A Survey of Autonomous Driving: Common Practices and Emerging Technologies

Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art is improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions including localization, mapping, perception, planning, and human machine interfaces, were thoroughly reviewed. Furthermore, many state-of-the-art algorithms were implemented and compared on our own platform in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development.

[1]  Ayoub Al-Hamadi,et al.  Stereo-Camera-Based Urban Environment Perception Using Occupancy Grid and Object Tracking , 2012, IEEE Transactions on Intelligent Transportation Systems.

[2]  John K. Tsotsos,et al.  50 Years of object recognition: Directions forward , 2013, Comput. Vis. Image Underst..

[3]  Iyad Rahwan,et al.  The social dilemma of autonomous vehicles , 2015, Science.

[4]  Iyad Rahwan,et al.  Autonomous Vehicles Need Experimental Ethics: Are We Ready for Utilitarian Cars? , 2015, ArXiv.

[5]  Pan Zhao,et al.  A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving , 2017 .

[6]  Ruigang Yang,et al.  The ApolloScape Open Dataset for Autonomous Driving and Its Application , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Matthias Althoff,et al.  CommonRoad: Composable benchmarks for motion planning on roads , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[8]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Miguel Angel Sotelo,et al.  A Hybrid Vision-Map Method for Urban Road Detection , 2017 .

[10]  David Isele,et al.  Navigating Occluded Intersections with Autonomous Vehicles Using Deep Reinforcement Learning , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[11]  Bin Yang,et al.  SBNet: Sparse Blocks Network for Fast Inference , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[12]  한보형,et al.  Learning Deconvolution Network for Semantic Segmentation , 2015 .

[13]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Weimin Wang,et al.  Incremental and Enhanced Scanline-Based Segmentation Method for Surface Reconstruction of Sparse LiDAR Data , 2016, Remote. Sens..

[15]  Rachid Belaroussi,et al.  Accurate lateral positioning from map data and road marking detection , 2016, Expert Syst. Appl..

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

[17]  Ryan M. Eustice,et al.  Learning visual feature descriptors for dynamic lighting conditions , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  Sergio M. Savaresi,et al.  Driving style estimation via inertial measurements , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[19]  Giovanni Pau,et al.  Internet of Vehicles: From intelligent grid to autonomous cars and vehicular fogs , 2014, 2014 IEEE World Forum on Internet of Things (WF-IoT).

[20]  Fernando A. Mujica,et al.  An Empirical Evaluation of Deep Learning on Highway Driving , 2015, ArXiv.

[21]  Jerome Douret,et al.  A Reliable and Robust Lane Detection System based on the Parallel Use of Three Algorithms for Driving Safety Assistance , 2006, MVA.

[22]  Ruigang Yang,et al.  Learning Depth with Convolutional Spatial Propagation Network , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Yann LeCun,et al.  Off-Road Obstacle Avoidance through End-to-End Learning , 2005, NIPS.

[24]  Jean-Claude Latombe,et al.  Robot Motion Planning: A Distributed Representation Approach , 1991, Int. J. Robotics Res..

[25]  Jianxiong Xiao,et al.  Sliding Shapes for 3D Object Detection in Depth Images , 2014, ECCV.

[26]  Alonzo Kelly,et al.  Efficient Constrained Path Planning via Search in State Lattices , 2005 .

[27]  Haojie Li,et al.  Accurate Monocular 3D Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[28]  Alberto Broggi,et al.  PROUD—Public Road Urban Driverless-Car Test , 2015, IEEE Transactions on Intelligent Transportation Systems.

[29]  Eijiro Takeuchi,et al.  Robust localization using 3D NDT scan matching with experimentally determined uncertainty and road marker matching , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[30]  Klaus Bengler,et al.  Taking Over Control From Highly Automated Vehicles in Complex Traffic Situations , 2016, Hum. Factors.

[31]  Hsu-Yung Cheng,et al.  Lane Detection With Moving Vehicles in the Traffic Scenes , 2006, IEEE Transactions on Intelligent Transportation Systems.

[32]  Emilio Frazzoli,et al.  A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles , 2016, IEEE Transactions on Intelligent Vehicles.

[33]  Olivier Aycard,et al.  Detection, classification and tracking of moving objects in a 3D environment , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[34]  Frank Gauterin,et al.  Online driving style recognition using fuzzy logic , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[35]  Weihua Zhuang,et al.  Infotainment and road safety service support in vehicular networking: From a communication perspective , 2011 .

[36]  Peter Sanders,et al.  Combining hierarchical and goal-directed speed-up techniques for dijkstra's algorithm , 2008, JEAL.

[37]  Andreas Geiger,et al.  Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art , 2017, Found. Trends Comput. Graph. Vis..

[38]  Kazuya Takeda,et al.  A Traffic Flow Simulation Framework for Learning Driver Heterogeneity from Naturalistic Driving Data using Autoencoders , 2019 .

[39]  Mohan M. Trivedi,et al.  Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation , 2006, IEEE Transactions on Intelligent Transportation Systems.

[40]  Brandon Schoettle,et al.  Sensor Fusion: A Comparison of Sensing Capabilities of Human Drivers and Highly Automated Vehicles , 2017 .

[41]  Sebastian Thrun,et al.  Model based vehicle detection and tracking for autonomous urban driving , 2009, Auton. Robots.

[42]  Kazuya Takeda,et al.  Driver Modeling Based on Driving Behavior and Its Evaluation in Driver Identification , 2007, Proceedings of the IEEE.

[43]  Weihua Zhuang,et al.  Interworking of DSRC and Cellular Network Technologies for V2X Communications: A Survey , 2016, IEEE Transactions on Vehicular Technology.

[44]  Tobi Delbrück,et al.  A 128$\times$ 128 120 dB 15 $\mu$s Latency Asynchronous Temporal Contrast Vision Sensor , 2008, IEEE Journal of Solid-State Circuits.

[45]  Alberto Broggi,et al.  The VisLab Intercontinental Autonomous Challenge: An Extensive Test for a Platoon of Intelligent Vehicles , 2012 .

[46]  Ryan M. Eustice,et al.  Fast LIDAR localization using multiresolution Gaussian mixture maps , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[47]  Vijay Gadepally,et al.  A Framework for Estimating Long Term Driver Behavior , 2016, ArXiv.

[48]  Milan Simic,et al.  Sampling-Based Robot Motion Planning: A Review , 2014, IEEE Access.

[49]  Germán Ros,et al.  CARLA: An Open Urban Driving Simulator , 2017, CoRL.

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

[51]  Joshué Pérez,et al.  Dynamic trajectory generation using continuous-curvature algorithms for door to door assistance vehicles , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[52]  Karl Zipser,et al.  MultiNet: Multi-Modal Multi-Task Learning for Autonomous Driving , 2017, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV).

[53]  Alberto Ferreira de Souza,et al.  Self-Driving Cars: A Survey , 2019, Expert Syst. Appl..

[54]  Mario Gerla,et al.  Vehicular Cloud Computing , 2012, 2012 The 11th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net).

[55]  Steven E. Shladover,et al.  PATH at 20—History and Major Milestones , 2007, IEEE Transactions on Intelligent Transportation Systems.

[56]  Shumeet Baluja,et al.  Evolution of an artificial neural network based autonomous land vehicle controller , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[57]  Eijiro Takeuchi,et al.  Tsukuba Challenge 2017 Dynamic Object Tracks Dataset for Pedestrian Behavior Analysis , 2018, J. Robotics Mechatronics.

[58]  Jean-Paul Laumond,et al.  Robot Motion Planning and Control , 1998 .

[59]  Natasha Merat,et al.  Transition to manual: driver behaviour when resuming control from a highly automated vehicle , 2014 .

[60]  Jing Ren,et al.  Modified Newton's method applied to potential field-based navigation for mobile robots , 2006, IEEE Transactions on Robotics.

[61]  Sterling J. Anderson,et al.  Constraint-based planning and control for safe, semi-autonomous operation of vehicles , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[62]  Matti Pietikäinen,et al.  Deep Learning for Generic Object Detection: A Survey , 2018, International Journal of Computer Vision.

[63]  Suman Jana,et al.  DeepTest: Automated Testing of Deep-Neural-Network-Driven Autonomous Cars , 2017, 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE).

[64]  Lee Skrypchuk,et al.  Steering the conversation: A linguistic exploration of natural language interactions with a digital assistant during simulated driving. , 2017, Applied ergonomics.

[65]  Changchun Liu,et al.  Baidu Apollo EM Motion Planner , 2018, ArXiv.

[66]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[67]  Wendy Ju,et al.  Monitoring driver cognitive load using functional near infrared spectroscopy in partially autonomous cars , 2016, 2016 IEEE Intelligent Vehicles Symposium (IV).

[68]  Sergiu Nedevschi,et al.  Probabilistic Lane Tracking in Difficult Road Scenarios Using Stereovision , 2009, IEEE Transactions on Intelligent Transportation Systems.

[69]  Yarin Gal,et al.  Uncertainty in Deep Learning , 2016 .

[70]  Rangachar Kasturi,et al.  Machine vision , 1995 .

[71]  Dario Floreano,et al.  Neuroevolution: from architectures to learning , 2008, Evol. Intell..

[72]  Jian Wang,et al.  A Survey of Vehicle to Everything (V2X) Testing , 2019, Sensors.

[73]  Andrew Howard,et al.  Design and use paradigms for Gazebo, an open-source multi-robot simulator , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[74]  Sanjiv Singh,et al.  The DARPA Urban Challenge: Autonomous Vehicles in City Traffic, George Air Force Base, Victorville, California, USA , 2009, The DARPA Urban Challenge.

[75]  Homayoun Najjaran,et al.  Autonomous vehicle perception: The technology of today and tomorrow , 2018 .

[76]  Silvio Savarese,et al.  Learning to Track at 100 FPS with Deep Regression Networks , 2016, ECCV.

[77]  Sanja Fidler,et al.  Holistic Scene Understanding for 3D Object Detection with RGBD Cameras , 2013, 2013 IEEE International Conference on Computer Vision.

[78]  Juan Pablo Gonzalez,et al.  High Speed Navigation of Unrehearsed Terrain: Red Team Technology for Grand Challenge 2004 , 2004 .

[79]  Wolfram Burgard,et al.  Map-Based Precision Vehicle Localization in Urban Environments , 2008 .

[80]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[81]  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).

[82]  Ahmad El Sallab,et al.  YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud , 2018, ECCV Workshops.

[83]  Ram Dantu,et al.  Safe Driving Using Mobile Phones , 2012, IEEE Transactions on Intelligent Transportation Systems.

[84]  David González,et al.  A Review of Motion Planning Techniques for Automated Vehicles , 2016, IEEE Transactions on Intelligent Transportation Systems.

[85]  Liam Paull,et al.  Autonomous Vehicle Navigation in Rural Environments Without Detailed Prior Maps , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[86]  Mark Campbell,et al.  All Weather Perception: Joint Data Association, Tracking, and Classification for Autonomous Ground Vehicles , 2016, ArXiv.

[87]  Byung-Seo Kim,et al.  Information-Centric Network-Based Vehicular Communications: Overview and Research Opportunities , 2018, Sensors.

[88]  Sebastian Thrun,et al.  Towards fully autonomous driving: Systems and algorithms , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[89]  Ho Gi Jung,et al.  Sensor Fusion-Based Low-Cost Vehicle Localization System for Complex Urban Environments , 2017, IEEE Transactions on Intelligent Transportation Systems.

[90]  Thomas Schamm,et al.  Testing of Advanced Driver Assistance Towards Automated Driving: A Survey and Taxonomy on Existing Approaches and Open Questions , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[91]  Eva Ericsson,et al.  Independent driving pattern factors and their influence on fuel-use and exhaust emission factors , 2001 .

[92]  Leonidas J. Guibas,et al.  Frustum PointNets for 3D Object Detection from RGB-D Data , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[93]  Roland Siegwart,et al.  A Toolbox for Easily Calibrating Omnidirectional Cameras , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[94]  Domitilla Del Vecchio,et al.  Efficient algorithms for collision avoidance at intersections , 2012, HSCC '12.

[95]  Lennart Svensson,et al.  LIDAR-based driving path generation using fully convolutional neural networks , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

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

[97]  Radu Bogdan Rusu,et al.  Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments , 2010, KI - Künstliche Intelligenz.

[98]  Pei-Sung Lin,et al.  Naturalistic Driving Study: Field Data Collection , 2014 .

[99]  Shinpei Kato,et al.  An Open Approach to Autonomous Vehicles , 2015, IEEE Micro.

[100]  Brent Schwarz,et al.  LIDAR: Mapping the world in 3D , 2010 .

[101]  Mohan M. Trivedi,et al.  Examining the impact of driving style on the predictability and responsiveness of the driver: Real-world and simulator analysis , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[102]  Ralph Helmar Rasshofer,et al.  Automotive Radar and Lidar Systems for Next Generation Driver Assistance Functions , 2005 .

[103]  Ashutosh Saxena,et al.  Learning Depth from Single Monocular Images , 2005, NIPS.

[104]  John H. L. Hansen,et al.  Risky Action Recognition in Lane Change Video Clips using Deep Spatiotemporal Networks with Segmentation Mask Transfer , 2019, 2019 IEEE Intelligent Transportation Systems Conference (ITSC).

[105]  Karl H. Johansson,et al.  Heavy-Duty Vehicle Platooning for Sustainable Freight Transportation: A Cooperative Method to Enhance Safety and Efficiency , 2015, IEEE Control Systems.

[106]  Dongwook Kim,et al.  Computer vision at the hyundai autonomous challenge , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[107]  Stefan Byttner,et al.  Data-Driven Methods for Classification of Driving Styles in Buses , 2012 .

[108]  Cewu Lu,et al.  LiDAR-Video Driving Dataset: Learning Driving Policies Effectively , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[109]  Lei Liu,et al.  Learning a Rotation Invariant Detector with Rotatable Bounding Box , 2017, ArXiv.

[110]  Paul Newman,et al.  Illumination Invariant Imaging : Applications in Robust Vision-based Localisation , Mapping and Classification for Autonomous Vehicles , 2014 .

[111]  Eijiro Takeuchi,et al.  Autonomous driving based on accurate localization using multilayer LiDAR and dead reckoning , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[112]  Luke Fletcher,et al.  A perception-driven autonomous urban vehicle , 2008 .

[113]  Peter Kontschieder,et al.  The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[114]  Oliver M. J. Carsten,et al.  How can humans understand their automated cars? HMI principles, problems and solutions , 2019, Cognition, Technology & Work.

[115]  Magnus Egerstedt,et al.  Autonomous driving in urban environments: approaches, lessons and challenges , 2010, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[116]  Rajesh Rajamani,et al.  Vehicle dynamics and control , 2005 .

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

[118]  Wolfgang Rosenstiel,et al.  Driver-Activity Recognition in the Context of Conditionally Autonomous Driving , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[119]  Eijiro Takeuchi,et al.  Driving Feature Extraction and Behavior Classification Using an Autoencoder to Reproduce the Velocity Styles of Experts , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[120]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[121]  Sebastian Ramos,et al.  The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[122]  Kazuya Takeda,et al.  Integrating Driving Behavior and Traffic Context Through Signal Symbolization for Data Reduction and Risky Lane Change Detection , 2018, IEEE Transactions on Intelligent Vehicles.

[123]  Joachim Hertzberg,et al.  Evaluation of 3D registration reliability and speed - A comparison of ICP and NDT , 2009, 2009 IEEE International Conference on Robotics and Automation.

[124]  Mohammad A. Al-Khedher,et al.  Hybrid GPS-GSM Localization of Automobile Tracking System , 2011, ArXiv.

[125]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[126]  Shinpei Kato,et al.  Open Source Integrated Planner for Autonomous Navigation in Highly Dynamic Environments , 2017, J. Robotics Mechatronics.

[127]  Andreas Geiger,et al.  Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[128]  Luc Van Gool,et al.  Object Detection and Tracking for Autonomous Navigation in Dynamic Environments , 2010, Int. J. Robotics Res..

[129]  Andreas Geiger,et al.  Omnidirectional 3D reconstruction in augmented Manhattan worlds , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[130]  Fazal Urrahman Syed,et al.  Design and Analysis of an Adaptive Real-Time Advisory System for Improving Real World Fuel Economy in a Hybrid Electric Vehicle , 2010 .

[131]  Eric Wood,et al.  Accounting for the Variation of Driver Aggression in the Simulation of Conventional and Advanced Vehicles , 2013 .

[132]  Shinpei Kato,et al.  Precise and efficient model-based vehicle tracking method using Rao-Blackwellized and scaling series particle filters , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[133]  Tobi Delbrück,et al.  DDD17: End-To-End DAVIS Driving Dataset , 2017, ArXiv.

[134]  D. Rus,et al.  Annual Review of Control , Robotics , and Autonomous Systems Planning and Decision-Making for Autonomous Vehicles , 2018 .

[135]  Tadahiro Taniguchi,et al.  Visualization of Driving Behavior Based on Hidden Feature Extraction by Using Deep Learning , 2017, IEEE Transactions on Intelligent Transportation Systems.

[136]  Domitilla Del Vecchio,et al.  Cooperative Collision Avoidance at Intersections: Algorithms and Experiments , 2013, IEEE Transactions on Intelligent Transportation Systems.

[137]  Paul Newman,et al.  1 year, 1000 km: The Oxford RobotCar dataset , 2017, Int. J. Robotics Res..

[138]  Guoyan Xu,et al.  Computer vision-based multiple-lane detection on straight road and in a curve , 2010, 2010 International Conference on Image Analysis and Signal Processing.

[139]  Jeremy D. Sudweeks,et al.  An Analysis of Driver Inattention Using a Case-Crossover Approach On 100-Car Data: Final Report , 2010 .

[140]  Julius Ziegler,et al.  Trajectory planning for Bertha — A local, continuous method , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[141]  George Papandreou,et al.  Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation , 2018, ECCV.

[142]  Paul Newman,et al.  Model-free detection and tracking of dynamic objects with 2D lidar , 2015, Int. J. Robotics Res..

[143]  Dirck Van Vliet,et al.  IMPROVED SHORTEST PATH ALGORITHMS FOR TRANSPORT NETWORKS , 1978 .

[144]  Juan Andrade-Cetto,et al.  Localization in highly dynamic environments using dual-timescale NDT-MCL , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[145]  Fridulv Sagberg,et al.  A Review of Research on Driving Styles and Road Safety , 2015, Hum. Factors.

[146]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[147]  William Whittaker,et al.  Autonomous driving in urban environments: Boss and the Urban Challenge , 2008, J. Field Robotics.

[148]  Eric Chan,et al.  SARTRE Automated Platooning Vehicles , 2016 .

[149]  Sebastien Glaser,et al.  Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving , 2017, IEEE Trans. Intell. Veh..

[150]  Klaus C. J. Dietmayer,et al.  Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges , 2019, IEEE Transactions on Intelligent Transportation Systems.

[151]  Huimin Ma,et al.  3D Object Proposals for Accurate Object Class Detection , 2015, NIPS.

[152]  Xin Zhang,et al.  End to End Learning for Self-Driving Cars , 2016, ArXiv.

[153]  Tim D. Barfoot,et al.  It's not easy seeing green: Lighting-resistant stereo Visual Teach & Repeat using color-constant images , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[154]  Edward Jones,et al.  Review of pedestrian detection techniques in automotive far-infrared video , 2015 .

[155]  Edwin Olson,et al.  Finding multiple lanes in urban road networks with vision and lidar , 2009, Auton. Robots.

[156]  Roberto Cipolla,et al.  Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning , 2017, IJCAI.

[157]  Andrew V. Goldberg,et al.  Route Planning in Transportation Networks , 2015, Algorithm Engineering.

[158]  Cordelia Schmid,et al.  The 2005 PASCAL Visual Object Classes Challenge , 2005, MLCW.

[159]  Edith Cohen,et al.  Reachability and distance queries via 2-hop labels , 2002, SODA '02.

[160]  A. Augustynowicz Preliminary classification of driving style with objective rank method , 2009 .

[161]  Min Bai,et al.  TorontoCity: Seeing the World with a Million Eyes , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[162]  Ragunathan Rajkumar,et al.  Towards a viable autonomous driving research platform , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[163]  Narciso García,et al.  Event-Based Vision Meets Deep Learning on Steering Prediction for Self-Driving Cars , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[164]  Lindsay Kleeman,et al.  Accurate odometry and error modelling for a mobile robot , 1997, Proceedings of International Conference on Robotics and Automation.

[165]  Sanjiv Singh,et al.  The 2005 DARPA Grand Challenge: The Great Robot Race , 2007 .

[166]  Hauke Stahle,et al.  A sensor fusion approach for localization with cumulative error elimination , 2012, 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI).

[167]  Sebastian Thrun,et al.  Path Planning for Autonomous Vehicles in Unknown Semi-structured Environments , 2010, Int. J. Robotics Res..

[168]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[169]  Bo Gao,et al.  Driving Style Recognition for Intelligent Vehicle Control and Advanced Driver Assistance: A Survey , 2018, IEEE Transactions on Intelligent Transportation Systems.

[170]  Xiaogang Wang,et al.  Pyramid Scene Parsing Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[171]  Yadong Mu,et al.  Deep Steering: Learning End-to-End Driving Model from Spatial and Temporal Visual Cues , 2017, ArXiv.

[172]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[173]  Tian Xia,et al.  Vehicle Detection from 3D Lidar Using Fully Convolutional Network , 2016, Robotics: Science and Systems.

[174]  Jiong Yang,et al.  PointPillars: Fast Encoders for Object Detection From Point Clouds , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[175]  Ümit Özgüner,et al.  Constrained Backward Path Tracking Control using a Plug-in Jackknife Prevention System for Autonomous Tractor-Trailers , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[176]  Ingmar Posner,et al.  Voting for Voting in Online Point Cloud Object Detection , 2015, Robotics: Science and Systems.

[177]  Andreas Lawitzky,et al.  A Combined Model- and Learning-Based Framework for Interaction-Aware Maneuver Prediction , 2016, IEEE Transactions on Intelligent Transportation Systems.

[178]  Alicia L. Carriquiry,et al.  Driving behavior at a roundabout: A hierarchical Bayesian regression analysis , 2014 .

[179]  Martin Magnusson,et al.  The three-dimensional normal-distributions transform : an efficient representation for registration, surface analysis, and loop detection , 2009 .

[180]  Etienne Perot,et al.  Deep Reinforcement Learning framework for Autonomous Driving , 2017, Autonomous Vehicles and Machines.

[181]  Chiara Bartolozzi,et al.  Event-Based Vision: A Survey , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[183]  Mohan M. Trivedi,et al.  Driver Behavior and Situation Aware Brake Assistance for Intelligent Vehicles , 2007, Proceedings of the IEEE.

[184]  Yilu Zhang,et al.  A Pattern-Recognition Approach for Driving Skill Characterization , 2010, IEEE Transactions on Intelligent Transportation Systems.

[185]  Tatsuya Suzuki,et al.  Trajectory planning for automated parking using multi-resolution state roadmap considering non-holonomic constraints , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[186]  Ruigang Yang,et al.  The ApolloScape Dataset for Autonomous Driving , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[187]  Mara Tanelli,et al.  Quantitative Driving Style Estimation for Energy-Oriented Applications in Road Vehicles , 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

[188]  Yi-Ting Chen,et al.  The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes , 2019, 2019 International Conference on Robotics and Automation (ICRA).

[189]  Kazuya Takeda,et al.  Traffic trajectory history and drive path generation using GPS data cloud , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

[190]  Eijiro Takeuchi,et al.  A Slope-robust Cascaded Ground Segmentation in 3D Point Cloud for Autonomous Vehicles , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[191]  Ingmar Posner,et al.  Find your own way: Weakly-supervised segmentation of path proposals for urban autonomy , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[192]  Bo Li,et al.  SECOND: Sparsely Embedded Convolutional Detection , 2018, Sensors.

[193]  Wenhan Luo,et al.  Multiple object tracking: A literature review , 2014, Artif. Intell..

[194]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[195]  Paul Newman,et al.  Distraction suppression for vision-based pose estimation at city scales , 2013, 2013 IEEE International Conference on Robotics and Automation.

[196]  Plamen Petrov,et al.  Modeling and Nonlinear Adaptive Control for Autonomous Vehicle Overtaking , 2014, IEEE Transactions on Intelligent Transportation Systems.

[197]  Rajkumar Buyya,et al.  A survey on vehicular cloud computing , 2014, J. Netw. Comput. Appl..

[198]  Mehrdad Dianati,et al.  A Survey of the State-of-the-Art Localization Techniques and Their Potentials for Autonomous Vehicle Applications , 2018, IEEE Internet of Things Journal.

[199]  Alberto Broggi,et al.  Extensive Tests of Autonomous Driving Technologies , 2013, IEEE Transactions on Intelligent Transportation Systems.

[200]  Yang Gao,et al.  End-to-End Learning of Driving Models from Large-Scale Video Datasets , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[201]  Elham Semsar-Kazerooni,et al.  The Grand Cooperative Driving Challenge 2016: boosting the introduction of cooperative automated vehicles , 2016, IEEE Wireless Communications.

[202]  Ryan M. Eustice,et al.  Visual localization within LIDAR maps for automated urban driving , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[203]  Christos Dimitrakakis,et al.  TORCS, The Open Racing Car Simulator , 2005 .

[204]  Angelos Amditis,et al.  Online prediction of an electric vehicle remaining range based on regression analysis , 2014, 2014 IEEE International Electric Vehicle Conference (IEVC).

[205]  David Janz,et al.  Learning to Drive in a Day , 2018, 2019 International Conference on Robotics and Automation (ICRA).

[206]  Ronen Lerner,et al.  Recent progress in road and lane detection: a survey , 2012, Machine Vision and Applications.

[207]  Mathias Perrollaz,et al.  Learning-based approach for online lane change intention prediction , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[208]  B. Ulmer VITA II-active collision avoidance in real traffic , 1994, Proceedings of the Intelligent Vehicles '94 Symposium.

[209]  Myoungho Sunwoo,et al.  Design factor optimization of 3D flash lidar sensor based on geometrical model for automated vehicle and advanced driver assistance system applications , 2017 .

[210]  Yin Zhou,et al.  VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[211]  Andrew V. Goldberg,et al.  PHAST: Hardware-Accelerated Shortest Path Trees , 2011, 2011 IEEE International Parallel & Distributed Processing Symposium.

[212]  Nicholas Roy,et al.  PROBE-GK: Predictive robust estimation using generalized kernels , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[213]  Ch. Ramesh Babu,et al.  Internet of Vehicles: From Intelligent Grid to Autonomous Cars and Vehicular Clouds , 2016 .

[214]  C. A. Pickering,et al.  A Review of Automotive Human Machine Interface Technologies and Techniques to Reduce Driver Distraction , 2007 .

[215]  Hong Jiang,et al.  Establishing Style-Oriented Driver Models by Imitating Human Driving Behaviors , 2015, IEEE Transactions on Intelligent Transportation Systems.

[216]  Swarun Kumar,et al.  CarSpeak: a content-centric network for autonomous driving , 2012, SIGCOMM '12.

[217]  Sagar Behere,et al.  A functional architecture for autonomous driving , 2015, 2015 First International Workshop on Automotive Software Architecture (WASA).

[218]  Bin Wang,et al.  Backward Path Tracking Control for Mobile Robot with Three Trailers , 2017, ICONIP.

[219]  Michael Weber,et al.  Autonomous driving: investigating the feasibility of car-driver handover assistance , 2015, AutomotiveUI.

[220]  Wei-Wen Kao,et al.  Integration of GPS and dead-reckoning navigation systems , 1991, Vehicle Navigation and Information Systems Conference, 1991.

[221]  Antonio M. López,et al.  Shadow Resistant Road Segmentation from a Mobile Monocular System , 2007, IbPRIA.

[222]  Hugh Durrant-Whyte,et al.  Simultaneous localization and mapping (SLAM): part II , 2006 .

[223]  Xiaozhi Qu,et al.  Vehicle localization using mono-camera and geo-referenced traffic signs , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

[224]  Julius Ziegler,et al.  Making Bertha Drive—An Autonomous Journey on a Historic Route , 2014, IEEE Intelligent Transportation Systems Magazine.

[225]  Vladlen Koltun,et al.  Playing for Benchmarks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[226]  Dizan Vasquez,et al.  A survey on motion prediction and risk assessment for intelligent vehicles , 2014 .

[227]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[228]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[229]  Hans-Joachim Wünsche,et al.  Autonomous convoy driving by night: The vehicle tracking system , 2015, 2015 IEEE International Conference on Technologies for Practical Robot Applications (TePRA).

[230]  Travis Crayton,et al.  Autonomous Vehicles: Developing a Public Health Research Agenda to Frame the Future of Transportation Policy , 2017 .

[231]  John H. L. Hansen,et al.  International Large-Scale Vehicle Corpora for Research on Driver Behavior on the Road , 2011, IEEE Transactions on Intelligent Transportation Systems.

[232]  Roland Siegwart,et al.  Appearance-Guided Monocular Omnidirectional Visual Odometry for Outdoor Ground Vehicles , 2008, IEEE Transactions on Robotics.

[233]  Qiang Xu,et al.  nuScenes: A Multimodal Dataset for Autonomous Driving , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[234]  S. Ali A. Moosavian,et al.  Robust Adaptive Controller for a Tractor–Trailer Mobile Robot , 2014, IEEE/ASME Transactions on Mechatronics.

[235]  T. Delbruck,et al.  > Replace This Line with Your Paper Identification Number (double-click Here to Edit) < 1 , 2022 .

[236]  Lutz Eckstein,et al.  euroFOT: Field Operational Test and Impact Assessment of Advanced Driver Assistance Systems: Final Results , 2013 .

[237]  Tao Wu,et al.  Light-weight localization for vehicles using road markings , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[238]  Daniel Krajzewicz,et al.  SUMO (Simulation of Urban MObility) - an open-source traffic simulation , 2002 .

[239]  Monson H. Hayes,et al.  Robust lane detection and tracking with ransac and Kalman filter , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[240]  Shu Liu,et al.  IPOD: Intensive Point-based Object Detector for Point Cloud , 2018, ArXiv.

[241]  Paulius Lengvenis,et al.  Driving style classification using long-term accelerometer information , 2014, 2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR).

[242]  Monica Vladoiu,et al.  Driving Style Analysis Using Data Mining Techniques , 2010, Int. J. Comput. Commun. Control.

[243]  Steven E Shladover,et al.  OVERVIEW OF PLATOONING SYSTEMS , 2012 .

[244]  Chiyomi Miyajima,et al.  Driving Signature Extraction , 2015 .

[245]  Eder Santana,et al.  Learning a Driving Simulator , 2016, ArXiv.

[246]  Xindong Wu,et al.  Object Detection With Deep Learning: A Review , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[247]  Bin Yang,et al.  PIXOR: Real-time 3D Object Detection from Point Clouds , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[248]  Carlos Busso,et al.  Driver Modeling for Detection and Assessment of Driver Distraction: Examples from the UTDrive Test Bed , 2017, IEEE Signal Processing Magazine.

[249]  Eder Santana,et al.  A Commute in Data: The comma2k19 Dataset , 2018, ArXiv.

[250]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[251]  Gary R. Bradski,et al.  Detection of Drivable Corridors for Off-Road Autonomous Navigation , 2006, 2006 International Conference on Image Processing.

[252]  Kostas Daniilidis,et al.  A Unifying Theory for Central Panoramic Systems and Practical Applications , 2000, ECCV.

[253]  S. LaValle,et al.  Randomized Kinodynamic Planning , 2001 .

[254]  Santokh Singh,et al.  Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey , 2015 .

[255]  Namil Kim,et al.  Fast multiple objects detection and tracking fusing color camera and 3D LIDAR for intelligent vehicles , 2016, 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI).

[256]  Kaiming He,et al.  Mask R-CNN , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[257]  Luc Van Gool,et al.  End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners , 2018, ECCV.

[258]  Xiaogang Wang,et al.  PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[259]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[260]  Peter Sanders,et al.  Exact Routing in Large Road Networks Using Contraction Hierarchies , 2012, Transp. Sci..

[261]  Yi Lu Murphey,et al.  Driver's style classification using jerk analysis , 2009, 2009 IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems.

[262]  B. Faverjon,et al.  Probabilistic Roadmaps for Path Planning in High-Dimensional Con(cid:12)guration Spaces , 1996 .

[263]  Takashi Tsubouchi,et al.  A 3-D Scan Matching using Improved 3-D Normal Distributions Transform for Mobile Robotic Mapping , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[264]  Tatsuya Harada,et al.  MFNet: Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[265]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[266]  Mohan M. Trivedi,et al.  Driving style recognition using a smartphone as a sensor platform , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[267]  M Cameron,et al.  World Report on Road Traffic Injury Prevention. , 2004 .

[268]  Sebastian Thrun,et al.  Robust vehicle localization in urban environments using probabilistic maps , 2010, 2010 IEEE International Conference on Robotics and Automation.

[269]  Emilio Frazzoli,et al.  Sampling-based algorithms for optimal motion planning , 2011, Int. J. Robotics Res..

[270]  Anil K. Jain,et al.  A modified Hausdorff distance for object matching , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[271]  In So Kweon,et al.  KAIST Multi-Spectral Day/Night Data Set for Autonomous and Assisted Driving , 2018, IEEE Transactions on Intelligent Transportation Systems.

[272]  Peng Liu,et al.  Classification of Highway Lane Change Behavior to Detect Dangerous Cut-in Maneuvers , 2016 .

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

[274]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[275]  Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles , 2022 .

[276]  Klaus C. J. Dietmayer,et al.  Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[277]  Ji Wan,et al.  Multi-view 3D Object Detection Network for Autonomous Driving , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[278]  Andrew McGordon,et al.  An investigation on the effect of driver style and driving events on energy demand of a PHEV , 2012 .