2017 IEEE Intelligent Vehicles Symposium (IV)最新文献_第2页

Efficient ConvNet for real-time semantic segmentation 用于实时语义分割的高效卷积神经网络

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995966

Eduardo Romera, J. Álvarez, L. Bergasa, R. Arroyo

{"title":"Efficient ConvNet for real-time semantic segmentation","authors":"Eduardo Romera, J. Álvarez, L. Bergasa, R. Arroyo","doi":"10.1109/IVS.2017.7995966","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995966","url":null,"abstract":"Semantic segmentation is a task that covers most of the perception needs of intelligent vehicles in an unified way. ConvNets excel at this task, as they can be trained end-to-end to accurately classify multiple object categories in an image at the pixel level. However, current approaches normally involve complex architectures that are expensive in terms of computational resources and are not feasible for ITS applications. In this paper, we propose a deep architecture that is able to run in real-time while providing accurate semantic segmentation. The core of our ConvNet is a novel layer that uses residual connections and factorized convolutions in order to remain highly efficient while still retaining remarkable performance. Our network is able to run at 83 FPS in a single Titan X, and at more than 7 FPS in a Jetson TX1 (embedded GPU). A comprehensive set of experiments demonstrates that our system, trained from scratch on the challenging Cityscapes dataset, achieves a classification performance that is among the state of the art, while being orders of magnitude faster to compute than other architectures that achieve top precision. This makes our model an ideal approach for scene understanding in intelligent vehicles applications.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"2 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":"122902735","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: 92

Multi-camera traffic light recognition using a classifying Labeled Multi-Bernoulli filter 基于分类标记多伯努利滤波的多摄像头红绿灯识别

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995852

Martin Bach, Stephan Reuter, K. Dietmayer

引用次数: 9

Efficient combination of Lidar intensity and 3D information by DNN for pedestrian recognition with high and low density 3D sensor 基于深度神经网络的激光雷达强度与三维信息的有效结合，用于高低密度三维传感器的行人识别

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995729

L. Mioulet, D. Tsishkou, R. Bendahan, F. Abad

{"title":"Efficient combination of Lidar intensity and 3D information by DNN for pedestrian recognition with high and low density 3D sensor","authors":"L. Mioulet, D. Tsishkou, R. Bendahan, F. Abad","doi":"10.1109/IVS.2017.7995729","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995729","url":null,"abstract":"Pedestrian recognition is one of the key components for assisted and autonomous driving. So far many researchers have investigated systems combining a high density LIDAR with cameras or stereo, which results in an expensive and complex setup where the LIDAR data is mostly used to extract regions of interest for the 2D sensor. Very few work has focused on using pure 3D data coming from the LIDAR to recognize pedestrians, and even less have made an intensive use of the intensity information returned by the LIDAR. The intensity information displays a high frequency change between neighboring points of similar material, this can be due to the angle or distance. Due to this, it has not been frequently investigated as a potentially interesting feature as it would require extensive time consuming feature engineering to be worthwhile. In this paper we present a novel 2D representation of a 3D point cloud including the intensity information. We show the ability of convolutional neural networks to handle this data in order to accurately recognize pedestrians in complex driving scenes. Our system outperformed state of the art technique on the STC database. Additionally we show that this system is still highly accurate on low density LIDAR data.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"28 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":"130191197","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

Modified artificial potential field method for online path planning applications 改进的人工势场法在在线路径规划中的应用

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995717

Farid Bounini, D. Gingras, Herve Pollart, D. Gruyer

引用次数: 106

Efficient L-shape fitting for vehicle detection using laser scanners 高效l形拟合车辆检测使用激光扫描仪

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995698

Xiao Zhang, Wenda Xu, Chiyu Dong, J. Dolan

引用次数: 87

Road-SLAM : Road marking based SLAM with lane-level accuracy Road-SLAM:基于道路标记的SLAM，具有车道级精度

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995958

Jinyong Jeong, Younggun Cho, Ayoung Kim

引用次数: 50

Developing a platoon-wide Eco-Cooperative Adaptive Cruise Control (CACC) system 开发全排生态合作自适应巡航控制(CACC)系统

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995884

Ziran Wang, Guoyuan Wu, Peng Hao, K. Boriboonsomsin, M. Barth

{"title":"Developing a platoon-wide Eco-Cooperative Adaptive Cruise Control (CACC) system","authors":"Ziran Wang, Guoyuan Wu, Peng Hao, K. Boriboonsomsin, M. Barth","doi":"10.1109/IVS.2017.7995884","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995884","url":null,"abstract":"Connected and automated vehicle (CAV) technology has become increasingly popular. As an example, Cooperative Adaptive Cruise Control (CACC) systems are of high interest, allowing CAVs to communicate and cooperate with each other to form platoons, where one vehicle follows another with a predefined spacing or time gap. Although numerous studies have been conducted on CACC systems, very few have examined the protocols from the perspective of environmental sustainability, not to mention from a platoon-wide consideration. In this study, we propose a vehicle-to-vehicle (V2V) communication based Eco-CACC system, aiming to minimize the platoon-wide energy consumption and pollutant emissions at different stages of the CACC operation. A full spectrum of environmentally-friendly CACC maneuvers are explored and the associated protocols are developed, including sequence determination, gap closing and opening, platoon cruising with gap regulation, and platoon joining and splitting. Simulation studies of different scenarios are conducted using MATLAB/Simulink. Compared to an existing CACC system, the proposed one can achieve additional 2% energy savings and additional 17% pollutant emissions reductions during the platoon joining scenario.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"1 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":"129111750","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: 61

Parallel vehicles based on the ACP theory: Safe trips via self-driving 基于ACP理论的平行车辆:自动驾驶安全行驶

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995693

Shuangshuang Han, Feiyue Wang, Yingchun Wang, Dongpu Cao, Li Li

引用次数: 8

GM-PHD filter for multiple extended object tracking based on the multiplicative error shape model and network flow labeling 基于乘法误差形状模型和网络流标记的多扩展目标跟踪GM-PHD滤波器

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995691

Florian Teich, Shishan Yang, M. Baum

引用次数: 3

A machine learning based biased-sampling approach for planning safe trajectories in complex, dynamic traffic-scenarios 一种基于机器学习的偏采样方法，用于规划复杂动态交通场景中的安全轨迹

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995735

Amit Chaulwar, M. Botsch, W. Utschick

{"title":"A machine learning based biased-sampling approach for planning safe trajectories in complex, dynamic traffic-scenarios","authors":"Amit Chaulwar, M. Botsch, W. Utschick","doi":"10.1109/IVS.2017.7995735","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995735","url":null,"abstract":"Many variants of the Rapidly-exploring Random Tree (RRT) algorithm use biased-sampling strategies for solving computationally intensive tasks. One of such tasks is the planning of safe trajectories with the simultaneous intervention in both the longitudinal and the lateral dynamics of the vehicle in complex traffic-scenarios with multiple static and dynamic objects. A recently proposed hybrid statistical learning approach uses a 3D convolutional neural network (3D-ConvNet) to predict suitable longitudinal acceleration profiles in combination with an RRT variant called the Augmented CL-RRT algorithm. This algorithm is not effective in complex traffic-scenarios, i.e., traffic scenarios with more than 4 dynamic objects, because of the lack of flexibility and biasing in the longitudinal and the lateral dynamics intervention, respectively. Therefore, an extension to the Augmented CL-RRT algorithm is introduced to improve the longitudinal dynamics intervention with actuator and stable profile constraints and named as the Augmented CL-RRT+ algorithm. A biased-sampling strategy is also proposed based on the predicted longitudinal acceleration and steering wheel angle profiles provided by a trained 3D-ConvNet. Simulations are performed to compare different trajectory planning algorithms based on efficiency and safety. The results show vast improvements in terms of the efficiency without harming the safety.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"33 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":"121595514","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: 9