Survey on Artificial Intelligence for Vehicles

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[2]  Xuelong Li,et al.  Learning Multilayer Channel Features for Pedestrian Detection , 2016, IEEE Transactions on Image Processing.

[3]  Bin Fan,et al.  Convolutional Neural Networks with Neural Cascade Classifier for Pedestrian Detection , 2016, CCPR.

[4]  Kyunghyun Cho,et al.  Query-Efficient Imitation Learning for End-to-End Autonomous Driving , 2016, ArXiv.

[5]  Hongliang Guo,et al.  Hierarchical Interactive Learning for a HUman-Powered Augmentation Lower EXoskeleton , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[6]  L. F. Abbott,et al.  Building functional networks of spiking model neurons , 2016, Nature Neuroscience.

[7]  Christos G. Cassandras,et al.  Decentralized Optimal Control for Connected and Automated Vehicles at an Intersection , 2016 .

[8]  Antonella Molinaro,et al.  Information-centric networking for connected vehicles: a survey and future perspectives , 2016, IEEE Communications Magazine.

[9]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[10]  Hong Cheng,et al.  Interactive learning for sensitivity factors of a human-powered augmentation lower exoskeleton , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[11]  Joshua B. Tenenbaum,et al.  Human-level concept learning through probabilistic program induction , 2015, Science.

[12]  Shashi Shekhar,et al.  Future connected vehicles: challenges and opportunities for spatio-temporal computing , 2015, SIGSPATIAL/GIS.

[13]  Pierluigi Pisu,et al.  Online Optimal Control of Connected Vehicles for Efficient Traffic Flow at Merging Roads , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[14]  Alois Knoll,et al.  Stochastic model predictive controller with chance constraints for comfortable and safe driving behavior of autonomous vehicles , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

[15]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[16]  Jianxiong Xiao,et al.  DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[17]  Bin Yang,et al.  Convolutional Channel Features , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[18]  N. M. Wagdarikar,et al.  A review: Control area network (CAN) based Intelligent vehicle system for driver assistance using advanced RISC machines (ARM) , 2015, 2015 International Conference on Pervasive Computing (ICPC).

[19]  Youmin Zhang,et al.  A Distributed Deployment Strategy for a Network of Cooperative Autonomous Vehicles , 2015, IEEE Transactions on Control Systems Technology.

[20]  Walter Lucia,et al.  The obstacle avoidance motion planning problem for autonomous vehicles: A low-demanding receding horizon control scheme , 2015, Syst. Control. Lett..

[21]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[22]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[23]  Dimil Jose,et al.  Intelligent Vehicle Monitoring Using Global Positioning System and Cloud Computing , 2015 .

[24]  Mauro Speranza Neto,et al.  Artificial Intelligence based semi-autonomous control system for military vehicle , 2015 .

[25]  Hossam S. Hassanein,et al.  Integrated Cooperative Localization for Connected Vehicles in Urban Canyons , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).

[26]  Denis Wolf,et al.  Road marking detection using LIDAR reflective intensity data and its application to vehicle localization , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[27]  Michael Sivak,et al.  A survey of public opinion about connected vehicles in the U.S., the U.K., and Australia , 2014, 2014 International Conference on Connected Vehicles and Expo (ICCVE).

[28]  C. Lawrence Zitnick,et al.  Edge Boxes: Locating Object Proposals from Edges , 2014, ECCV.

[29]  Pietro Perona,et al.  Fast Feature Pyramids for Object Detection , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Philip H. S. Torr,et al.  BING: Binarized normed gradients for objectness estimation at 300fps , 2014, Computational Visual Media.

[31]  Xuemin Shen,et al.  Connected Vehicles: Solutions and Challenges , 2014, IEEE Internet of Things Journal.

[32]  Martin Lauer,et al.  3D Traffic Scene Understanding From Movable Platforms , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Mario Gerla,et al.  Vehicular cloud networking: architecture and design principles , 2014, IEEE Communications Magazine.

[34]  R. Service.  The brain chip. , 2014, Science.

[35]  Andreas Geiger,et al.  Vision meets robotics: The KITTI dataset , 2013, Int. J. Robotics Res..

[36]  Koen E. A. van de Sande,et al.  Selective Search for Object Recognition , 2013, International Journal of Computer Vision.

[37]  Jürgen Schmidhuber,et al.  Evolving large-scale neural networks for vision-based reinforcement learning , 2013, GECCO '13.

[38]  Francesco Borrelli,et al.  Robust Predictive Control for semi-autonomous vehicles with an uncertain driver model , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[39]  James M. Rehg,et al.  Decoupling behavior, perception, and control for autonomous learning of affordances , 2013, 2013 IEEE International Conference on Robotics and Automation.

[40]  Luc Van Gool,et al.  Fast Stixel Computation for Fast Pedestrian Detection , 2012, ECCV Workshops.

[41]  I. Kaminer,et al.  Time-Critical Cooperative Control of Multiple Autonomous Vehicles: Robust Distributed Strategies for Path-Following Control and Time-Coordination over Dynamic Communications Networks , 2012, IEEE Control Systems.

[42]  Cristian Sminchisescu,et al.  CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[43]  Luc Van Gool,et al.  Pedestrian detection at 100 frames per second , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[44]  Byungkyu Brian Park,et al.  Development and Evaluation of a Cooperative Vehicle Intersection Control Algorithm Under the Connected Vehicles Environment , 2012, IEEE Transactions on Intelligent Transportation Systems.

[45]  Miad Faezipour,et al.  Progress and challenges in intelligent vehicle area networks , 2012, Commun. ACM.

[46]  Thomas Deselaers,et al.  Measuring the Objectness of Image Windows , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[47]  George Dimitrakopoulos,et al.  Intelligent transportation systems based on internet-connected vehicles: Fundamental research areas and challenges , 2011, 2011 11th International Conference on ITS Telecommunications.

[48]  Byung-Gon Chun,et al.  CloneCloud: elastic execution between mobile device and cloud , 2011, EuroSys '11.

[49]  Hong Cheng,et al.  Multi-support-region image descriptors and its application to street landmark localization , 2011, Machine Vision and Applications.

[50]  Emre Ugur,et al.  Traversability: A Case Study for Learning and Perceiving Affordances in Robots , 2010, Adapt. Behav..

[51]  Stylianos Papanastasiou,et al.  Artificial Intelligence Rationale for Autonomous Vehicle Agents Behaviour in Driving Simulation Environment , 2008 .

[52]  Mohamed Aly,et al.  Real time detection of lane markers in urban streets , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[53]  Nanning Zheng,et al.  Enhancing a Driver's Situation Awareness using a Global View Map , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[54]  Nanning Zheng,et al.  Interactive Road Situation Analysis for Driver Assistance and Safety Warning Systems: Framework and Algorithms , 2007, IEEE Transactions on Intelligent Transportation Systems.

[55]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[56]  Andrew G. Barto,et al.  Elevator Group Control Using Multiple Reinforcement Learning Agents , 1998, Machine Learning.

[57]  Michael P. Wellman,et al.  Nash Q-Learning for General-Sum Stochastic Games , 2003, J. Mach. Learn. Res..

[58]  William T. B. Uther,et al.  Adversarial Reinforcement Learning , 2003 .

[59]  Manuela Veloso,et al.  Scalable Learning in Stochastic Games , 2002 .

[60]  Dick de Waard,et al.  Behavioural impacts of Advanced Driver Assistance Systems–an overview , 2019, European Journal of Transport and Infrastructure Research.

[61]  Sandip Sen,et al.  Learning in multiagent systems , 1999 .

[62]  Craig Boutilier,et al.  The Dynamics of Reinforcement Learning in Cooperative Multiagent Systems , 1998, AAAI/IAAI.

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[64]  Gerald Tesauro,et al.  TD-Gammon, a Self-Teaching Backgammon Program, Achieves Master-Level Play , 1994, Neural Computation.

[65]  Dean A. Pomerleau,et al.  Neural Network Perception for Mobile Robot Guidance , 1993 .

[66]  Dean Pomerleau,et al.  ALVINN, an autonomous land vehicle in a neural network , 2015 .

[67]  S. Ullman.  Against direct perception , 1980, Behavioral and Brain Sciences.

[68]  J. Gibson.  The Ecological Approach to Visual Perception , 1979 .