15.3.1.6.4 Driver Modeling, Behavior Models, Analysis

Hu, J.S., Cheng, C.C., Liu, W.H.,
Robust Speaker's Location Detection in a Vehicle Environment Using GMM Models,
SMC-B(36), No. 2, April 2006, pp. 403-412.
IEEE DOI 0604
BibRef

Yamada, K., Kuchar, J.K.,
Preliminary study of behavioral and safety effects of driver dependence on a warning system in a driving simulator,
SMC-A(36), No. 3, May 2006, pp. 602-610.
IEEE DOI 0606
BibRef

McCall, J.C., Trivedi, M.M.,
Driver Behavior and Situation Aware Brake Assistance for Intelligent Vehicles,
PIEEE(95), No. 2, February 2007, pp. 374-387.
IEEE DOI 0702
BibRef

Cheng, H., Zheng, N., Zhang, X., Qin, J., van de Wetering, H.,
Interactive Road Situation Analysis for Driver Assistance and Safety Warning Systems: Framework and Algorithms,
ITS(8), No. 1, March 2007, pp. 157-167.
IEEE DOI 0703
BibRef

Li, Z.H.[Zhi-Heng], Li, L.[Li], Zhang, Y.[Yi],
IVS 09: Future Research in Vehicle Vision Systems,
IEEE_Int_Sys(24), No. 6, November-December 2009, pp. 62-65.
IEEE DOI 1001
BibRef

Amditis, A., Bertolazzi, E., Bimpas, M., Biral, F., Bosetti, P., da Lio, M., Danielsson, L., Gallione, A., Lind, H., Saroldi, A., Sjogren, A.,
A Holistic Approach to the Integration of Safety Applications: The INSAFES Subproject Within the European Framework Programme 6 Integrating Project PReVENT,
ITS(11), No. 3, September 2010, pp. 554-566.
IEEE DOI 1003
BibRef

Amditis, A., da Lio, M., Goudy, R.,
Guest Editorial Special Section on ITS and Road Safety,
ITS(11), No. 3, September 2010, pp. 524-524.
IEEE DOI 1003
Special section introduction. BibRef

Zhang, Y.[Yilu], Lin, W.C., Chin, Y.K.S.,
A Pattern-Recognition Approach for Driving Skill Characterization,
ITS(11), No. 4, December 2010, pp. 905-916.
IEEE DOI 1101
BibRef

Boyle, R.[Rebecca],
How Intelligent Cars Will Make Driving Easier and Greener,
PopSci(News), July 18, 2011.
WWW Link. A new generation of smarter-car technology is helping drivers and cars manage trips more efficiently, preventing gridlock, avoiding wrecks and ameliorating 5 p.m. road rage BibRef 1107

Riener, A.,
Subliminal Persuasion and Its Potential for Driver Behavior Adaptation,
ITS(13), No. 1, March 2012, pp. 71-80.
IEEE DOI 1203
BibRef

Fuller, H.J.A., Reed, M.P., Liu, Y.[Yili],
Integration of Physical and Cognitive Human Models to Simulate Driving With a Secondary In-Vehicle Task,
ITS(13), No. 2, June 2012, pp. 967-972.
IEEE DOI 1206
BibRef

Takeda, K., Miyajima, C., Suzuki, T., Angkititrakul, P., Kurumida, K., Kuroyanagi, Y., Ishikawa, H., Terashima, R., Wakita, T., Oikawa, M., Komada, Y.,
Self-Coaching System Based on Recorded Driving Data: Learning From One's Experiences,
ITS(13), No. 4, December 2012, pp. 1821-1831.
IEEE DOI 1212
BibRef

Windridge, D., Shaukat, A., Hollnagel, E.,
Characterizing Driver Intention via Hierarchical Perception-Action Modeling,
HMS(43), No. 1, January 2013, pp. 17-31.
IEEE DOI 1301
BibRef

Okuda, H., Ikami, N., Suzuki, T., Tazaki, Y., Takeda, K.,
Modeling and Analysis of Driving Behavior Based on a Probability-Weighted ARX Model,
ITS(14), No. 1, March 2013, pp. 98-112.
IEEE DOI 1303
BibRef

Berthoz, A., Bles, W., Bulthoff, H.H., Correia Gracio, B.J., Feenstra, P., Filliard, N., Huhne, R., Kemeny, A., Mayrhofer, M., Mulder, M., Nusseck, H.G., Pretto, P., Reymond, G., Schlusselberger, R., Schwandtner, J., Teufel, H., Vailleau, B., van Paassen, M.M., Vidal, M., Wentink, M.,
Motion Scaling for High-Performance Driving Simulators,
HMS(43), No. 3, May 2013, pp. 265-276.
IEEE DOI 1305
BibRef

Malikopoulos, A.A., Aguilar, J.P.,
An Optimization Framework for Driver Feedback Systems,
ITS(14), No. 2, 2013, pp. 955-964.
IEEE DOI 1307
automotive electronics; fuel economy; road traffic BibRef

Schiebi, C., Fricke, N., Staubach, M.,
Identification and analysis of motives for eco-friendly driving within the eco-move project,
IET-ITS(7), No. 1, 2013, pp. 46-54.
DOI Link 1307
BibRef

Gouy, M., Diels, C., Reed, N., Stevens, A., Burnett, G.,
Do drivers reduce their headway to a lead vehicle because of the presence of platoons in traffic? a conformity study conducted within a simulator,
IET-ITS(7), No. 2, 2013, pp. -.
DOI Link 1307
BibRef

Moreno, A.T., Garcia, A., Camacho-Torregrosa, F.J., Llorca, C.,
Influence of highway three-dimensional coordination on drivers perception of horizontal curvature and available sight distance,
IET-ITS(7), No. 2, 2013, pp. -.
DOI Link 1307
BibRef

Xiong, C., Zhang, L.,
A Descriptive Bayesian Approach to Modeling and Calibrating Drivers' En Route Diversion Behavior,
ITS(14), No. 4, 2013, pp. 1817-1824.
IEEE DOI 1312
Bayes methods BibRef

Geyer, S., Baltzer, M., Franz, B., Hakuli, S., Kauer, M., Kienle, M., Meier, S., Weissgerber, T., Bengler, K., Bruder, R., Flemisch, F., Winner, H.,
Concept and development of a unified ontology for generating test and use-case catalogues for assisted and automated vehicle guidance,
IET-ITS(8), No. 3, May 2014, pp. 183-189.
DOI Link 1407
BibRef

Ho, C., Gray, R., Spence, C.,
To What Extent do the Findings of Laboratory-Based Spatial Attention Research Apply to the Real-World Setting of Driving?,
HMS(44), No. 4, August 2014, pp. 524-530.
IEEE DOI 1407
Accidents BibRef

Birrell, S.A., Fowkes, M., Jennings, P.A.,
Effect of Using an In-Vehicle Smart Driving Aid on Real-World Driver Performance,
ITS(15), No. 4, August 2014, pp. 1801-1810.
IEEE DOI 1410
driver information systems BibRef

Stahl, P., Donmez, B., Jamieson, G.A.,
Anticipation in Driving: The Role of Experience in the Efficacy of Pre-event Conflict Cues,
HMS(44), No. 5, October 2014, pp. 603-613.
IEEE DOI 1411
biomedical measurement BibRef

Qu, T., Chen, H., Cao, D., Guo, H., Gao, B.,
Switching-Based Stochastic Model Predictive Control Approach for Modeling Driver Steering Skill,
ITS(16), No. 1, February 2015, pp. 365-375.
IEEE DOI 1502
Force BibRef

Fussl, E., Oberlader, M., Beanland, V., Spyropoulou, I., Lenne, M.G., Joshi, S., Roßger, L., Leden, L., Underwood, G., Carvalhais, J.,
Methodological development of a specific tool for assessing acceptability of assistive systems of powered two-wheeler-riders,
IET-ITS(9), No. 1, 2015, pp. 12-21.
DOI Link 1503
driver information systems BibRef

Guo, W.H.[Wei-Hong], Blythe, P.T., Edwards, S., Pavkova, K., Brennan, D.,
Effect of intelligent speed adaptation technology on older drivers: driving performance,
IET-ITS(9), No. 3, 2015, pp. 343-350.
DOI Link 1506
automated highways BibRef

Spyropoulou, I., Antoniou, C.,
Determinants of driver response to variable message sign information in Athens,
IET-ITS(9), No. 4, 2015, pp. 453-466.
DOI Link 1506
behavioural sciences computing BibRef

He, Y.[Yi], Yan, X.P.[Xin-Ping], Wu, C.Z.[Chao-Zhong], Zhong, M.[Ming], Chu, D.F.[Duan-Feng], Huang, Z.[Zhen], Wang, X.[Xu],
Evaluation of the effectiveness of auditory speeding warnings for commercial passenger vehicles: A field study in Wuhan, China,
IET-ITS(9), No. 4, 2015, pp. 467-476.
DOI Link 1506
alarm systems BibRef

Yang, Y.[Yan], Wong, A., McDonald, M.,
Does gender make a difference to performing in-vehicle tasks?,
IET-ITS(9), No. 4, 2015, pp. 359-365.
DOI Link 1506
Driver evaluation. behavioural sciences computing BibRef

Lanata, A., Valenza, G., Greco, A., Gentili, C., Bartolozzi, R., Bucchi, F., Frendo, F., Scilingo, E.P.,
How the Autonomic Nervous System and Driving Style Change With Incremental Stressing Conditions During Simulated Driving,
ITS(16), No. 3, June 2015, pp. 1505-1517.
IEEE DOI 1506
Biomedical monitoring BibRef

Wang, J.Q.[Jian-Qiang], Xiong, C.[Chenfeng], Lu, M.[Meng], Li, K.Q.[Ke-Qiang],
Longitudinal driving behaviour on different roadway categories: An instrumented-vehicle experiment, data collection and case study in China,
IET-ITS(9), No. 5, 2015, pp. 555-563.
DOI Link 1507
driver information systems BibRef

Malik, H., Larue, G.S., Rakotonirainy, A., Maire, F.,
Fuzzy Logic to Evaluate Driving Maneuvers: An Integrated Approach to Improve Training,
ITS(16), No. 4, August 2015, pp. 1728-1735.
IEEE DOI 1508
Computer crashes BibRef

Oeltze, K., Schießl, C.,
Benefits and challenges of multi-driver simulator studies,
IET-ITS(9), No. 6, 2015, pp. 618-625.
DOI Link 1509
behavioural sciences computing BibRef

Haupt, J., Kahvežic-Seljubac, A., Risser, R.,
Role of driver assistance experience, system functionality, gender, age and sensation seeking in attitudes towards the safety of driver assistance systems,
IET-ITS(9), No. 7, 2015, pp. 716-726.
DOI Link 1509
driver information systems BibRef

Schindhelm, R., Schmidt, E.,
Evaluation of the tactile detection response task in a laboratory test using a surrogate driving set-up,
IET-ITS(9), No. 7, 2015, pp. 683-689.
DOI Link 1509
haptic interfaces BibRef

Valero-Mora, P.M., Pareja, I., Pons, D., Sánchez, M., Montes, S.A., Ledesma, R.D.,
Mindfulness, inattention and performance in a driving simulator,
IET-ITS(9), No. 7, 2015, pp. 690-693.
DOI Link 1509
cognition BibRef

Eyssartier, C.,
Acceptability of driving an equipped vehicle with drive recorder: The impact of the context,
IET-ITS(9), No. 7, 2015, pp. 710-715.
DOI Link 1509
data recording BibRef

Xu, L.[Li], Hu, J.[Jie], Jiang, H.[Hong], Meng, W.[Wuqiang],
Establishing Style-Oriented Driver Models by Imitating Human Driving Behaviors,
ITS(16), No. 5, October 2015, pp. 2522-2530.
IEEE DOI 1511
automobile industry BibRef

Oorni, R.,
Demand for intelligent vehicle safety systems in Europe,
IET-ITS(9), No. 10, 2015, pp. 916-923.
DOI Link 1512
intelligent transportation systems BibRef

Shi, B., Xu, L., Hu, J., Tang, Y., Jiang, H., Meng, W., Liu, H.,
Evaluating Driving Styles by Normalizing Driving Behavior Based on Personalized Driver Modeling,
SMCS(45), No. 12, December 2015, pp. 1502-1508.
IEEE DOI 1512
Adaptation models BibRef

Tang, K., Zhu, S., Xu, Y., Wang, F.,
Modeling Drivers' Dynamic Decision-Making Behavior During the Phase Transition Period: An Analytical Approach Based on Hidden Markov Model Theory,
ITS(17), No. 1, January 2016, pp. 206-214.
IEEE DOI 1601
Acceleration BibRef

Dey, K.C., Yan, L., Wang, X., Wang, Y., Shen, H., Chowdhury, M., Yu, L., Qiu, C., Soundararaj, V.,
A Review of Communication, Driver Characteristics, and Controls Aspects of Cooperative Adaptive Cruise Control (CACC),
ITS(17), No. 2, February 2016, pp. 491-509.
IEEE DOI 1602
Sardis Award, Survey. Delays BibRef

Castaldo, F., Palmieri, F.A.N., Regazzoni, C.S.,
Bayesian Analysis of Behaviors and Interactions for Situation Awareness in Transportation Systems,
ITS(17), No. 2, February 2016, pp. 313-322.
IEEE DOI 1602
Analytical models BibRef

Qi, G., Du, Y., Wu, J., Hounsell, N., Jia, Y.,
What is the Appropriate Temporal Distance Range for Driving Style Analysis?,
ITS(17), No. 5, May 2016, pp. 1393-1403.
IEEE DOI 1605
Analytical models BibRef

Bender, A., Ward, J.R., Worrall, S., Moreyra, M.L., Konrad, S.G.[S. Gerling], Masson, F., Nebot, E.M.,
A Flexible System Architecture for Acquisition and Storage of Naturalistic Driving Data,
ITS(17), No. 6, June 2016, pp. 1748-1761.
IEEE DOI 1606
Computer architecture BibRef

Zeng, W., Miwa, T., Morikawa, T.,
Application of hyperpath strategy and driving experience to risk-averse navigation,
IET-ITS(10), No. 5, 2016, pp. 338-346.
DOI Link 1608
navigation BibRef

Wang, D., Ma, X., Ma, D., Jin, S.,
A Novel Speed-Density Relationship Model Based on the Energy Conservation Concept,
ITS(18), No. 5, May 2017, pp. 1179-1189.
IEEE DOI 1705
Automobiles, Data models, Energy conservation, Mathematical model, Psychology, Driver's psychological field, energy conservation concept, fundamental diagram, speed-density, relationship BibRef

Hansen, J.H.L., Busso, C., Zheng, Y., Sathyanarayana, A.,
Driver Modeling for Detection and Assessment of Driver Distraction: Examples from the UTDrive Test Bed,
SPMag(34), No. 4, July 2017, pp. 130-142.
IEEE DOI 1708
Autonomous vehicles, Hidden Markov models, Intelligent vehicles, Sensors, Vehicle safety, Vehicular ad hoc networks, Visualization BibRef

Zeng, X., Wang, J.,
A Stochastic Driver Pedal Behavior Model Incorporating Road Information,
HMS(47), No. 5, October 2017, pp. 614-624.
IEEE DOI 1709
Hidden Markov models, Mechanical power transmission, Predictive models, Roads, Sensors, Torque, Vehicles, Driver attention and cognition, driver modeling and state detection, hidden Markov models, intelligent vehicles, stochastic, systems BibRef

Schnelle, S., Wang, J., Su, H.J., Jagacinski, R.,
A Personalizable Driver Steering Model Capable of Predicting Driver Behaviors in Vehicle Collision Avoidance Maneuvers,
HMS(47), No. 5, October 2017, pp. 625-635.
IEEE DOI 1709
Feedforward neural networks, Mathematical model, Predictive models, Road transportation, Trajectory, Vehicles, Wheels, Collision avoidance, driver modeling and state detection, driver-automation collaboration, steering, model BibRef

Wang, W., Xi, J., Chong, A., Li, L.,
Driving Style Classification Using a Semisupervised Support Vector Machine,
HMS(47), No. 5, October 2017, pp. 650-660.
IEEE DOI 1709
Hidden Markov models, Kernel, Labeling, Optimization, Support vector machines, Training data, Vehicles, Driving style classification, longitudinal driving behavior, nonconvex optimization, quasi-Newton (QN) methods, semisupervised, support, vector, machine, (S3VM) BibRef

You, C., Lu, J., Tsiotras, P.,
Nonlinear Driver Parameter Estimation and Driver Steering Behavior Analysis for ADAS Using Field Test Data,
HMS(47), No. 5, October 2017, pp. 686-699.
IEEE DOI 1709
Data models, Hidden Markov models, Kalman filters, Roads, Vehicles, Visualization, Wheels, Extended Kalman filter (EKF), field test, parameter estimation, two-point visual driver model, unscented Kalman filter (UKF), wavelet, signal, analysis BibRef

Liu, H., Taniguchi, T., Tanaka, Y., Takenaka, K., Bando, T.,
Visualization of Driving Behavior Based on Hidden Feature Extraction by Using Deep Learning,
ITS(18), No. 9, September 2017, pp. 2477-2489.
IEEE DOI 1709
driving behavior data BibRef

Castignani, G., Derrmann, T., Frank, R., Engel, T.,
Smartphone-Based Adaptive Driving Maneuver Detection: A Large-Scale Evaluation Study,
ITS(18), No. 9, September 2017, pp. 2330-2339.
IEEE DOI 1709
distributed telematics, supervised learning, Driving maneuver detection, anomaly detection BibRef

Eboli, L.[Laura], Mazzulla, G.[Gabriella], Pungillo, G.[Giuseppe],
Measuring the driver's perception error in the traffic accident risk evaluation,
IET-ITS(11), No. 10, December 2017, pp. 659-666.
DOI Link 1711
BibRef

Ahlstrom, C., Kircher, K.,
A Generalized Method to Extract Visual Time-Sharing Sequences From Naturalistic Driving Data,
ITS(18), No. 11, November 2017, pp. 2929-2938.
IEEE DOI 1711
Data mining, Gaze tracking, Mirrors, Roads, Uncertainty, Vehicles, Visualization, Driver behaviour, glance analysis, visual, time-sharing BibRef

Gruber, T.[Thierry], Larue, G.S.[Grégoire S.], Rakotonirainy, A.[Andry], Poulsen, N.K.[Niels K.],
Developing a simulation framework for safe and optimal trajectories considering drivers' driving style,
IET-ITS(11), No. 10, December 2017, pp. 624-631.
DOI Link 1711
BibRef

Driggs-Campbell, K., Govindarajan, V., Bajcsy, R.,
Integrating Intuitive Driver Models in Autonomous Planning for Interactive Maneuvers,
ITS(18), No. 12, December 2017, pp. 3461-3472.
IEEE DOI 1712
Adaptation models, Autonomous vehicles, Planning, Predictive models, Roads, Trajectory, Autonomous vehicles, trajectory optimization BibRef

Wang, D., Pei, X., Li, L., Yao, D.,
Risky Driver Recognition Based on Vehicle Speed Time Series,
HMS(48), No. 1, February 2018, pp. 63-71.
IEEE DOI 1801
Acceleration, Accidents, Insurance, Man-machine systems, Time series analysis, Vehicles, Velocity measurement, risky driving BibRef

Liu, Y., Liu, Q., Lv, C., Zheng, M., Ji, X.,
A Study on Objective Evaluation of Vehicle Steering Comfort Based on Driver's Electromyogram and Movement Trajectory,
HMS(48), No. 1, February 2018, pp. 41-49.
IEEE DOI 1801
Electromyography, Muscles, Predictive models, Trajectory, Vehicle dynamics, Vehicles, Electromyogram (EMG), steering comfort BibRef

Bruschetta, M., Cenedese, C., Beghi, A., Maran, F.,
A Motion Cueing Algorithm With Look-Ahead and Driver Characterization: Application to Vertical Car Dynamics,
HMS(48), No. 1, February 2018, pp. 6-16.
IEEE DOI 1801
Acceleration, Automobiles, Optimization, Real-time systems, Solid modeling, Dynamic driving simulator, virtual reality BibRef

Cleij, D., Venrooij, J., Pretto, P., Pool, D.M., Mulder, M., Bülthoff, H.H.,
Continuous Subjective Rating of Perceived Motion Incongruence During Driving Simulation,
HMS(48), No. 1, February 2018, pp. 17-29.
IEEE DOI 1801
Atmospheric measurements, Current measurement, Motion measurement, Particle measurements, virtual reality BibRef

Arbabzadeh, N., Jafari, M.,
A Data-Driven Approach for Driving Safety Risk Prediction Using Driver Behavior and Roadway Information Data,
ITS(19), No. 2, February 2018, pp. 446-460.
IEEE DOI 1802
Computer crashes, Data models, Predictive models, Real-time systems, Safety, Vehicle crash testing, Vehicles, regularized multinomial logistic regression BibRef

Alonso, I.P.[I. Parra], Gonzalo, R.I.[R. Izquierdo], Alonso, J., Garcia-Morcillo, Á., Fernandez-Llorca, D., Sotelo, M.Á.,
The Experience of DRIVERTIVE-DRIVERless cooperaTIve VEhicle-Team in the 2016 GCDC,
ITS(19), No. 4, April 2018, pp. 1322-1334.
IEEE DOI 1804
Acceleration, Automation, Automobiles, Brakes, DC motors, Merging, Cooperative systems, automatization, autonomous vehicles, control BibRef

Dolk, V., Ouden, J.d., Steeghs, S., Devanesan, J.G., Badshah, I., Sudhakaran, A., Elferink, K., Chakraborty, D.,
Cooperative Automated Driving for Various Traffic Scenarios: Experimental Validation in the GCDC 2016,
ITS(19), No. 4, April 2018, pp. 1308-1321.
IEEE DOI 1804
Computer architecture, Hardware, Merging, Protocols, Roads, Cooperative automated driving, GCDC, automated highway systems, vehicle platooning BibRef

Benderius, O., Berger, C., Lundgren, V.M.[V. Malmsten],
The Best Rated Human-Machine Interface Design for Autonomous Vehicles in the 2016 Grand Cooperative Driving Challenge,
ITS(19), No. 4, April 2018, pp. 1302-1307.
IEEE DOI 1804
Autonomous vehicles, Bars, Legislation, Roads, Safety, Wheels, Human-machine interface, autonomous autonomous, external interface BibRef

Kolekar, S., Mugge, W., Abbink, D.,
Modeling Intradriver Steering Variability Based on Sensorimotor Control Theories,
HMS(48), No. 3, June 2018, pp. 291-303.
IEEE DOI 1805
Adaptation models, Mathematical model, Muscles, Roads, Task analysis, Torque, Vehicles, Intradriver variability (IDV), stochastic optimal feedback BibRef

Tanaka, T.[Takahiro], Fujikake, K.[Kazuhiro], Yonekawa, T.[Takashi], Yamagishi, M.[Misako], Inagami, M.[Makoto], Kinoshita, F.[Fumiya], Aoki, H.[Hirofumi], Kanamori, H.[Hitoshi],
Study on Driver Agent Based on Analysis of Driving Instruction Data: Driver Agent for Encouraging Safe Driving Behavior (1),
IEICE(E101-D), No. 5, May 2018, pp. 1401-1409.
WWW Link. 1805
BibRef

Cunningham, M.L.[Mitchell L.], Regan, M.A.[Michael A.],
Driver distraction and inattention in the realm of automated driving,
IET-ITS(12), No. 6, August 2018, pp. 407-413.
DOI Link 1807
BibRef

Large, D.R.[David R.], Burnett, G.[Gary], Antrobus, V.[Vicki], Skrypchuk, L.[Lee],
Driven to discussion: engaging drivers in conversation with a digital assistant as a countermeasure to passive task-related fatigue,
IET-ITS(12), No. 6, August 2018, pp. 420-426.
DOI Link 1807
BibRef

Eren, A.L.[Ayse Leyla], Burnett, G.[Gary], Large, D.R.[David R.], Harvey, C.[Catherine],
Understanding the effects of peripheral vision and muscle memory on in-vehicle touchscreen interactions,
IET-ITS(12), No. 6, August 2018, pp. 434-439.
DOI Link 1807
BibRef

Engström, J.[Johan], Markkula, G.[Gustav], Xue, Q.W.[Qing-Wan], Merat, N.[Natasha],
Simulating the effect of cognitive load on braking responses in lead vehicle braking scenarios,
IET-ITS(12), No. 6, August 2018, pp. 427-433.
DOI Link 1807
BibRef

Petzoldt, T.[Tibor], Schleinitz, K.[Katja], Banse, R.[Rainer],
Potential safety effects of a frontal brake light for motor vehicles,
IET-ITS(12), No. 6, August 2018, pp. 449-453.
DOI Link 1807
BibRef

Liu, L.[Lei], Gao, Y.[Yan], Wang, F.C.[Fu-Cheng],
Road safety analysis for high-speed vehicle in complex environments based on the viability kernel,
IET-ITS(12), No. 6, August 2018, pp. 495-503.
DOI Link 1807
BibRef

da Lio, M., Mazzalai, A., Gurney, K., Saroldi, A.,
Biologically Guided Driver Modeling: the Stop Behavior of Human Car Drivers,
ITS(19), No. 8, August 2018, pp. 2454-2469.
IEEE DOI 1808
Biological system modeling, Brakes, Hidden Markov models, Automobiles, Cognitive science, Driver modeling, motor primitives BibRef

Li, Z., Bao, S., Kolmanovsky, I.V., Yin, X.,
Visual-Manual Distraction Detection Using Driving Performance Indicators With Naturalistic Driving Data,
ITS(19), No. 8, August 2018, pp. 2528-2535.
IEEE DOI 1808
Vehicles, Measurement, Wheels, Entropy, Safety, Kinematics, Distraction detection, driver modeling, support vector machines BibRef

Deng, T., Yan, H., Li, Y.,
Learning to Boost Bottom-Up Fixation Prediction in Driving Environments via Random Forest,
ITS(19), No. 9, September 2018, pp. 3059-3067.
IEEE DOI 1809
Predictive models, Vehicles, Visualization, Computational modeling, Feature extraction, Image color analysis, Vegetation, traffic driving environments BibRef

Liao, Y.[Yuan], Wang, M.J.[Min-Juan], Duan, L.[Lian], Chen, F.[Fang],
Cross-regional driver-vehicle interaction design: an interview study on driving risk perceptions, decisions, and ADAS function preferences,
IET-ITS(12), No. 8, October 2018, pp. 801-808.
DOI Link 1809
BibRef

Khan, W.[Waqar], Klette, R.[Reinhard],
Accuracy of a Driver-Assistance System in a Collision Scenario,
ISVC18(251-263).
Springer DOI 1811
BibRef

Shi, B., Xu, L., Meng, W.,
Applying a WNN-HMM Based Driver Model in Human Driver Simulation: Method and Test,
ITS(19), No. 11, November 2018, pp. 3431-3438.
IEEE DOI 1812
driver information systems, fuel economy, hidden Markov models, neural nets, road vehicles, steering systems, three-term control, neural network BibRef

Sun, D.H.[Di-Hua], He, Y.C.[Yu-Chu], Zhao, M.[Min], Cheng, S.L.[Sen-Lin],
Cooperative driving modelling in the vicinity of traffic signals based on intelligent driver model,
IET-ITS(12), No. 10, December 2018, pp. 1236-1242.
DOI Link 1812
BibRef

Markkula, G., Romano, R., Jamson, A.H., Pariota, L., Bean, A., Boer, E.R.,
Using Driver Control Models to Understand and Evaluate Behavioral Validity of Driving Simulators,
HMS(48), No. 6, December 2018, pp. 592-603.
IEEE DOI 1812
closed loop systems, driver information systems, position control, road vehicles, steering systems, simulator validation BibRef

Zhu, B., Liu, Z., Zhao, J., Chen, Y., Deng, W.,
Driver Behavior Characteristics Identification Strategies Based on Bionic Intelligent Algorithms,
HMS(48), No. 6, December 2018, pp. 572-581.
IEEE DOI 1812
backpropagation, behavioural sciences computing, data acquisition, driver information systems, genetic algorithms, particle swarm optimization (PSO) BibRef

Christopoulos, S.G., Kanarachos, S., Chroneos, A.,
Learning Driver Braking Behavior Using Smartphones, Neural Networks and the Sliding Correlation Coefficient: Road Anomaly Case Study,
ITS(20), No. 1, January 2019, pp. 65-74.
IEEE DOI 1901
Smart phones, Roads, Vehicles, Acceleration, Global Positioning System, Accelerometers, Correlation, road condition BibRef

Xu, J., Shu, H., Shao, Y.,
Modeling of Driver Behavior on Trajectory-Speed Decision Making in Minor Traffic Roadways With Complex Features,
ITS(20), No. 1, January 2019, pp. 41-53.
IEEE DOI 1901
Trajectory, Roads, Integrated circuit modeling, Automobiles, Decision making, Computational modeling, Driving simulation, target trajectory BibRef

Yi, D., Su, J., Liu, C., Chen, W.,
Personalized Driver Workload Inference by Learning From Vehicle Related Measurements,
SMCS(49), No. 1, January 2019, pp. 159-168.
IEEE DOI 1901
Vehicles, Clustering algorithms, Support vector machines, Safety, Real-time systems, Adaptive systems, Temperature measurement, workload recognition BibRef

Yi, D., Su, J., Liu, C., Chen, W.,
New Driver Workload Prediction Using Clustering-Aided Approaches,
SMCS(49), No. 1, January 2019, pp. 64-70.
IEEE DOI 1901
Automobiles, Clustering algorithms, Prediction algorithms, Predictive models, Data models, Analytical models, workload inference BibRef

Han, W.[Wei], Wang, W.S.[Wen-Shuo], Li, X.H.[Xiao-Han], Xi, J.Q.A.[Jun-Qi-Ang],
Statistical-based approach for driving style recognition using Bayesian probability with kernel density estimation,
IET-ITS(13), No. 1, January 2019, pp. 22-30.
DOI Link 1901
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Eriksson, A., Petermeijer, S.M., Zimmermann, M., de Winter, J.C.F., Bengler, K.J., Stanton, N.A.,
Rolling Out the Red (and Green) Carpet: Supporting Driver Decision Making in Automation-to-Manual Transitions,
HMS(49), No. 1, February 2019, pp. 20-31.
IEEE DOI 1901
Vehicles, Automation, Visualization, Decision making, Information analysis, Task analysis, Wheels, Augmented reality, transitions of control BibRef

Fugiglando, U., Massaro, E., Santi, P., Milardo, S., Abida, K., Stahlmann, R., Netter, F., Ratti, C.,
Driving Behavior Analysis through CAN Bus Data in an Uncontrolled Environment,
ITS(20), No. 2, February 2019, pp. 737-748.
IEEE DOI 1902
Automobiles, Sensors, Real-time systems, Data collection, Accidents, Global Positioning System, Driving behavior, CAN bus, drivers segmentation BibRef

Yu, S.[Shu], Lü, L.[Lin],
Research on the influence factors of real driving cycle with statistical analysis and dynamic time warping,
IET-ITS(13), No. 2, February 2019, pp. 286-292.
DOI Link 1902
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Deng, C.[Chao], Cao, S.[Shi], Wu, C.Z.[Chao-Zhong], Lyu, N.C.[Neng-Chao],
Predicting drivers' direction sign reading reaction time using an integrated cognitive architecture,
IET-ITS(13), No. 4, April 2019, pp. 622-627.
DOI Link 1903
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Kim, H.S., Yoon, D., Shin, H.S., Park, C.H.,
Predicting the EEG Level of a Driver Based on Driving Information,
ITS(20), No. 4, April 2019, pp. 1215-1225.
IEEE DOI 1904
Electroencephalography, Vehicles, Brain modeling, Task analysis, Predictive models, Roads, Physiology, Cognitive workload, support vector machine BibRef

Jacob, J.[Johnny], Rabha, P.[Pankaj],
Driving Data Collection Framework Using Low Cost Hardware,
AutoNUE18(V:617-625).
Springer DOI 1905
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Bouhoute, A., Oucheikh, R., Boubouh, K., Berrada, I.,
Advanced Driving Behavior Analytics for an Improved Safety Assessment and Driver Fingerprinting,
ITS(20), No. 6, June 2019, pp. 2171-2184.
IEEE DOI 1906
Automobiles, Safety, Numerical models, Analytical models, Hidden Markov models, Graphical models, Driving behavior, graph-based analysis BibRef

Aliakbarian, M.S.[Mohammad Sadegh], Saleh, F.S.[Fatemeh Sadat], Salzmann, M.[Mathieu], Fernando, B.[Basura], Petersson, L.[Lars], Andersson, L.[Lars],
VIENA2: A Driving Anticipation Dataset,
ACCV18(I:449-466).
Springer DOI 1906
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Zhu, L.[Lei], Li, S.G.[Shu-Guang], Li, Y.H.[Yao-Hua], Wang, M.[Min], Zhang, C.Y.[Chen-Yang], Li, Y.Y.[Yan-Yu], Yao, J.[Jin], Ji, H.[Hao],
Analysis of braking intention based on fNIRS in driving simulation experiments,
IET-ITS(13), No. 7, July 2019, pp. 1181-1189.
DOI Link 1906
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He, X.[Xin], Xu, L.[Li], Zhang, Z.[Zhe],
Driving behaviour characterisation by using phase-space reconstruction and pre-trained convolutional neural network,
IET-ITS(13), No. 7, July 2019, pp. 1173-1180.
DOI Link 1906
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Cheng, R., Yu, W., Song, Y., Chen, D., Ma, X., Cheng, Y.,
Intelligent Safe Driving Methods Based on Hybrid Automata and Ensemble CART Algorithms for Multihigh-Speed Trains,
Cyber(49), No. 10, October 2019, pp. 3816-3826.
IEEE DOI 1907
Mathematical model, Optimization, Safety, Control systems, Public transportation, Data mining, Rails, system safety BibRef

Wang, W., Xi, J., Zhao, D.,
Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches,
ITS(20), No. 8, August 2019, pp. 2986-2998.
IEEE DOI 1908
Hidden Markov models, Vehicles, Bayes methods, Vehicle dynamics, Semantics, Standards, Mechanical engineering, Driving style, behavioral semantics BibRef

Figueredo, G.P., Agrawal, U., Mase, J.M.M., Mesgarpour, M., Wagner, C., Soria, D., Garibaldi, J.M., Siebers, P., John, R.I.,
Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom,
ITS(20), No. 9, September 2019, pp. 3324-3336.
IEEE DOI 1909
Vehicles, Telematics, Roads, Companies, Acceleration, Brakes, Meteorology, Driver profiling, driving pattern, driving habit, big data analysis BibRef

Rhie, Y.L., Lim, J.H., Yun, M.H.,
Queueing Network Based Driver Model for Varying Levels of Information Processing,
HMS(49), No. 6, December 2019, pp. 508-517.
IEEE DOI 1912
Task analysis, Queueing analysis, Computational modeling, Visualization, Information processing, Context-aware services, levels of processing (LOP) BibRef

Inder, K.[Katie], de Silva, V.[Varuna], Shi, X.[Xiyu],
Learning Control Policies of Driverless Vehicles from UAV Video Streams in Complex Urban Environments,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912
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Lu, M.Q.[Ming-Qi], Hu, Y.C.[Yao-Cong], Lu, X.B.[Xiao-Bo],
Dilated Light-Head R-CNN using tri-center loss for driving behavior recognition,
IVC(90), 2019, pp. 103800.
Elsevier DOI 1912
Driving behavior, Dilated convolution, Tri-center loss, Positive-sensitive RoI alignment BibRef

Hong, S., Lu, J., Panigrahi, S.R., Scott, J., Filev, D.P.,
An Interacting Multiple-Model-Based Algorithm for Driver Behavior Characterization Using Handling Risk,
ITS(20), No. 12, December 2019, pp. 4308-4317.
IEEE DOI 2001
Vehicles, Vehicle dynamics, Tires, Heuristic algorithms, Acceleration, Dynamics, Control systems, active safety BibRef

Xu, L., Zhang, J., Shi, B., Meng, W.,
Automating Shift-Scheduling Calibration by Using Bionic Optimization and Personalized Driver Models,
ITS(20), No. 12, December 2019, pp. 4367-4376.
IEEE DOI 2001
Calibration, Vehicles, Biological system modeling, Schedules, Optimization, Robots, Fuel economy, Calibration, shift-scheduling BibRef

Makantasis, K.[Konstantinos], Kontorinaki, M.[Maria], Nikolos, I.[Ioannis],
Deep reinforcement-learning-based driving policy for autonomous road vehicles,
IET-ITS(14), No. 1, January 2020, pp. 13-24.
DOI Link 2001
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Payalan, Y.F., Guvensan, M.A.,
Towards Next-Generation Vehicles Featuring the Vehicle Intelligence,
ITS(21), No. 1, January 2020, pp. 30-47.
IEEE DOI 2001
Next generation networking, Safety, Automobiles, Autonomous vehicles, Accidents, Self-driving, contexts and sensors for autonomous vehicle BibRef

Chen, L., Wang, Q., Lu, X., Cao, D., Wang, F.,
Learning Driving Models From Parallel End-to-End Driving Data Set,
PIEEE(108), No. 2, February 2020, pp. 262-273.
IEEE DOI 2001
Data models, Training, Adaptation models, Task analysis, Reinforcement learning, Decision making, Transforms, Data set, parallel driving BibRef

Morando, A., Victor, T., Dozza, M.,
A Bayesian Reference Model for Visual Time-Sharing Behaviour in Manual and Automated Naturalistic Driving,
ITS(21), No. 2, February 2020, pp. 803-814.
IEEE DOI 2002
Vehicles, Task analysis, Bayes methods, Manuals, Measurement, Biological system modeling, Visualization, ADAS, attention, visual behavior BibRef

Bejani, M.M., Ghatee, M.,
Convolutional Neural Network With Adaptive Regularization to Classify Driving Styles on Smartphones,
ITS(21), No. 2, February 2020, pp. 543-552.
IEEE DOI 2002
Feature extraction, Smart phones, Acceleration, Training, Vehicles, Data mining, Deep learning, driving style evaluation, over-fitting BibRef

Zhu, Q.,
Research on Road Traffic Situation Awareness System Based on Image Big Data,
IEEE_Int_Sys(35), No. 1, January 2020, pp. 18-26.
IEEE DOI 2004
Human computer interaction, Convolutional neural networks, Computational modeling, Task analysis, Big Data, situational awarenesss BibRef

van der El, K., Pool, D.M., van Paassen, M.R.M., Mulder, M.,
A Unifying Theory of Driver Perception and Steering Control on Straight and Winding Roads,
HMS(50), No. 2, April 2020, pp. 165-175.
IEEE DOI 2004
Vehicles, Roads, Visualization, Control theory, Windings, Vehicle dynamics, Task analysis, Driver steering, visual perception BibRef

Huang, X., Zhang, S., Peng, H.,
Developing Robot Driver Etiquette Based on Naturalistic Human Driving Behavior,
ITS(21), No. 4, April 2020, pp. 1393-1403.
IEEE DOI 2004
Vehicles, Acceleration, Roads, Robots, Accidents, Safety, Automated vehicles, human driving behavior, naturalistic driving data BibRef

Peng, X.S.[Xi-Shuai], Murphey, Y.L.[Yi Lu], Liu, R.R.[Rui-Rui], Li, Y.X.[Yuan-Xiang],
Driving maneuver early detection via sequence learning from vehicle signals and video images,
PR(103), 2020, pp. 107276.
Elsevier DOI 2005
Driving maneuver early detection, Deep neural networks, Sequence learning, Advanced driver assistance systems BibRef

Peng, X.S.[Xi-Shuai], Liu, R.R.[Rui-Rui], Murphey, Y.L.[Yi Lu], Stent, S.[Simon], Li, Y.X.[Yuan-Xiang],
Driving Maneuver Detection via Sequence Learning from Vehicle Signals and Video Images,
ICPR18(1265-1270)
IEEE DOI 1812
Vehicles, Feature extraction, Global Positioning System, Trajectory, Hidden Markov models, Time-domain analysis, Meters BibRef

Ouyang, Z., Niu, J., Liu, Y., Liu, X.,
An Ensemble Learning-Based Vehicle Steering Detector Using Smartphones,
ITS(21), No. 5, May 2020, pp. 1964-1975.
IEEE DOI 2005
Driving behavior, ensemble learning, pattern recognition, vehicle steering, smartphones BibRef

Deng, T., Yan, H., Qin, L., Ngo, T., Manjunath, B.S.,
How Do Drivers Allocate Their Potential Attention? Driving Fixation Prediction via Convolutional Neural Networks,
ITS(21), No. 5, May 2020, pp. 2146-2154.
IEEE DOI 2005
Videos, Predictive models, Gaze tracking, Visualization, Data models, Automobiles, Fixation prediction, visual attention, eye tracking, traffic driving BibRef

Fan, X.M.[Xin-Miao], Pan, G.F.[Gao-Feng], Mao, Y.[Yan], He, W.[Wu],
A personalized traffic simulation integrating emotion using a driving simulator,
VC(36), No. 6, June 2020, pp. 1203-1218.
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Jia, S.[Shuo], Hui, F.[Fei], Li, S.[Shining], Zhao, X.[Xiangmo], Khattak, A.J.[Asad J.],
Long short-term memory and convolutional neural network for abnormal driving behaviour recognition,
IET-ITS(14), No. 5, May 2020, pp. 306-312.
DOI Link 2005
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Duan, J.L.[Jing-Liang], Li, S.E.[Shengbo Eben], Guan, Y.[Yang], Sun, Q.[Qi], Cheng, B.[Bo],
Hierarchical reinforcement learning for self-driving decision-making without reliance on labelled driving data,
IET-ITS(14), No. 5, May 2020, pp. 297-305.
DOI Link 2005
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Spyridakos, P.D.[Panagiotis D.], Merat, N.[Natasha], Boer, E.R.[Erwin R.], Markkula, G.M.[Gustav M.],
Behavioural validity of driving simulators for prototype HMI evaluation,
IET-ITS(14), No. 6, June 2020, pp. 601-610.
DOI Link 2005
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Kashevnik, A., Lashkov, I., Gurtov, A.,
Methodology and Mobile Application for Driver Behavior Analysis and Accident Prevention,
ITS(21), No. 6, June 2020, pp. 2427-2436.
IEEE DOI 2006
Vehicles, Sensors, Smart phones, Cameras, Mobile applications, Monitoring, Safety, Driver behavior, smartphone, mobile application BibRef

Zhao, C., Wang, W., Li, S., Gong, J.,
Influence of Cut-In Maneuvers for an Autonomous Car on Surrounding Drivers: Experiment and Analysis,
ITS(21), No. 6, June 2020, pp. 2266-2276.
IEEE DOI 2006
Autonomous vehicles, Safety, Decision making, Automobiles, Accidents, Task analysis, Autonomous vehicle, cut-in behavior, human drivers, driving comfort BibRef

Pampel, S.M.[Sanna M.], Southey, T.J.R.[Thomas J.R.], Burnett, G.[Gary],
Understanding the distraction and behavioural adaptations of drivers when experiencing failures of digital side mirrors,
IET-ITS(14), No. 7, July 2020, pp. 775-782.
DOI Link 2006
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Liao, L.C.[Lyu-Chao], Chen, B.J.[Bi-Jun], Zou, F.M.[Fu-Min], Li, S.B.E.[Sheng-Bo Eben], Liu, J.R.[Jie-Rui], Wu, X.K.[Xin-Ke], Dong, N.[Ni],
Hierarchical quantitative analysis to evaluate unsafe driving behaviour from massive trajectory data,
IET-ITS(14), No. 8, August 2020, pp. 849-856.
DOI Link 2007
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Carlos, M.R., González, L.C., Wahlström, J., Ramírez, G., Martínez, F., Runger, G.,
How Smartphone Accelerometers Reveal Aggressive Driving Behavior?: The Key is the Representation,
ITS(21), No. 8, August 2020, pp. 3377-3387.
IEEE DOI 2008
Accelerometers, Sensors, Acceleration, Vehicles, Machine learning, Data models, Proposals, Driving analytics, aggressive driving, insurance telematics BibRef

Saleh, K., Hossny, M., Nahavandi, S.,
Contextual Recurrent Predictive Model for Long-Term Intent Prediction of Vulnerable Road Users,
ITS(21), No. 8, August 2020, pp. 3398-3408.
IEEE DOI 2008
Trajectory, Predictive models, Roads, Reinforcement learning, Recurrent neural networks, Forecasting, Kalman filters, reinforcement learning (IRL) BibRef

Tang, C., Xu, Z., Tomizuka, M.,
Disturbance-Observer-Based Tracking Controller for Neural Network Driving Policy Transfer,
ITS(21), No. 9, September 2020, pp. 3961-3972.
IEEE DOI 2008
Robustness, Neural networks, Autonomous vehicles, Trajectory, Task analysis, Adaptation models, Target tracking, machine learning BibRef

Li, Y.C.[Yao-Chen], Wu, X.[Xiao], Lu, D.H.[Dan-Hui], Li, L.[Ling], Liu, Y.H.[Yue-Hu], Zhu, L.[Li],
Style Transfer of Urban Road Images Using Generative Adversarial Networks With Structural Details,
MultMedMag(27), No. 3, July 2020, pp. 54-65.
IEEE DOI 2009
Driving style. Semantics, Image segmentation, Generators, Roads, Unmanned vehicles, Training data, Image reconstruction BibRef

Loiseau, P.[Paul], Boultifat, C.N.E.[Chaouki Nacer Eddine], Chevrel, P.[Philippe], Claveau, F.[Fabien], Espié, S.[Stéphane], Mars, F.[Franck],
Rider model identification: neural networks and quasi-LPV models,
IET-ITS(14), No. 10, October 2020, pp. 1259-1264.
DOI Link 2009
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Chan, T.K., Chin, C.S., Chen, H., Zhong, X.,
A Comprehensive Review of Driver Behavior Analysis Utilizing Smartphones,
ITS(21), No. 10, October 2020, pp. 4444-4475.
IEEE DOI 2010
Vehicles, Smart phones, Sensors, Monitoring, Acceleration, Electrodes, Electrocardiography, Driving behavior analysis, smartphone telematics solution BibRef

Cheng, X.[Xin], Zhou, J.M.[Jing-Mei], Zhao, X.M.[Xiang-Mo],
Safety assessment of vehicle behaviour based on the improved D-S evidence theory,
IET-ITS(14), No. 11, November 2020, pp. 1396-1402.
DOI Link 2010
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El Khatib, A., Ou, C., Karray, F.,
Driver Inattention Detection in the Context of Next-Generation Autonomous Vehicles Design: A Survey,
ITS(21), No. 11, November 2020, pp. 4483-4496.
IEEE DOI 2011
Vehicles, Automation, Task analysis, Accidents, Monitoring, Visualization, Vehicle dynamics, Autonomous vehicles, vehicle safety BibRef

Pozueco, L., Gupta, N., Pañeda, X.G., García, R., Tuero, A.G., Melendi, D., Rionda, A., Corcoba, V.,
Analysis of Driving Patterns and On-Board Feedback-Based Training for Proactive Road Safety Monitoring,
HMS(50), No. 6, December 2020, pp. 529-537.
IEEE DOI 2011
Advanced driver assistance systems, Intelligent transportation systems, Real-time systems, sociodemographic influence BibRef

Fernando, T., Denman, S., Sridharan, S., Fookes, C.,
Deep Inverse Reinforcement Learning for Behavior Prediction in Autonomous Driving: Accurate Forecasts of Vehicle Motion,
SPMag(38), No. 1, January 2021, pp. 87-96.
IEEE DOI 2012
Deep learning, Navigation, Supervised learning, Decision making, Focusing, Reinforcement learning, Autonomous vehicles BibRef

Guo, Y.Q.[Yong-Qing], Wang, X.Y.[Xiao-Yuan], Yuan, Q.[Quan], Liu, S.L.[Shan-Liang], Liu, S.J.[Shi-Jie],
Transition characteristics of driver's intentions triggered by emotional evolution in two-lane urban roads,
IET-ITS(14), No. 13, 15 December 2020, pp. 1788-1798.
DOI Link 2102
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Sharath, M.N.[Mysore N.], Velaga, N.R.[Nagendra R.], Quddus, M.A.[Mohammed A.],
2-dimensional human-like driver model for autonomous vehicles in mixed traffic,
IET-ITS(14), No. 13, 15 December 2020, pp. 1913-1922.
DOI Link 2102
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Hong, S., Lu, J., Filev, D.P.,
Driving Behavior Evaluation for Future Mobility: Application of Online Transition Probability Estimation,
ITS(22), No. 2, February 2021, pp. 782-791.
IEEE DOI 2102
Hidden Markov models, Vehicle dynamics, Vehicles, Estimation, Heuristic algorithms, Safety, Control systems, Driving behavior, future mobility BibRef

Siami, M., Naderpour, M., Lu, J.,
A Mobile Telematics Pattern Recognition Framework for Driving Behavior Extraction,
ITS(22), No. 3, March 2021, pp. 1459-1472.
IEEE DOI 2103
Telematics, Self-organizing feature maps, Clustering algorithms, Feature extraction, Pattern recognition, Unsupervised learning, unsupervised learning BibRef

Hang, P., Lv, C., Xing, Y., Huang, C., Hu, Z.,
Human-Like Decision Making for Autonomous Driving: A Noncooperative Game Theoretic Approach,
ITS(22), No. 4, April 2021, pp. 2076-2087.
IEEE DOI 2104
Decision making, Vehicles, Games, Safety, Planning, Predictive models, Nash equilibrium, Decision making, human-like, autonomous vehicle, model predictive control BibRef

Nascimento, E.R.[Erickson R.], Bajcsy, R.[Ruzena], Gregor, M.[Michal], Huang, I.[Isabella], Villegas, I.[Ismael], Kurillo, G.[Gregorij],
On the Development of an Acoustic-Driven Method to Improve Driver's Comfort Based on Deep Reinforcement Learning,
ITS(22), No. 5, May 2021, pp. 2923-2932.
IEEE DOI 2105
Vehicles, Psychoacoustics, Psychoacoustic models, Measurement, Safety, Reinforcement learning, Psychoacoustic metrics, safety BibRef

Cheng, S.[Shuai], Song, J.[Jian], Fang, S.N.[Sheng-Nan],
A Universal Control Scheme of Human-Like Steering in Multiple Driving Scenarios,
ITS(22), No. 5, May 2021, pp. 3135-3145.
IEEE DOI 2105
Safety, Vehicles, Decision making, Trajectory, Computer architecture, Process control, Wheels, Human-like steering control, driving simulator BibRef

Lee, H.[Hojin], Kim, H.[Hyoungkyun], Choi, S.[Seungmoon],
Driving Skill Modeling Using Neural Networks for Performance-Based Haptic Assistance,
HMS(51), No. 3, June 2021, pp. 198-210.
IEEE DOI 2106
Haptic interfaces, Artificial neural networks, Wheels, Torque, Brakes, Vehicles, Performance evaluation, simulated driving BibRef

Zhong, S.[Shan], Wei, M.[Meng], Gong, S.R.[Sheng-Rong], Xia, K.J.[Kai-Jian], Fu, Y.C.[Yu-Chen], Fu, Q.M.[Qi-Ming], Yin, H.S.[Hong-Sheng],
Behavior Prediction for Unmanned Driving Based on Dual Fusions of Feature and Decision,
ITS(22), No. 6, June 2021, pp. 3687-3696.
IEEE DOI 2106
Task analysis, Decision making, Trajectory, Visualization, Deep learning, Data models, Computer science, Unmanned driving, decision fusion BibRef

Chang, X.[Xin], Rong, J.[Jian], Li, H.[Haijian], Wu, Y.P.[Yi-Ping], Zhao, X.H.[Xiao-Hua],
Impact of connected vehicle environment on driving performance: A case of an extra-long tunnel scenario,
IET-ITS(15), No. 3, 2021, pp. 423-431.
DOI Link 2106
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Xing, Y.[Yang], Lv, C.[Chen], Mo, X.Y.[Xiao-Yu], Hu, Z.X.[Zhong-Xu], Huang, C.[Chao], Hang, P.[Peng],
Toward Safe and Smart Mobility: Energy-Aware Deep Learning for Driving Behavior Analysis and Prediction of Connected Vehicles,
ITS(22), No. 7, July 2021, pp. 4267-4280.
IEEE DOI 2107
Energy consumption, Trajectory, Predictive models, Connected vehicles, Acceleration, Deep learning, Energy management, deep learning BibRef

Nagahama, A.[Akihito], Saito, T.[Takahiro], Wada, T.[Takahiro], Sonoda, K.[Kohei],
Autonomous Driving Learning Preference of Collision Avoidance Maneuvers,
ITS(22), No. 9, September 2021, pp. 5624-5634.
IEEE DOI 2109
Trajectory, Mathematical model, Autonomous vehicles, Collision avoidance, Roads, Torque, Autonomous driving, authority transfer BibRef

Navarro, J.[Jordan], Allali, S.[Sarah], Cabrignac, N.[Nicolas], Cegarra, J.[Julien],
Impact of Pilot's Expertise on Selection, Use, Trust, and Acceptance of Automation,
HMS(51), No. 5, October 2021, pp. 432-441.
IEEE DOI 2109
Automation, Task analysis, Monitoring, Tools, Human factors, Batteries, Analysis of variance, Aircraft piloting, automation, expertise, workload BibRef

Cura, A.[Aslihan], Küçük, H.[Haluk], Ergen, E.[Erdem], Öksüzoglu, I.B.[Ismail Burak],
Driver Profiling Using Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) Methods,
ITS(22), No. 10, October 2021, pp. 6572-6582.
IEEE DOI 2110
Vehicles, Acceleration, Engines, Neural networks, Fuels, Road transportation, Cameras, Driver profiling, CNN, LSTM BibRef

Lyu, N.C.[Neng-Chao], Wang, Y.G.[Yu-Gang], Wu, C.Z.[Chao-Zhong], Wu, H.R.[Hao-Ran], Wen, J.Q.[Jia-Qiang],
Exploring longitudinal driving behaviour on a freeway deceleration lane using field operational test data,
IET-ITS(15), No. 11, 2021, pp. 1401-1413.
DOI Link 2110
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Ramah, S.E.[Salah-Eddine], Bouhoute, A.[Afaf], Boubouh, K.[Karim], Berrada, I.[Ismail],
One Step Further Towards Real-Time Driving Maneuver Recognition Using Phone Sensors,
ITS(22), No. 10, October 2021, pp. 6599-6611.
IEEE DOI 2110
Sensors, Vehicles, Smart phones, Gyroscopes, Accelerometers, Automobiles, Acceleration, Maneuvers recognition, normalization BibRef

Lin, C.T.[Chin-Teng], Chuang, C.H.[Chun-Hsiang], Hung, Y.C.[Yu-Chia], Fang, C.N.[Chieh-Ning], Wu, D.[Dongrui], Wang, Y.K.[Yu-Kai],
A Driving Performance Forecasting System Based on Brain Dynamic State Analysis Using 4-D Convolutional Neural Networks,
Cyber(51), No. 10, October 2021, pp. 4959-4967.
IEEE DOI 2110
Electroencephalography, Task analysis, Vehicles, Deep learning, Fatigue, Monitoring, Feature extraction, response time (RT) BibRef

Wang, X.Y.[Xiao-Yuan], Guo, Y.Q.[Yong-Qing], Bai, C.L.[Cheng-Lin], Yuan, Q.[Quan], Liu, S.L.[Shan-Liang], Han, J.Y.[Jun-Yan],
Driver's Intention Identification With the Involvement of Emotional Factors in Two-Lane Roads,
ITS(22), No. 11, November 2021, pp. 6866-6874.
IEEE DOI 2112
Vehicles, Support vector machines, Hidden Markov models, Roads, Safety, Visualization, Emotion recognition, Driver's emotion, support vector machine BibRef

Xu, D.H.[Dong-Hao], Ding, Z.Z.[Zhe-Zhang], He, X.[Xu], Zhao, H.J.[Hui-Jing], Moze, M.[Mathieu], Aioun, F.[François], Guillemard, F.[Franck],
Learning From Naturalistic Driving Data for Human-Like Autonomous Highway Driving,
ITS(22), No. 12, December 2021, pp. 7341-7354.
IEEE DOI 2112
Trajectory, Planning, Cost function, Autonomous vehicles, Robots, Automobiles, Autonomous vehicles, motion planning BibRef

Ben-Younes, H.[Hédi], Zablocki, É.[Éloi], Pérez, P.[Patrick], Cord, M.[Matthieu],
Driving behavior explanation with multi-level fusion,
PR(123), 2022, pp. 108421.
Elsevier DOI 2112
Explainable self-driving, Multi-level fusion, Cause classification, Natural language explanations, HDD, BDD-X BibRef

Li, Y.[Yi], Yu, B.[Bo], Chen, Y.[Yuren], Hu, Z.H.[Zhi-Hua],
A new theory of driver vision pressure energy field and its application in driver behaviour decision-making model,
IET-ITS(16), No. 1, 2022, pp. 1-12.
DOI Link 2112
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Lu, M.Q.[Ming-Qi], Hu, Y.[Yaocong], Lu, X.B.[Xiao-Bo],
Pose-guided model for driving behavior recognition using keypoint action learning,
SP:IC(100), 2022, pp. 116513.
Elsevier DOI 2112
Driving behavior, Pose estimation, Keypoint action, Keypoint Gating module BibRef

Seet, M.[Manuel], Harvy, J.[Jonathan], Bose, R.[Rohit], Dragomir, A.[Andrei], Bezerianos, A.[Anastasios], Thakor, N.[Nitish],
Differential Impact of Autonomous Vehicle Malfunctions on Human Trust,
ITS(23), No. 1, January 2022, pp. 548-557.
IEEE DOI 2201
Junctions, Automation, Electroencephalography, Autonomous vehicles, Monitoring, Brain modeling, Human factors, electroencephalography BibRef

Straub, T.[Tobias], Frey, M.[Michael], Gauterin, F.[Frank],
Learning From the Fleet: Map Attributes for Energetic Representation of Driving Profiles,
ITS(23), No. 1, January 2022, pp. 471-482.
IEEE DOI 2201
Computational modeling, Resistance, Predictive models, Wheels, Mechanical power transmission, Roads, Vehicle dynamics, feature extraction BibRef

Chen, H.Y.W.[Huei-Yen Winnie], Donmez, B.[Birsen],
A Naturalistic Driving Study of Feedback Timing and Financial Incentives in Promoting Speed Limit Compliance,
HMS(52), No. 1, February 2022, pp. 64-73.
IEEE DOI 2201
Real-time systems, Vehicles, Urban areas, Data collection, Visualization, Statistics, Sociology, Driver safety, speeding BibRef

Deng, Z.J.[Ze-Jian], Chu, D.F.[Duan-Feng], Wu, C.Z.[Chao-Zhong], Liu, S.D.[Shi-Dong], Sun, C.[Chen], Liu, T.[Teng], Cao, D.[Dongpu],
A Probabilistic Model for Driving-Style-Recognition-Enabled Driver Steering Behaviors,
SMCS(52), No. 3, March 2022, pp. 1838-1851.
IEEE DOI 2202
Vehicles, Acceleration, Stochastic processes, Support vector machines, Predictive models, Probabilistic logic, stochastic programming BibRef

Rasch, A.[Alexander], Dozza, M.[Marco],
Modeling Drivers' Strategy When Overtaking Cyclists in the Presence of Oncoming Traffic,
ITS(23), No. 3, March 2022, pp. 2180-2189.
IEEE DOI 2203
Vehicles, Acceleration, Safety, Predictive models, Roads, Accidents, Brakes, Advanced driver assistance systems (ADAS), overtaking BibRef

Chandra, R.[Rohan], Bera, A.[Aniket], Manocha, D.[Dinesh],
Using Graph-Theoretic Machine Learning to Predict Human Driver Behavior,
ITS(23), No. 3, March 2022, pp. 2572-2585.
IEEE DOI 2203
Vehicles, Navigation, Trajectory, Machine learning, Planning, Autonomous vehicles, Task analysis, Advanced driver assistance, network theory BibRef

Yang, L.[Liu], Li, M.[Maoying], Shen, C.Y.[Chen-Yang], Hu, Q.H.[Qing-Hua], Wen, J.[Jia], Xu, S.[Shujie],
Discriminative Transfer Learning for Driving Pattern Recognition in Unlabeled Scenes,
Cyber(52), No. 3, March 2022, pp. 1429-1442.
IEEE DOI 2203
Pattern recognition, Automotive engineering, Cybernetics, Learning systems, Data structures, Companies, transfer learning BibRef

Mullakkal-Babu, F.A.[Freddy Antony], Wang, M.[Meng], van Arem, B.[Bart], Happee, R.[Riender],
Comparative Safety Assessment of Automated Driving Strategies at Highway Merges in Mixed Traffic,
ITS(23), No. 4, April 2022, pp. 3626-3639.
IEEE DOI 2204
Safety, Accidents, Trajectory, Road transportation, Vehicle dynamics, Measurement, Predictive models, Traffic safety, tactical decisions BibRef

Imberg, H.[Henrik], Lisovskaja, V.[Vera], Selpi, Nerman, O.[Olle],
Optimization of Two-Phase Sampling Designs With Application to Naturalistic Driving Studies,
ITS(23), No. 4, April 2022, pp. 3575-3588.
IEEE DOI 2204
Databases, Automobiles, Safety, Vehicles, Brakes, Annotations, Maximum likelihood estimation, Case-control studies, unequal probability sampling BibRef

Zhang, H.[Hua], Zhang, Z.Y.[Zhi-Yuan], Liang, J.[Jun],
Dynamic driving intention recognition of vehicles with different driving styles of surrounding vehicles,
IET-ITS(16), No. 5, , pp. 571-585.
DOI Link 2204
BibRef

Wang, Z.[Zheng], Zheng, R.[Rencheng], Nacpil, E.J.C.[Edric John Cruz], Nakano, K.[Kimihiko],
Modeling and analysis of driver behaviour under shared control through weighted visual and haptic guidance,
IET-ITS(16), No. 5, 2022, pp. 648-660.
DOI Link 2204
BibRef

Liu, C.[Chao], Wang, Z.[Zheng], Nacpil, E.J.C.[Edric John Cruz], Hou, W.B.[Wen-Bin], Zheng, R.C.[Ren-Cheng],
Analysis of visual risk perception model for braking control behaviour of human drivers: A literature review,
IET-ITS(16), No. 6, 2022, pp. 711-724.
DOI Link 2205
BibRef

Ayoub, J.[Jackie], Avetisyan, L.[Lilit], Makki, M.[Mustapha], Zhou, F.[Feng],
An Investigation of Drivers' Dynamic Situational Trust in Conditionally Automated Driving,
HMS(52), No. 3, June 2022, pp. 501-511.
IEEE DOI 2205
Videos, Particle measurements, Atmospheric measurements, System performance, Vehicle dynamics, Vehicles, Uncertainty, undertrust BibRef

Xie, S.S.[Shan-Shan], Chen, S.T.[Shi-Tao], Zheng, J.Y.[Jing-Yue], Tomizuka, M.[Masayoshi], Zheng, N.N.[Nan-Ning], Wang, J.Q.[Jian-Qiang],
From Human Driving to Automated Driving: What Do We Know About Drivers?,
ITS(23), No. 7, July 2022, pp. 6189-6205.
IEEE DOI 2207
Cognition, Vehicles, Task analysis, Computer architecture, Biological neural networks, Automation, Adaptation models, human factors BibRef

Tian, Y.T.[Yan-Tao], Cao, X.H.[Xuan-Hao], Huang, K.[Kai], Fei, C.[Cong], Zheng, Z.[Zhu], Ji, X.[Xuewu],
Learning to Drive Like Human Beings: A Method Based on Deep Reinforcement Learning,
ITS(23), No. 7, July 2022, pp. 6357-6367.
IEEE DOI 2207
Autonomous vehicles, Reinforcement learning, Task analysis, Wheels, Roads, Mathematical model, Markov processes, Autonomous driving, reinforcement learning BibRef

Azadani, M.N.[Mozhgan Nasr], Boukerche, A.[Azzedine],
Driving Behavior Analysis Guidelines for Intelligent Transportation Systems,
ITS(23), No. 7, July 2022, pp. 6027-6045.
IEEE DOI 2207
Vehicles, Sensors, Smart phones, Global Positioning System, Sensor fusion, Laser radar, Automobiles, Driving style, survey BibRef

Zhang, Z.Y.[Zi-Yu], Wang, C.Y.[Chun-Yan], Zhao, W.Z.[Wan-Zhong], Xu, C.[Can], Chen, G.P.[Guo-Ping],
Driving Authority Allocation Strategy Based on Driving Authority Real-Time Allocation Domain,
ITS(23), No. 7, July 2022, pp. 8528-8543.
IEEE DOI 2207
Resource management, Vehicles, Real-time systems, Switches, Muscles, Vehicle dynamics, Man-machine systems, dynamic optimization BibRef

Li, L.[Li], Zhao, C.[Can], Wang, X.[Xiao], Li, Z.H.[Zhi-Heng], Chen, L.[Long], Lv, Y.S.[Yi-Sheng], Zheng, N.N.[Nan-Ning], Wang, F.Y.[Fei-Yue],
Three Principles to Determine the Right-of-Way for AVs: Safe Interaction With Humans,
ITS(23), No. 7, July 2022, pp. 7759-7774.
IEEE DOI 2207
Vehicles, Safety, Accidents, Sensors, Prototypes, Autonomous vehicles, Automation, Autonomous vehicles, driving safety, social interaction BibRef

Domeyer, J.E.[Joshua E.], Lee, J.D.[John D.], Toyoda, H.[Heishiro], Mehler, B.[Bruce], Reimer, B.[Bryan],
Driver-Pedestrian Perceptual Models Demonstrate Coupling: Implications for Vehicle Automation,
HMS(52), No. 4, August 2022, pp. 557-566.
IEEE DOI 2208
Vehicles, Roads, Automation, Legged locomotion, Visualization, Safety, Predictive models, Dynamic modeling, joint activity, perception, vehicle automation BibRef

He, B.T.[Bao-Tian], Li, Y.B.[Yi-Bing],
Multi-future Transformer: Learning diverse interaction modes for behaviour prediction in autonomous driving,
IET-ITS(16), No. 9, 2022, pp. 1249-1267.
DOI Link 2208
BibRef

Palaniappan, R.[Ramaswamy], Mouli, S.[Surej], Bowman, H.[Howard], McLoughlin, I.[Ian],
Investigating the Cognitive Response of Brake Lights in Initiating Braking Action Using EEG,
ITS(23), No. 8, August 2022, pp. 13878-13883.
IEEE DOI 2208
Brakes, Electroencephalography, Vehicles, Hardware, Brain modeling, Light emitting diodes, Shape, Brake light reaction time, road safety BibRef

Huang, Z.Y.[Zhi-Yu], Wu, J.D.[Jing-Da], Lv, C.[Chen],
Driving Behavior Modeling Using Naturalistic Human Driving Data With Inverse Reinforcement Learning,
ITS(23), No. 8, August 2022, pp. 10239-10251.
IEEE DOI 2208
Trajectory, Vehicles, Entropy, Decision making, Predictive models, Hidden Markov models, Task analysis, Driving behavior modeling, interaction awareness BibRef

Huang, Z.Y.[Zhi-Yu], Liu, H.C.[Hao-Chen], Wu, J.D.[Jing-Da], Lv, C.[Chen],
Conditional Predictive Behavior Planning With Inverse Reinforcement Learning for Human-Like Autonomous Driving,
ITS(24), No. 7, July 2023, pp. 7244-7258.
IEEE DOI 2307
Behavioral sciences, Trajectory, Predictive models, Planning, Proposals, Autonomous vehicles, Reinforcement learning, inverse reinforcement learning BibRef

Zhang, H.L.[Hai-Lun], Fu, R.[Rui],
An Ensemble Learning-Online Semi-Supervised Approach for Vehicle Behavior Recognition,
ITS(23), No. 8, August 2022, pp. 10610-10626.
IEEE DOI 2208
Hidden Markov models, Vehicles, Turning, Vehicle dynamics, Sensors, Deep learning, Data models, Advanced driver assistance system, vehicle behavior recognition BibRef

Yang, S.[Sen], Wang, W.[Wenshuo], Xi, J.Q.[Jun-Qiang],
Leveraging Human Driving Preferences to Predict Vehicle Speed,
ITS(23), No. 8, August 2022, pp. 11137-11147.
IEEE DOI 2208
Vehicles, Hidden Markov models, Roads, Prediction algorithms, Vehicle dynamics, Trajectory, Random variables, driving preferences BibRef

Fu, Y.C.[Yu-Chuan], Li, C.L.[Chang-Le], Yu, F.R.[F. Richard], Luan, T.H.[Tom H.], Zhang, Y.[Yao],
Hybrid Autonomous Driving Guidance Strategy Combining Deep Reinforcement Learning and Expert System,
ITS(23), No. 8, August 2022, pp. 11273-11286.
IEEE DOI 2208
Autonomous vehicles, Decision making, Blockchains, Planning, Adaptation models, Roads, Expert systems, Autonomous driving, vehicular blockchain BibRef

Fu, Y.C.[Yu-Chuan], Li, C.L.[Chang-Le], Yu, F.R.[F. Richard], Luan, T.H.[Tom H.], Zhang, Y.[Yao],
A Selective Federated Reinforcement Learning Strategy for Autonomous Driving,
ITS(24), No. 2, February 2023, pp. 1655-1668.
IEEE DOI 2302
Autonomous vehicles, Adaptation models, Reinforcement learning, Computational modeling, Data models, Training, Task analysis, knowledge aggregation BibRef

Tao, J.[Jili], Zhang, R.[Ridong],
Intelligent Feature Selection Using GA and Neural Network Optimization for Real-Time Driving Pattern Recognition,
ITS(23), No. 8, August 2022, pp. 12665-12674.
IEEE DOI 2208
Feature extraction, Pattern recognition, Neural networks, Genetic algorithms, Support vector machines, Optimization, neural network classifier BibRef

Bhattacharyya, R.[Raunak], Jung, S.[Soyeon], Kruse, L.A.[Liam A.], Senanayake, R.[Ransalu], Kochenderfer, M.J.[Mykel J.],
A Hybrid Rule-Based and Data-Driven Approach to Driver Modeling Through Particle Filtering,
ITS(23), No. 8, August 2022, pp. 13055-13068.
IEEE DOI 2208
Vehicles, Data models, Merging, Mathematical models, Autonomous vehicles, Trajectory, Task analysis, particle filters BibRef

Abdelrahman, A.E.[Abdalla Ebrahim], Hassanein, H.S.[Hossam S.], Abu-Ali, N.[Najah],
Robust Data-Driven Framework for Driver Behavior Profiling Using Supervised Machine Learning,
ITS(23), No. 4, April 2022, pp. 3336-3350.
IEEE DOI 2204
BibRef

Abdelrahman, A.E.[Abdalla Ebrahim], Hassanein, H.S.[Hossam S.], Abu-Ali, N.[Najah],
A Robust Environment-Aware Driver Profiling Framework Using Ensemble Supervised Learning,
ITS(23), No. 9, September 2022, pp. 14456-14469.
IEEE DOI 2209
Vehicles, Smart phones, Predictive models, Machine learning algorithms, Insurance, Accidents, Sensors, vehicle-to-cloud (V2C) applications. Predictive models, Hidden Markov models, Roads, Data models, Context modeling, Driver profiling, telematics BibRef

Zhu, Z.[Zeyu], Zhao, H.J.[Hui-Jing],
A Survey of Deep RL and IL for Autonomous Driving Policy Learning,
ITS(23), No. 9, September 2022, pp. 14043-14065.
IEEE DOI 2209
Task analysis, Reinforcement learning, Planning, Taxonomy, Safety, Autonomous vehicles, Uncertainty, Deep reinforcement learning, autonomous driving policy BibRef

Zekany, S.A.[Stephen A.], Larsen, T.F.[Thomas F.], Dreslinski, R.G.[Ronald G.], Wenisch, T.F.[Thomas F.],
Finding and Indexing Vehicle Maneuvers From Dashboard Camera Video,
ITS(23), No. 9, September 2022, pp. 16098-16109.
IEEE DOI 2209
Cameras, Deep learning, Trajectory, Global Positioning System, Vehicle dynamics, Visual odometry, Roads, Intelligent systems, software algorithms BibRef

Lu, C.[Chao], Lv, C.[Chen], Gong, J.W.[Jian-Wei], Wang, W.[Wenshuo], Cao, D.[Dongpu], Wang, F.Y.[Fei-Yue],
Instance-Level Knowledge Transfer for Data-Driven Driver Model Adaptation With Homogeneous Domains,
ITS(23), No. 10, October 2022, pp. 17015-17026.
IEEE DOI 2210
Vehicles, Adaptation models, Data models, Hidden Markov models, Knowledge transfer, Transfer learning, Training, Driver behaviour, importance weight BibRef

Azadani, M.N.[Mozhgan Nasr], Boukerche, A.[Azzedine],
Siamese Temporal Convolutional Networks for Driver Identification Using Driver Steering Behavior Analysis,
ITS(23), No. 10, October 2022, pp. 18076-18087.
IEEE DOI 2210
Vehicles, Wheels, Task analysis, Feature extraction, Brakes, Automobiles, Deep learning, Driver verification, driving behavior BibRef

Li, X.[Xueyun], Wang, Y.P.[Yi-Ping], Su, C.[Chuqi], Gong, X.[Xinle], Huang, J.[Jin], Yang, D.[Dengke],
Adaptive Authority Allocation Approach for Shared Steering Control System,
ITS(23), No. 10, October 2022, pp. 19428-19439.
IEEE DOI 2210
Vehicles, Resource management, Adaptation models, Vehicle dynamics, Control systems, Reliability, Man-machine systems, driver behavior BibRef

Szoke, L.[Laszlo], Aradi, S.[Szilárd], Bécsi, T.[Tamás], Gáspár, P.[Péter],
Skills to Drive: Successor Features for Autonomous Highway Pilot,
ITS(23), No. 10, October 2022, pp. 18707-18718.
IEEE DOI 2210
Task analysis, Road transportation, Reinforcement learning, Autonomous vehicles, Training, Technological innovation, successor features BibRef

Li, Q.[Qiang], Liu, C.S.[Chun-Sheng], Chang, F.[Faliang], Li, S.[Shuang], Liu, H.[Hui], Liu, Z.[Zehao],
Adaptive Short-Temporal Induced Aware Fusion Network for Predicting Attention Regions Like a Driver,
ITS(23), No. 10, October 2022, pp. 18695-18706.
IEEE DOI 2210
Feature extraction, Visualization, Vehicles, Task analysis, Estimation, Solid modeling, Semantics, Driver attention prediction, motion feature extraction BibRef

Zhang, K.P.[Kun-Peng], Chang, C.[Cheng], Zhong, W.Q.[Wen-Qin], Li, S.[Shen], Li, Z.H.[Zhi-Heng], Li, L.[Li],
A Systematic Solution of Human Driving Behavior Modeling and Simulation for Automated Vehicle Studies,
ITS(23), No. 11, November 2022, pp. 21944-21958.
IEEE DOI 2212
Trajectory, Predictive models, Data models, Computational modeling, Vehicles, Analytical models, Uncertainty, Automated vehicles, vehicle simulation BibRef

Wang, J.H.[Jun-Hua], Xu, W.X.[Wen-Xiang], Fu, T.[Ting], Jiang, R.[Rui],
Recognition of Trip-Based Aggressive Driving: A System Integrated With Gaussian Mixture Model Structured of Factor-Analysis, and Hierarchical Clustering,
ITS(23), No. 11, November 2022, pp. 20442-20451.
IEEE DOI 2212
Behavioral sciences, Vehicles, Data models, Clustering algorithms, Computational modeling, Machine learning algorithms, Gaussian mixture model BibRef

Seo, H.W.[Hyun-Woo], Shin, J.[Jongkyung], Kim, K.H.[Ki-Hun], Lim, C.[Chiehyeon], Bae, J.[Jungcheol],
Driving Risk Assessment Using Non-Negative Matrix Factorization With Driving Behavior Records,
ITS(23), No. 11, November 2022, pp. 20398-20412.
IEEE DOI 2212
Accidents, Risk management, Behavioral sciences, Vehicles, Transportation, Computer crashes, Injuries, non-negative matrix factorization BibRef

Jiang, Z.H.[Zhi-Han], He, X.[Xin], Lu, C.[Chenhui], Zhou, B.B.[Bin-Bin], Fan, X.L.[Xiao-Liang], Wang, C.[Cheng], Ma, X.J.[Xiao-Juan], Ngai, E.C.H.[Edith C.H.], Chen, L.B.[Long-Biao],
Understanding Drivers' Visual and Comprehension Loads in Traffic Violation Hotspots Leveraging Crowd-Based Driving Simulation,
ITS(23), No. 12, December 2022, pp. 23369-23383.
IEEE DOI 2212
Vehicles, Visualization, Load modeling, Environmental factors, Solid modeling, Point cloud compression, Traffic violation, driving simulation BibRef

Toghi, B.[Behrad], Valiente, R.[Rodolfo], Sadigh, D.[Dorsa], Pedarsani, R.[Ramtin], Fallah, Y.P.[Yaser P.],
Social Coordination and Altruism in Autonomous Driving,
ITS(23), No. 12, December 2022, pp. 24791-24804.
IEEE DOI 2212
Behavioral sciences, Vehicles, Safety, Games, Training, Navigation, Decision making, Cooperative driving, social navigation, multi-agent reinforcement learning BibRef

Eraqi, H.M.[Hesham M.], Moustafa, M.N.[Mohamed N.], Honer, J.[Jens],
Dynamic Conditional Imitation Learning for Autonomous Driving,
ITS(23), No. 12, December 2022, pp. 22988-23001.
IEEE DOI 2212
Roads, Laser radar, Autonomous vehicles, Cameras, Planning, Navigation, Meteorology, Autonomous driving, occupancy grid mapping, road blockages avoidance BibRef

Roitberg, A.[Alina], Peng, K.Y.[Kun-Yu], Schneider, D.[David], Yang, K.L.[Kai-Lun], Koulakis, M.[Marios], Martinez, M.[Manuel], Stiefelhagen, R.[Rainer],
Is My Driver Observation Model Overconfident? Input-Guided Calibration Networks for Reliable and Interpretable Confidence Estimates,
ITS(23), No. 12, December 2022, pp. 25271-25286.
IEEE DOI 2212
Vehicles, Activity recognition, Uncertainty, Reliability, Neural networks, Predictive models, Calibration, uncertainty in deep learning BibRef

Wang, R.[Rui], Xu, J.F.[Jin-Feng], Liu, J.[Jia], Wu, D.[Di], Hao, Y.X.[Yi-Xue], Li, X.Z.[Xian-Zhi], Chen, M.[Min],
TIF: Trajectory and Information Flow Coupling Mechanism for Behavior Analysis in Autonomous Driving,
ITS(23), No. 12, December 2022, pp. 25216-25225.
IEEE DOI 2212
Behavioral sciences, Sensors, Fabrics, Couplings, Trajectory, Intelligent sensors, Motion segmentation, Behavior analysis, motion pattern BibRef

Wu, C.[Chuna], Cao, J.[Jing], Du, Y.C.[Yu-Chuan],
Impacts of advanced driver assistance systems on commercial truck driver behaviour performance using naturalistic data,
IET-ITS(17), No. 1, 2023, pp. 119-128.
DOI Link 2301
BibRef

Li, D.D.[Dan-Dan], Wang, Y.[Yibo], Xu, W.[Wei],
A Deep Multichannel Network Model for Driving Behavior Risk Classification,
ITS(24), No. 1, January 2023, pp. 1204-1219.
IEEE DOI 2301
Behavioral sciences, Vehicles, Feature extraction, Accidents, Hidden Markov models, Deep learning, Data models, Driving risk, driving behavior BibRef

Yang, D.[Di], Ozbay, K.[Kaan], Gao, J.Q.[Jing-Qin], Zuo, F.[Fan],
A Functional Approach for Analyzing Time-Dependent Driver Response Behavior to Real-World Connected Vehicle Warnings,
ITS(24), No. 3, March 2023, pp. 3438-3447.
IEEE DOI 2303
Vehicles, Behavioral sciences, Safety, Accidents, Urban areas, Data models, Connected vehicles, Connected vehicles, functional data analysis BibRef

Liu, Y.Q.[Ya-Qi], Wang, X.Y.[Xiao-Yuan],
The Analysis of Driver's Behavioral Tendency Under Different Emotional States Based on a Bayesian Network,
AffCom(14), No. 1, January 2023, pp. 165-177.
IEEE DOI 2303
Vehicles, Physiology, Bayes methods, Videos, Roads, Task analysis, Safety, Vehicle operation, affective behavior, Bayesian network BibRef

Bhattacharyya, R.[Raunak], Wulfe, B.[Blake], Phillips, D.J.[Derek J.], Kuefler, A.[Alex], Morton, J.[Jeremy], Senanayake, R.[Ransalu], Kochenderfer, M.J.[Mykel J.],
Modeling Human Driving Behavior Through Generative Adversarial Imitation Learning,
ITS(24), No. 3, March 2023, pp. 2874-2887.
IEEE DOI 2303
Behavioral sciences, Cost function, Trajectory, Costs, Vehicles, Mathematical models, Computational modeling, Autonomous driving, reinforcement learning BibRef

Hu, H.Y.[Hong-Yu], Liu, J.R.[Jia-Rui], Chen, G.Y.[Guo-Ying], Zhao, Y.T.[Yu-Ting], Gao, Z.H.[Zhen-Hai], Zheng, R.C.[Ren-Cheng],
Driver Identification Using Deep Generative Model With Limited Data,
ITS(24), No. 5, May 2023, pp. 5159-5171.
IEEE DOI 2305
Vehicles, Data models, Feature extraction, Convolutional neural networks, Computational modeling, deep learning BibRef

Hu, H.Y.[Hong-Yu], Liu, J.R.[Jia-Rui], Chen, G.Y.[Guo-Ying], Zhao, Y.T.[Yu-Ting], Men, Y.Z.[Yu-Zhuo], Wang, P.[Pin],
Driver identification through vehicular CAN bus data: An ensemble deep learning approach,
IET-ITS(17), No. 5, 2023, pp. 867-877.
DOI Link 2305
BibRef

Jeon, H.[Hyeongseok], Kim, S.[Sanmin], Lee, K.[Kibeom], Kang, D.[Daejun], Choi, J.[Junwon], Kum, D.[Dongsuk],
Are Reactions to Ego Vehicles Predictable Without Data?: A Semi-Supervised Approach,
ITS(24), No. 6, June 2023, pp. 6477-6490.
IEEE DOI 2306
Trajectory, Predictive models, Task analysis, Unsupervised learning, History, Data models, Training, data deficit problem BibRef

Large, D.R.[David R.], Pampel, S.M.[Sanna M.], Merriman, S.E.[Siobhan E.], Burnett, G.[Gary],
A validation study of a fixed-based, medium fidelity driving simulator for human-machine interfaces visual distraction testing,
IET-ITS(17), No. 6, 2023, pp. 1104-1117.
DOI Link 2307
distraction testing, driving simulator, human-computer interaction, on-road, pointing, touchscreen, visual demand BibRef

Sadid, H.[Hashmatullah], Antoniou, C.[Constantinos],
Modelling and simulation of (connected) autonomous vehicles longitudinal driving behavior: A state-of-the-art,
IET-ITS(17), No. 6, 2023, pp. 1051-1071.
DOI Link 2307
automated driving and intelligent vehicles, autonomous driving, transport modeling and microsimulation BibRef

Srinivasan, A.R.[Aravinda Ramakrishnan], Lin, Y.S.[Yi-Shin], Antonello, M.[Morris], Knittel, A.[Anthony], Hasan, M.[Mohamed], Hawasly, M.[Majd], Redford, J.[John], Ramamoorthy, S.[Subramanian], Leonetti, M.[Matteo], Billington, J.[Jac], Romano, R.[Richard], Markkula, G.[Gustav],
Beyond RMSE: Do Machine-Learned Models of Road User Interaction Produce Human-Like Behavior?,
ITS(24), No. 7, July 2023, pp. 7166-7177.
IEEE DOI 2307
Behavioral sciences, Predictive models, Trajectory, Measurement, Roads, Analytical models, Merging, Machine-learned models, highway driving BibRef

Na, X.X.[Xiao-Xiang], Cole, D.J.[David J.],
Experimental Evaluation of a Game-Theoretic Human Driver Steering Control Model,
Cyber(53), No. 8, August 2023, pp. 4791-4804.
IEEE DOI 2307
Vehicles, Mathematical models, Games, Automation, Robot sensing systems, Predictive models, Axles, game theory BibRef

Yi, Y.T.[Yang-Tian], Lu, C.[Chao], Wang, B.Y.[Bo-Yang], Cheng, L.[Long], Li, Z.[Zirui], Gong, J.W.[Jian-Wei],
Fusion of Gaze and Scene Information for Driving Behaviour Recognition: A Graph-Neural-Network- Based Framework,
ITS(24), No. 8, August 2023, pp. 8109-8120.
IEEE DOI 2308
Vehicles, Feature extraction, Cameras, Optical flow, Graph neural networks, Trajectory, Reliability, Driving behaviours, data fusion BibRef

Khan, S.S.[Shehroz S.], Shen, Z.T.[Zi-Ting], Sun, H.Y.[Hao-Ying], Patel, A.[Ax], Abedi, A.[Ali],
Supervised Contrastive Learning for Detecting Anomalous Driving Behaviours from Multimodal Videos,
CRV22(16-23)
IEEE DOI 2301
Training, Visualization, Task analysis, Anomaly detection, Video recording, Tuning, Standards, driving behaviours, video anomaly detection BibRef

Han, Y.[Yuci], Yilmaz, A.[Alper],
Learning to Drive Using Sparse Imitation Reinforcement Learning,
ICPR22(3736-3742)
IEEE DOI 2212
Training, Process control, Reinforcement learning, Hybrid power systems, Complexity theory, Behavioral sciences BibRef

Zhang, Q.H.[Qi-Hang], Peng, Z.H.[Zheng-Hao], Zhou, B.[Bolei],
Learning to Drive by Watching YouTube Videos: Action-Conditioned Contrastive Policy Pretraining,
ECCV22(XXVI:111-128).
Springer DOI 2211
BibRef

Chen, D.[Dian], Krähenbühl, P.[Philipp],
Learning from All Vehicles,
CVPR22(17201-17210)
IEEE DOI 2210
Driving policy from many vehicles. Training, Navigation, Computational modeling, Cognition, Pattern recognition, Task analysis, Navigation and autonomous driving BibRef

Zhao, H.Y.[Hang-Yue], Xiao, Y.C.[Yu-Chao], Zhao, Y.Y.[Yan-Yun],
PAND: Precise Action Recognition on Naturalistic Driving,
AICity22(3290-3298)
IEEE DOI 2210
Location awareness, Urban areas, Pipelines, Object detection, Data models, Pattern recognition BibRef

Zhang, J.Y.[Jimu-Yang], Zhu, R.Z.[Rui-Zhao], Ohn-Bar, E.[Eshed],
SelfD: Self-Learning Large-Scale Driving Policies From the Web,
CVPR22(17295-17305)
IEEE DOI 2210
Training, Navigation, Annotations, Data collection, Cameras, Navigation and autonomous driving, Robot vision, Self- semi- meta- Vision applications and systems BibRef

Ding, G.C.[Guan-Chen], Han, W.W.[Wen-Wei], Wang, C.L.[Cheng-Long], Cui, M.P.[Ming-Peng], Zhou, L.[Lin], Pan, D.[Dianbo], Wang, J.Y.[Jia-Yi], Zhang, J.X.[Jun-Xi], Chen, Z.Z.[Zhen-Zhong],
A Coarse-to-Fine Boundary Localization method for Naturalistic Driving Action Recognition,
AICity22(3233-3240)
IEEE DOI 2210
Location awareness, Urban areas, Estimation, Detectors, Cameras, Behavioral sciences BibRef

Nguyen, C.[Chuong], Nguyen, N.[Ngoc], Huynh, S.[Su], Nguyen, V.[Vinh], Nguyen, S.[Son],
Learning Generalized Feature for Temporal Action Detection: Application for Natural Driving Action Recognition Challenge,
AICity22(3248-3255)
IEEE DOI 2210
Training, Urban areas, Supervised learning, Detectors, Feature extraction, Pattern recognition BibRef

Alkanat, T.[Tunc], Akdag, E.[Erkut], Bondarev, E.[Egor], de With, P.H.N.[Peter H.N.],
Density-Guided Label Smoothing for Temporal Localization of Driving Actions,
AICity22(3173-3181)
IEEE DOI 2210
Location awareness, Performance evaluation, Smoothing methods, Urban areas, Cameras, Robustness, Probability distribution BibRef

Tran, M.T.[Manh Tung], Vu, M.Q.[Minh Quan], Hoang, N.D.[Ngoc Duong], Bui, K.H.N.[Khac-Hoai Nam],
An Effective Temporal Localization Method with Multi-View 3D Action Recognition for Untrimmed Naturalistic Driving Videos,
AICity22(3167-3172)
IEEE DOI 2210
Location awareness, Training, Solid modeling, Correlation, Urban areas, X3D BibRef

Bharadhwaj, M.[Manoj], Ramadurai, G.[Gitakrishnan], Ravindran, B.[Balaraman],
Detecting Vehicles on the Edge: Knowledge Distillation to Improve Performance in Heterogeneous Road Traffic,
AICity22(3191-3197)
IEEE DOI 2210
Performance evaluation, Image edge detection, Computational modeling, Soft sensors, Roads, Detectors, Real-time systems BibRef

Greco, A.[Antonio], Rundo, L.[Leonardo], Saggese, A.[Alessia], Vento, M.[Mario], Vicinanza, A.[Antonio],
Imitation Learning for Autonomous Vehicle Driving: How Does the Representation Matter?,
CIAP22(I:15-26).
Springer DOI 2205
BibRef

Gawronski, P.[Peter], Burschka, D.[Darius],
Visual Prediction of Driver Behavior in Shared Road Areas,
ICPR21(9614-9621)
IEEE DOI 2105
Visualization, Roads, Collaboration, Cameras, Time measurement, Trajectory, Topology BibRef

Wang, S.[Shun], Zhou, F.[Fang], Chen, S.L.[Song-Lu], Yang, C.[Chun],
Recurrent Graph Convolutional Network for Skeleton-based Abnormal Driving Behavior Recognition,
DLPR20(551-565).
Springer DOI 2103
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Ameksa, M., Mousannif, H., Moatassime, H.A.[H. Al], Elassad, Z.E.A.[Z. Elamrani Abou],
Toward Flexible Data Collection of Driving Behaviour,
SmartCityApp20(33-43).
DOI Link 2012
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Gao, J.Y.[Ji-Yang], Sun, C.[Chen], Zhao, H.[Hang], Shen, Y.[Yi], Anguelov, D.[Dragomir], Li, C.C.[Cong-Cong], Schmid, C.[Cordelia],
VectorNet: Encoding HD Maps and Agent Dynamics From Vectorized Representation,
CVPR20(11522-11530)
IEEE DOI 2008
Trajectory, Roads, Vehicle dynamics, Neural networks, Semantics, Encoding, Rendering (computer graphics) BibRef

Xia, Y., Kim, J., Canny, J., Zipser, K., Canas-Bajo, T., Whitney, D.,
Periphery-Fovea Multi-Resolution Driving Model Guided by Human Attention,
WACV20(1756-1764)
IEEE DOI 2006
Visualization, Vehicles, Predictive models, Videos, Feature extraction, Computational modeling, Cameras BibRef

Li, M.G., Jiang, B., Che, Z., Shi, X., Liu, M., Meng, Y., Ye, J., Liu, Y.,
DBUS: Human Driving Behavior Understanding System,
ADW19(2436-2444)
IEEE DOI 2004
automobiles, behavioural sciences computing, driver information systems, image segmentation, neural nets, Sensor Fusion BibRef

Codevilla, F., Santana, E., Lopez, A., Gaidon, A.,
Exploring the Limitations of Behavior Cloning for Autonomous Driving,
ICCV19(9328-9337)
IEEE DOI 2004
Dataset, Driver Behavior.
WWW Link. behavioural sciences computing, learning (artificial intelligence), neural nets, Vehicle dynamics BibRef

Martin, M., Roitberg, A., Haurilet, M., Horne, M., Reiß, S., Voit, M., Stiefelhagen, R.,
Drive Act: A Multi-Modal Dataset for Fine-Grained Driver Behavior Recognition in Autonomous Vehicles,
ICCV19(2801-2810)
IEEE DOI 2004
behavioural sciences computing, feature extraction, image motion analysis, learning (artificial intelligence), Skeleton BibRef

Hong, J.[Joey], Sapp, B.[Benjamin], Philbin, J.[James],
Rules of the Road: Predicting Driving Behavior With a Convolutional Model of Semantic Interactions,
CVPR19(8446-8454).
IEEE DOI 2002
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Koetsier, C., Busch, S., Sester, M.,
Trajectory Extraction for Analysis of Unsafe Driving Behaviour,
C3MGBD19(1573-1578).
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Jegham, I.[Imen], Ben Khalifa, A.[Anouar], Alouani, I.[Ihsen], Mahjoub, M.A.[Mohamed Ali],
MDAD: A Multimodal and Multiview in-Vehicle Driver Action Dataset,
CAIP19(I:518-529).
Springer DOI 1909
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Kaushik, M.[Meha], Krishna, K.M.[K. Madhava],
Learning Driving Behaviors for Automated Cars in Unstructured Environments,
AutoNUE18(V:583-599).
Springer DOI 1905
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Maqueda, A.I.[Ana I.], Loquercio, A.[Antonio], Gallego, G.[Guillermo], García, N.[Narciso], Scaramuzza, D.[Davide],
Event-Based Vision Meets Deep Learning on Steering Prediction for Self-Driving Cars,
CVPR18(5419-5427)
IEEE DOI 1812
Cameras, Gray-scale, Task analysis, Neural networks, Feature extraction, Histograms BibRef

Brahma, P.P., Othon, A.,
Subset Replay Based Continual Learning for Scalable Improvement of Autonomous Systems,
AutoDrive18(1179-11798)
IEEE DOI 1812
Training, Task analysis, Machine learning, Computational modeling, Training data, Neural networks, Feature extraction BibRef

Bera, A., Randhavane, T., Wang, A., Manocha, D., Kubin, E., Gray, K.,
Classifying Group Emotions for Socially-Aware Autonomous Vehicle Navigation,
AutoDrive18(1152-11528)
IEEE DOI 1812
Navigation, Trajectory, Psychology, Computational modeling, Autonomous robots, Heuristic algorithms BibRef

Cheung, E., Bera, A., Manocha, D.,
Efficient and Safe Vehicle Navigation Based on Driver Behavior Classification,
AutoDrive18(1137-11377)
IEEE DOI 1812
Trajectory, Navigation, Measurement, Autonomous vehicles, Feature extraction, Automobiles BibRef

He, S., Kangin, D., Mi, Y., Pugeault, N.,
Aggregated Sparse Attention for Steering Angle Prediction,
ICPR18(2398-2403)
IEEE DOI 1812
aggregates (materials), automobiles, driver information systems, steering systems, vehicle dynamics, aggregated sparse attention, Training BibRef

Chen, Y., Wang, J., Li, J., Lu, C., Luo, Z., Xue, H., Wang, C.,
LiDAR-Video Driving Dataset: Learning Driving Policies Effectively,
CVPR18(5870-5878)
IEEE DOI 1812
Benchmark testing, Sensors, Roads, Laser radar, Autonomous vehicles BibRef

Ramanishka, V., Chen, Y., Misu, T., Saenko, K.,
Toward Driving Scene Understanding: A Dataset for Learning Driver Behavior and Causal Reasoning,
CVPR18(7699-7707)
IEEE DOI 1812
Vehicles, Task analysis, Global Positioning System, Cognition, Sensors, Cameras, Road transportation BibRef

Morita, S.[Satoru],
Visualizing Viewpoint Movement on Driving by Space Information Rendering,
ISVC18(205-214).
Springer DOI 1811
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Hecker, S.[Simon], Dai, D.X.[Deng-Xin], Van Gool, L.J.[Luc J.],
End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners,
ECCV18(VII: 449-468).
Springer DOI 1810
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Landry, S.[Steven], Wang, Y.[Yuguang], Lautala, P.[Pasi], Nelson, D.[David], Jeon, M.[Myounghoon],
Driver Behavior at Simulated Railroad Crossings,
DHM18(599-609).
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Xu, J., Chen, Y.A., Guo, K., Wang, J., Menchinelli, F., Jiang, C., Zhang, C., Shao, L.,
What has been missed for real life driving? an inspirational thinking from human innate biases,
AVSS17(1-6)
IEEE DOI 1806
cognition, cognitive systems, driver information systems, ear, eye, learning (artificial intelligence), acoustician researchers, vision researchers BibRef

Alyuz, N., Aslan, S., Healey, J., Alvarez, I.J., Esme, A.A.,
Towards Understanding Emotional Reactions of Driver-Passenger Dyads in Automated Driving,
FG18(585-592)
IEEE DOI 1806
Automation, Cameras, Data collection, Labeling, Roads, Vehicles, affective computing, automated driving, drive simulation, labeling BibRef

Budvytis, I., Sauer, P., Roddick, T., Breen, K., Cipolla, R.,
Large Scale Labelled Video Data Augmentation for Semantic Segmentation in Driving Scenarios,
CVRoads17(230-237)
IEEE DOI 1802
Autonomous vehicles, Image segmentation, Labeling, Machine learning, Semantics, Training, Uncertainty BibRef

Bautista, M.A.[Miguel A.], Fuchs, P.[Patrick], Ommer, B.[Björn],
Learning Where to Drive by Watching Others,
GCPR17(29-40).
Springer DOI 1711
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Xu, H., Gao, Y., Yu, F., Darrell, T.J.,
End-to-End Learning of Driving Models from Large-Scale Video Datasets,
CVPR17(3530-3538)
IEEE DOI 1711
Computer architecture, Data models, Motion segmentation, Predictive models, Training, Visualization BibRef

Minhas, S.[Saad], Hernández-Sabaté, A.[Aura], Ehsan, S.[Shoaib], Díaz-Chito, K.[Katerine], Leonardis, A.[Ales], López, A.M.[Antonio M.], McDonald-Maier, K.D.[Klaus D.],
LEE: A Photorealistic Virtual Environment for Assessing Driver-Vehicle Interactions in Self-driving Mode,
VARVAI16(III: 894-900).
Springer DOI 1611
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Chang, S.P.[Shao-Pin], Chien, J.T.[Jui-Ting], Wang, F.E.[Fu-En], Yang, S.D.[Shang-Da], Chen, H.T.[Hwann-Tzong], Sun, M.[Min],
Extracting Driving Behavior: Global Metric Localization from Dashcam Videos in the Wild,
CVRoads16(I: 136-148).
Springer DOI 1611
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Bradler, H., Wiegand, B.A., Mester, R.,
The Statistics of Driving Sequences -- And What We Can Learn from Them,
CVRoads15(106-114)
IEEE DOI 1602
Adaptive optics BibRef

Wakita, T.[Toshihiro], Ozawa, K.[Koji], Miyajima, C.[Chiyomi], Takeda, K.[Kazuya],
Parametric Versus Non-parametric Models of Driving Behavior Signals for Driver Identification,
AVBPA05(739).
Springer DOI 0509
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Chapter on Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following continues in
Lane Departure Detection, Lane Keeping, Lane Control Assistance, Lateral Control .