Towards Next-Generation Vehicles Featuring the Vehicle Intelligence

Safe driving and minimizing the number of casualties are the main motivations of researchers and car companies for decades. They also care very much on saving fuel consumption and high comfort-level trips. With the help of advanced driver assistance systems (ADAS) applications, safer, more comfortable, and greener trips are very likely at the present time. However, today humankind is very close to make a very old dream, namely, driverless vehicles, to come true. In this paper, we address the concept of next-generation vehicles, their requirements, challenges, advantages, and problems. Regarding Society of Automotive Engineers (SAE), levels (1–5), we first define the crucial contexts for next-generation vehicles. We then discuss existing ADAS, their abilities, and available platforms which they run on from past to present. Next, we introduce a novel vehicle intelligence (VI) architecture consisting of ADAS modules and VI services which would pave the way for fully autonomous vehicles regarding not only driving issue, but also human-centric new demands such as entertainment and comfort level of the journey. The proposed conceptual design is built on sensors, vehicle ad hoc networks (VANETs), and big data. Afterward, we describe how current ADAS applications would transform on the way toward SAE Level 5 cars. We finally discuss the open issues for next-generation vehicles.

[1]  Moustafa Youssef,et al.  Robust and ubiquitous smartphone-based lane detection , 2016, Pervasive Mob. Comput..

[2]  Anders Lindgren,et al.  State of the Art Analysis: An Overview of Advanced Driver Assistance Systems (ADAS) and Possible Human Factors Issues , 2006 .

[3]  Aniruddha Sinha,et al.  Participatory sensing based traffic condition monitoring using horn detection , 2013, SAC '13.

[4]  Pengfei Shi,et al.  Yawning detection for determining driver drowsiness , 2005, Proceedings of 2005 IEEE International Workshop on VLSI Design and Video Technology, 2005..

[5]  Hans P A Van Dongen,et al.  Efficient driver drowsiness detection at moderate levels of drowsiness. , 2013, Accident; analysis and prevention.

[6]  Mohan M. Trivedi,et al.  Turn-Intent Analysis Using Body Pose for Intelligent Driver Assistance , 2006, IEEE Pervasive Computing.

[7]  Gyuchoon Cho Real Time Driver Safety System , 2009 .

[8]  Mohan M. Trivedi,et al.  Where is the driver looking: Analysis of head, eye and iris for robust gaze zone estimation , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[9]  Erdal Oruklu,et al.  FPGA-Based Traffic Sign Recognition for Advanced Driver Assistance Systems , 2013 .

[10]  Changsheng Li,et al.  Characterizing Driving Styles with Deep Learning , 2016, ArXiv.

[11]  Neil Mansfield,et al.  Driving performance and driver discomfort in an elevated and standard driving position during a driving simulation. , 2015, Applied ergonomics.

[12]  B. Schiele,et al.  How Far are We from Solving Pedestrian Detection? , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[14]  Athanasios V. Vasilakos,et al.  Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles , 2016, Sensors.

[15]  Xiaohui Wu,et al.  Detection of driver drowsiness using wearable devices: A feasibility study of the proximity sensor. , 2017, Applied ergonomics.

[16]  Mark Vollrath,et al.  Speech and driving - solution or problem? , 2007 .

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

[18]  Richard P. Martin,et al.  Detecting driver phone use leveraging car speakers , 2011, MobiCom.

[19]  L. M. Bergasa,et al.  Fog detection system based on computer vision techniques , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[20]  Jean-Philippe Tarel,et al.  Vision Enhancement in Homogeneous and Heterogeneous Fog , 2012, IEEE Intelligent Transportation Systems Magazine.

[21]  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).

[22]  Franz Kummert,et al.  Pedestrian crossing prediction using multiple context-based models , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[23]  Mike McDonald,et al.  Advanced Driver Assistance Systems from Autonomous to Cooperative Approach , 2008 .

[24]  Mohan M. Trivedi,et al.  Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis , 2013, IEEE Transactions on Intelligent Transportation Systems.

[25]  Stefan Baten,et al.  Road recognition for a tracked vehicle , 2000, Defense, Security, and Sensing.

[26]  Catherine M. Burns,et al.  Autonomous Driving in the Real World: Experiences with Tesla Autopilot and Summon , 2016, AutomotiveUI.

[27]  Nicolas Hautière,et al.  Towards night fog detection through use of in-vehicle multipurpose cameras , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[28]  Jin-Hyuk Hong,et al.  A smartphone-based sensing platform to model aggressive driving behaviors , 2014, CHI.

[29]  Jean Ponce,et al.  General Road Detection From a Single Image , 2010, IEEE Transactions on Image Processing.

[30]  Mohan M. Trivedi,et al.  Vision for Looking at Traffic Lights: Issues, Survey, and Perspectives , 2016, IEEE Transactions on Intelligent Transportation Systems.

[31]  Erhan Akin,et al.  Estimating driving behavior by a smartphone , 2012, 2012 IEEE Intelligent Vehicles Symposium.

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

[33]  Tong Boon Tang,et al.  Vehicle Detection Techniques for Collision Avoidance Systems: A Review , 2015, IEEE Transactions on Intelligent Transportation Systems.

[34]  N. H. C. Yung,et al.  Automated Vehicle Overtaking based on a Multiple-Goal Reinforcement Learning Framework , 2007, 2007 IEEE Intelligent Transportation Systems Conference.

[35]  김효태 Control method for hill start assist control system , 2014 .

[36]  Luigi del Re,et al.  Opportunities on Fuel Economy Utilizing V2V Based Drive Systems , 2013 .

[37]  Kai-Tai Song,et al.  Lateral Driving Assistance Using Optical Flow and Scene Analysis , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[38]  Vassilis Gikas,et al.  Localization and Driving Behavior Classification with Smartphone Sensors in Direct Absence of Global Navigation Satellite Systems , 2015 .

[39]  Steven E. Shladover,et al.  Potential Cyberattacks on Automated Vehicles , 2015, IEEE Transactions on Intelligent Transportation Systems.

[40]  Wenjia Li,et al.  Driver identification and authentication with active behavior modeling , 2016, 2016 12th International Conference on Network and Service Management (CNSM).

[41]  Rafael A. Berri,et al.  A pattern recognition system for detecting use of mobile phones while driving , 2014, 2014 International Conference on Computer Vision Theory and Applications (VISAPP).

[42]  Mona Omidyeganeh,et al.  Intelligent driver drowsiness detection through fusion of yawning and eye closure , 2011, 2011 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems Proceedings.

[43]  Necmettin Sezgin,et al.  The ANN-based computing of drowsy level , 2009, Expert Syst. Appl..

[44]  Jadwiga Indulska,et al.  A survey of context modelling and reasoning techniques , 2010, Pervasive Mob. Comput..

[45]  Wu He,et al.  Developing Vehicular Data Cloud Services in the IoT Environment , 2014, IEEE Transactions on Industrial Informatics.

[46]  Jay Lee,et al.  Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility , 2014 .

[47]  Shuyan Hu,et al.  Driver drowsiness detection with eyelid related parameters by Support Vector Machine , 2009, Expert Syst. Appl..

[48]  Luca Delgrossi,et al.  IEEE 802.11p: Towards an International Standard for Wireless Access in Vehicular Environments , 2008, VTC Spring 2008 - IEEE Vehicular Technology Conference.

[49]  Petros A. Ioannou,et al.  Personalized Driver Assistance for Signalized Intersections Using V2I Communication , 2016, IEEE Transactions on Intelligent Transportation Systems.

[50]  Kazuaki Terashima,et al.  A survey of technical trend of ADAS and autonomous driving , 2014, Proceedings of Technical Program - 2014 International Symposium on VLSI Technology, Systems and Application (VLSI-TSA).

[51]  Carlos Canudas de Wit,et al.  Eco-driving in urban traffic networks using traffic signal information , 2013, 52nd IEEE Conference on Decision and Control.

[52]  Daqiang Zhang,et al.  Cloud-Assisted Mobile Crowd Sensing for Traffic Congestion Control , 2017, Mob. Networks Appl..

[53]  Seema Verma,et al.  A survey on driver behavior detection techniques for intelligent transportation systems , 2017, 2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence.

[54]  Yashon O. Ouma,et al.  Pothole detection on asphalt pavements from 2D-colour pothole images using fuzzy c-means clustering and morphological reconstruction , 2017 .

[55]  Osama Masoud,et al.  Vision-based methods for driver monitoring , 2003, Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems.

[56]  Tom Brijs,et al.  Driving with intelligent speed adaptation: Final results of the Belgian ISA-trial , 2007 .

[57]  Zahid Halim,et al.  Profiling drivers based on driver dependent vehicle driving features , 2015, Applied Intelligence.

[58]  Juan-Carlos Cano,et al.  Drivingstyles: a mobile platform for driving styles and fuel consumption characterization , 2016, Journal of Communications and Networks.

[59]  Pietro Perona,et al.  Pedestrian detection: A benchmark , 2009, CVPR.

[60]  Jean Ponce,et al.  Vanishing point detection for road detection , 2009, CVPR.

[61]  Assia Belbachir An embedded testbed architecture to evaluate autonomous car driving , 2017, Intell. Serv. Robotics.

[62]  Girts Strazdins,et al.  Real time pothole detection using Android smartphones with accelerometers , 2011, 2011 International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS).

[63]  David Cebon,et al.  Application of observer-based fault detection in vehicle roll control , 2009 .

[64]  Mohan M. Trivedi,et al.  Driver classification and driving style recognition using inertial sensors , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[65]  Gwen Littlewort,et al.  Drowsy Driver Detection Through Facial Movement Analysis , 2007, ICCV-HCI.

[66]  Bing-Fei Wu,et al.  Driving behaviour-based event data recorder , 2014 .

[67]  Sibel Yenikaya,et al.  Keeping the vehicle on the road: A survey on on-road lane detection systems , 2013, CSUR.

[68]  Ho Gi Jung,et al.  A New Approach to Urban Pedestrian Detection for Automatic Braking , 2009, IEEE Transactions on Intelligent Transportation Systems.

[69]  Orhan Bulan,et al.  Driver Cell Phone Usage Detection from HOV/HOT NIR Images , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[70]  Zhilu Wu,et al.  A robust, coarse-to-fine traffic sign detection method , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[71]  Gang Li,et al.  Detection of Driver Drowsiness Using Wavelet Analysis of Heart Rate Variability and a Support Vector Machine Classifier , 2013, Sensors.

[72]  W. Jatmiko,et al.  Vehicle counting and speed measurement using headlight detection , 2013, 2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS).

[73]  Qingquan Li,et al.  A Sensor-Fusion Drivable-Region and Lane-Detection System for Autonomous Vehicle Navigation in Challenging Road Scenarios , 2014, IEEE Transactions on Vehicular Technology.

[74]  Pengtao Xie,et al.  Poseidon: A System Architecture for Efficient GPU-based Deep Learning on Multiple Machines , 2015, ArXiv.

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

[76]  Andrea Baiocchi,et al.  Traffic monitoring and incident detection through VANETs , 2014, 2014 11th Annual Conference on Wireless On-demand Network Systems and Services (WONS).

[77]  Frans Coenen,et al.  Road surface traffic sign detection with hybrid region proposal and fast R-CNN , 2016, 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD).

[78]  Sergiu Nedevschi,et al.  Detection and classification of painted road objects for intersection assistance applications , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[79]  Rosaldo J. F. Rossetti,et al.  Forward collision warning systems using heads-up displays: Testing usability of two new metaphors , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[80]  Gianpaolo Francesco Trotta,et al.  Computer vision and deep learning techniques for pedestrian detection and tracking: A survey , 2018, Neurocomputing.

[81]  Myoungho Sunwoo,et al.  GPS-bias correction for precise localization of autonomous vehicles , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[82]  C. Asensio,et al.  On-board wet road surface identification using tyre/road noise and Support Vector Machines , 2014 .

[83]  Yu Tao Study on Forward Collision Warning-avoidance Algorithm Based on Driver Characteristics Adaptation , 2009 .

[84]  Fawzi Nashashibi,et al.  Real time visual traffic lights recognition based on Spot Light Detection and adaptive traffic lights templates , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[85]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[86]  Pietro Perona,et al.  Pedestrian Detection: An Evaluation of the State of the Art , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[87]  Charles A. Green,et al.  Human factors evaluation of Level 2 and Level 3 automated driving concepts , 2015 .

[88]  Kunja Bihari Swain,et al.  Driver assistant for the detection of drowsiness and alcohol effect , 2017, 2017 Third International Conference on Sensing, Signal Processing and Security (ICSSS).

[89]  Raja Sengupta,et al.  Cooperative Collision Warning: Enabling Crash Avoidance with Wireless Technology , 2005 .

[90]  Masao Nagai Research into ADAS with autonomous driving intelligence for future innovation , 2014 .

[91]  Feng Guo,et al.  Driver crash risk factors and prevalence evaluation using naturalistic driving data , 2016, Proceedings of the National Academy of Sciences.

[92]  Qingzhang Chen,et al.  Research on Real-time Identification for Tire Failure , 2014 .

[93]  Zhitao Xiao,et al.  Driver Fatigue Detection Based on Eye State Recognition , 2017, 2017 International Conference on Machine Vision and Information Technology (CMVIT).

[94]  Wei Tu,et al.  A survey of in-vehicle communications: Requirements, solutions and opportunities in IoT , 2015, 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT).

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

[96]  Jennifer Healey,et al.  SmartCar: detecting driver stress , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[97]  Zheng Liu,et al.  Traffic light recognition in varying illumination using deep learning and saliency map , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[98]  Klaus C. J. Dietmayer,et al.  Automatic generation of a highly accurate map for driver assistance systems in road construction sites , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[99]  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.

[100]  David Gerónimo Gómez,et al.  Survey of Pedestrian Detection for Advanced Driver Assistance Systems , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[101]  Michael Sivak,et al.  A Survey of Public Opinion about Autonomous and Self-Driving Vehicles in the U.S., the U.K., and Australia , 2014 .

[102]  Peter Rossiter,et al.  Applying neural network analysis on heart rate variability data to assess driver fatigue , 2011, Expert Syst. Appl..

[103]  Juan E. Gilbert,et al.  A Usability Evaluation of the BMW Active Cruise Control System With “Stop and Go” Function , 2017 .

[104]  Yanjun Huang,et al.  Path Planning and Tracking for Vehicle Collision Avoidance Based on Model Predictive Control With Multiconstraints , 2017, IEEE Transactions on Vehicular Technology.

[105]  Cristina Olaverri-Monreal,et al.  The See-Through System: A VANET-enabled assistant for overtaking maneuvers , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[106]  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.

[107]  Thierry Derrmann,et al.  Driver Behavior Profiling Using Smartphones: A Low-Cost Platform for Driver Monitoring , 2015, IEEE Intelligent Transportation Systems Magazine.

[108]  P. S. Hiremath,et al.  A Technique for Bump Detection in Indian Road Images Using Color Segmentation and Knowledge Base Object Detection , 2013 .

[109]  Thomas B. Moeslund,et al.  Vision-Based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey , 2012, IEEE Transactions on Intelligent Transportation Systems.

[110]  Peter R. Stopher,et al.  Review of GPS Travel Survey and GPS Data-Processing Methods , 2014 .

[111]  Wei Li,et al.  Evaluation of driver fatigue on two channels of EEG data , 2012, Neuroscience Letters.

[112]  A A Tecimer,et al.  Assessment of vehicular transportation quality via smartphones , 2015 .

[113]  Michael Schier,et al.  Next Generation Car – Technologies for future EVs , 2016 .

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

[115]  Hermann Winner,et al.  Three Decades of Driver Assistance Systems: Review and Future Perspectives , 2014, IEEE Intelligent Transportation Systems Magazine.

[116]  Xiaofeng Wang,et al.  Seat Belt Detection Using Convolutional Neural Network BN-AlexNet , 2017, ICIC.

[117]  Prakash Choudhary,et al.  A Survey Paper On Drowsiness Detection & Alarm System for Drivers , 2017 .

[118]  Weixing Wang,et al.  Driver Fatigue Detection Based on Eye Tracking , 2006, 2006 6th World Congress on Intelligent Control and Automation.

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

[120]  A Williamson,et al.  Review of on-road driver fatigue monitoring devices , 2005 .

[121]  Mohan M. Trivedi,et al.  Self-Driving Cars , 2017, Computer.

[122]  Divya Bansal,et al.  Road Condition Detection Using Smartphone Sensors : A Survey , 2014 .

[123]  Elias B. Kosmatopoulos,et al.  Collision avoidance analysis for lane changing and merging , 1999, IEEE Trans. Veh. Technol..

[124]  Lynn Batten,et al.  Cyber security attacks to modern vehicular systems , 2017, J. Inf. Secur. Appl..

[125]  Franklin M. Silva,et al.  Real Time Driver Drowsiness Detection Based on Driver's Face Image Behavior Using a System of Human Computer Interaction Implemented in a Smartphone , 2018, ICITS.

[126]  Jay A. Farrell,et al.  Traffic sign detection, state estimation, and identification using onboard sensors , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[127]  Yoshiko Kojima,et al.  A Multimodal ADAS System for Unmarked Urban Scenarios Based on Road Context Understanding , 2015, IEEE Transactions on Intelligent Transportation Systems.

[128]  Hassan Mostafa,et al.  V2V-based vehicle risk assessment and control for lane-keeping and collision avoidance , 2017, 2017 29th International Conference on Microelectronics (ICM).

[129]  Dongpu Cao,et al.  Parallel driving in CPSS: a unified approach for transport automation and vehicle intelligence , 2017, IEEE/CAA Journal of Automatica Sinica.

[130]  Jay D. Fuletra A Survey on Driver’s Drowsiness Detection Techniques , 2013 .

[131]  Peter Davidson,et al.  AUTONOMOUS VEHICLES - WHAT COULD THIS MEAN FOR THE FUTURE OF TRANSPORT? , 2015 .

[132]  Thomas A. Dingus,et al.  The Impact of Driver Inattention on Near-Crash/Crash Risk: An Analysis Using the 100-Car Naturalistic Driving Study Data , 2006 .

[133]  Marthinus J. Booysen,et al.  Survey of smartphone-based sensing in vehicles for intelligent transportation system applications , 2015 .

[134]  Johannes Stallkamp,et al.  Detection of traffic signs in real-world images: The German traffic sign detection benchmark , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[135]  Florian Kerschbaum,et al.  Privacy and Integrity Considerations in Hyperconnected Autonomous Vehicles , 2018, Proceedings of the IEEE.

[136]  Dong Xuan,et al.  Mobile phone based drunk driving detection , 2010, 2010 4th International Conference on Pervasive Computing Technologies for Healthcare.

[137]  Joshué Pérez,et al.  Arbitration for balancing control between the driver and ADAS systems in an automated vehicle: Survey and approach , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[138]  Martin N. Milkovits Modeling the Factors Affecting Bus Stop Dwell Time , 2008 .

[139]  Dieter Schmalstieg,et al.  Indoor Positioning and Navigation with Camera Phones , 2009, IEEE Pervasive Computing.

[140]  Ding-Yu Fei,et al.  Accelerometer-based steering-wheel movement monitoring for drowsy-driving detection , 2015 .

[141]  Ryan Newton,et al.  The pothole patrol: using a mobile sensor network for road surface monitoring , 2008, MobiSys '08.

[142]  Purushottam Kulkarni,et al.  Wolverine: Traffic and road condition estimation using smartphone sensors , 2012, 2012 Fourth International Conference on Communication Systems and Networks (COMSNETS 2012).

[143]  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.

[144]  Martin Cerny,et al.  Emergency horn detection using embedded systems , 2016, 2016 IEEE 14th International Symposium on Applied Machine Intelligence and Informatics (SAMI).

[145]  G. M. Bhandari,et al.  YAWNING ANALYSIS FOR DRIVER DROWSINESS DETECTION , 2014 .

[146]  Marios Savvides,et al.  Driver cell phone usage detection on Strategic Highway Research Program (SHRP2) face view videos , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[147]  Cristiano Premebida,et al.  LIDAR and vision‐based pedestrian detection system , 2009, J. Field Robotics.

[148]  German Castignani,et al.  Driver behavior profiling using smartphones , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[149]  P. Manzoni,et al.  Automatic Accident Detection: Assistance Through Communication Technologies and Vehicles , 2012, IEEE Vehicular Technology Magazine.

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

[151]  Kenneth Sundaraj,et al.  Detecting Driver Drowsiness Based on Sensors: A Review , 2012, Sensors.

[152]  Keiichi Uchimura,et al.  Driver inattention monitoring system for intelligent vehicles: A review , 2009 .

[153]  Hongmei Ren,et al.  Accurate seat belt detection in road surveillance images based on CNN and SVM , 2018, Neurocomputing.

[154]  I. Ide,et al.  Rainy weather recognition from in-vehicle camera images for driver assistance , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[155]  Mohan M. Trivedi,et al.  On the Roles of Eye Gaze and Head Dynamics in Predicting Driver's Intent to Change Lanes , 2009, IEEE Transactions on Intelligent Transportation Systems.

[156]  I. Parra,et al.  Automatic LightBeam Controller for driver assistance , 2011, Machine Vision and Applications.

[157]  M. Amaç Güvensan,et al.  Driver Behavior Analysis for Safe Driving: A Survey , 2015, IEEE Transactions on Intelligent Transportation Systems.

[158]  A. Fascioli,et al.  Pedestrian Protection Systems : Issues , Survey , and Challenges , 2007 .

[159]  José Eugenio Naranjo,et al.  Vulnerable Road Users Detection Using V2X Communications , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[160]  Yan Song Wang,et al.  A sound quality model for objective synthesis evaluation of vehicle interior noise based on artificial neural network , 2014 .

[161]  Eiichi Yasuda,et al.  Evaluation of riding comfort: From the viewpoint of interaction of human body and seat for static, dynamic, long time driving , 2000 .

[162]  Lorenzo Torresani,et al.  CarSafe: a driver safety app that detects dangerous driving behavior using dual-cameras on smartphones , 2012, UbiComp.

[163]  Luc Van Gool,et al.  Multi-view traffic sign detection, recognition, and 3D localisation , 2014, 2009 Workshop on Applications of Computer Vision (WACV).

[164]  J. Chris Forsythe,et al.  Supervised machine learning for modeling human recognition of vehicle-driving situations , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.