ADAS-ACC渗透率对交叉口道路安全水平的影响:评估模拟平台SUMO和场景套件

2017 IEEE International Conference on Vehicular Electronics and Safety (ICVES) Pub Date : 2017-06-27 DOI: 10.1109/ICVES.2017.7991898

Aso Validi, T. Ludwig, C. Olaverri-Monreal

引用次数: 6

Motion planning via optimization of risk quantified by collision velocity accompanied with AEB activation 通过碰撞速度量化风险优化的运动规划，同时激活AEB

2017 IEEE International Conference on Vehicular Electronics and Safety (ICVES) Pub Date : 2017-06-27 DOI: 10.1109/ICVES.2017.7991895

Tsukasa Shimizu, P. Raksincharoensak

引用次数: 10

Real-time multiplexing of mixed-criticality data streams for Automotive Multi-Core Test Systems 汽车多核测试系统混合临界数据流的实时多路复用

2017 IEEE International Conference on Vehicular Electronics and Safety (ICVES) Pub Date : 2017-06-27 DOI: 10.1109/ICVES.2017.7991929

Markus Nager, M. Baunach, P. Priller, J. Wurzinger

引用次数: 5