15.3.1.7 Autonomous Vehicles, Surveys, Collections, Overviews

Chapter Contents (Back)
Survey, Autonomous Vehicles. Autonomous Vehicles.
See also Autonomous Vehicle Safety, Evaluation, Analysis.

Aldebaran Robotics,
2005. WWW Link.
Vendor, Robots. The Nao robot. A robot that sees, speaks, reacts to touch and surfs the web as one story on it says.

Evolution Robotics,
2001. Visual controlled robotics.
WWW Link. Vendor, Autonomous Navigation. visual Pattern Recognition package, visual Simultaneous Localization and Mapping package.

Cloud Cap Technology,
1999.
WWW Link. Vendor, Autonomous Aircraft. A Goodrich Company.

Seegrid Corporation,
2010.
WWW Link. Vendor, Mobile Robots. Vision for mapping and control. Sells vision-guided robots for moving materials.

Flightmare,
2020.
WWW Link. Code, Drone Control. Photorealistic, customizable, and easy to use simulator for quadrotors! It is compatible with ROS, Gazebo, OpenAI Gym, and even Oculus #VR headsets. Also a set of reinforcement learning baselines for benchmarking.

DDD17: End-To-End DAVIS Driving Dataset,
2017
WWW Link. Dataset, Road Scenes. Over 12 h of a 346x260 pixel DAVIS sensor recording highway and city driving in daytime, evening, night, dry and wet weather.

Waymo Open Dataset,
2020
WWW Link. Dataset, Road Scenes. high-resolution sensor data collected by autonomous vehicles operated by the Waymo Driver in a wide variety of situations.

Cameron, S.[Stephen], Probert, P.[Penelope], (Eds.)
Advanced Guided Vehicles: Aspects of the Oxford AGV Project,
World ScientificOctober 1994. ISBN: 978-981-02-1393-0.
HTML Version. Reports on the Oxford University projects. BibRef 9410

UZH FPV Drone Racing Dataset 2.0,
2024 WWW Link.
Dataset, Visual Odometry. Dataset, SLAM. The dataset comprises dozens of real-world sequences where a quadrotor controlled in first-person view (FPV) by a professional pilot has been flown both indoors and outdoors. Each sequence contains images, IMU, and events (from an event-based camera) recorded on-board, as well as ground truth from a robotic total station or motion capture system.

UZH-FPV Drone Racing Dataset,
2019.
HTML Version. Earlier version. 28 real-world sequences where a quadrotor controlled in first-person view. 1906

See also Are We Ready for Autonomous Drone Racing? The UZH-FPV Drone Racing Dataset.

The ROad event Awareness Dataset for Autonomous Driving (ROAD),
2021
WWW Link. Dataset, Autonomous Driving. It contains 22 long-duration videos (ca 8 minutes each), ideal for continual learning research, annotated in terms of road events, defined as triplets E = (Agent, Action, Location) and represented as tubes, i.e., a series of frame-wise bounding box detections. ROAD is a large, high-quality multi-label benchmark, with 122K labelled video frames comprising 560K detection bounding boxes associated with 1.7M unique individual labels (560K agent labels, 640K action labels and 499K location labels).

DSEC: A Stereo Event Camera Dataset for Driving Scenarios,
2021.
HTML Version. CVPR 2021 competition dataset. Dataset, Stereo. Dataset, Driving. 2104
Stereo Event Camera large-scale dataset for challenging driving scenarios! DSEC features over 400GB of data including stereo VGA Prophesee event cameras, stereo RGB cameras, Velodyne lidar, and RTK-GPS, recorded in challenging high-dynamic-range, day and night, sunrise and sunset, urban and Swiss-mountain driving scenarios.

Iyengar, S.S., and Kashyap, R.L., Guest editors for
Autonomous Intelligent Machines,
Computer(22), No. 6, June 1989. A special issue. Many of the papers are not vision related and are not listed here. BibRef 8906

Masaki, I., (Ed.),
Vision-based Vehicle Guidance,
Berlin: Springer-Verlag1992, 356 pp. BibRef 9200 BookReferenced as BibRef VVGCollection of articles on the IVHS (intelligent vehicle and highway systems) work covering steering, collision avoidance, warning systems. BibRef

Kanade, T.[Takeo], Reed, M.L.[Michael L.], Weiss, L.E.[Lee E.],
New Technologies and Applications in Robotics,
CACM(37), No. 3, March 1994, pp. 58-67. Describes various systems (NAVLAB) for mobile outdoor navigation. BibRef 9403

Trivedi, M.M.[Mohan M.],
Intelligent Robotic Systems,
EST1994, pp. 226-229. Review of vision systems for robotics. BibRef 9400

Trivedi, M.M.,
Intelligent Robots: Control and Cooperation,
SPIE(2493), Orlando, FL, April 1995, pp. 139-142. BibRef 9504

Masaki, I.,
Vision-Based Mobile Robots on Highways,
AdvRob(9), No. 4, 1995, pp. 417-427. BibRef 9500

Meyrowitz, A.L., Blidberg, D.R., and Michelson, R.C.,
Autonomous Vehicles,
PIEEE(84), 1996, pp. 1145-1164. BibRef 9600

Aloimonos, Y., (Ed.),
Visual Navigation: From Biological Systems to Unmanned Ground Vehicles,
ErlbaumHillsdale, NJ, 1996. BibRef 9600

Broggi, A.,
Preface to the Special Section on Machine Vision for Intelligent Vehicles and Autonomous Robots,
EngAAI(11), No. 2, April 1998, pp. 161-162. 9807
BibRef

Bertozzi, M.[Massimo], Broggi, A.[Alberto],
Vision-Based Vehicle Guidance,
Computer(30), No. 7, July 1997, pp. 49-55. 9708
BibRef

Masaki, I.,
Machine-Vision Systems for Intelligent Transportation Systems,
IEEE_Expert(13), No. 6, November/December 1998, pp. 24-31. 9812
BibRef

Broggi, A.[Alberto], Bertozzi, M.[Massimo], Conte, G.[Gianni], Fascioli, A.[Alessandra],
Automatic Vehicle Guidance: The Experinces of the ARGO Autonomous Vehicle,
World Scientific1999, ISBN 981-02-3720-0. Survey, Vehicle Guidance. Surveys work in guidance up through the ARGO project. The ARGO vehicle drove 2000km over the Italian highway network. BibRef 9900

Broggi, A.[Alberto], Bertozzi, M.[Massimo], Fascioli, A.[Alessandra],
Architectural Issues on Vision-Based Automatic Vehicle Guidance: The Experience of the ARGO Project,
RealTimeImg(6), No. 4, August 2000, pp. 313-324. 0010

See also Vision-Based Driving Assistance. BibRef

Bertozzi, M., Broggi, A., Cellario, M., Fascioli, A., Lombardi, P., Porta, M.,
Artificial vision in road vehicles,
PIEEE(90), No. 7, July 2002, pp. 1258-1271.
IEEE DOI 0207
BibRef

Broggi, A., Dickmanns, E.D.,
Applications of computer vision to intelligent vehicles,
IVC(18), No. 5, April 2000, pp. 365-366.
Elsevier DOI 0003
BibRef

Asada, M.[Minoru], Veloso, M.M.[Manuela M.], Tambe, M.[Milind], Noda, I.[Itsuki], Kitano, H.[Hiroaki], Kraetzschmar, G.K.[Gerhard K.],
Overview of RoboCup-98,
AIMag(21), No. 1, Spring 2000, pp. 9-19. Survey of the event. The winners have articles in the issue. 0009
BibRef

Coradeschi, S.[Silvia], Karlsson, L.[Lars], Stone, P.[Peter], Balch, T.[Tucker], Kraetzschmar, G.K.[Gerhard K.], Asada, M.[Minoru],
Overview of RoboCup-99,
AIMag(21), No. 3, Fall 2000, pp. 11-18. Survey of the event. The winners have articles in the issue. 0009
BibRef

Dudek, G.[Gregory], Jenkin, M.R.M.[Michael R.M.], Milios, E.E.[Evangelos E.],
Mobile Agent Perception,
IVC(19), No. 11, September 2001, pp. 711.
Elsevier DOI Special issue introduction. 0108
BibRef

Zheng, Y.F., Yun, X.P.,
Introduction to the Special Issue on Mobile Robots,
RAMag(2), No. 1, March 1995, pp. 2-5. BibRef 9503

de Souza, G.N.[Guilherme N.], Kak, A.C.[Avinash C.],
Vision for Mobile Robot Navigation: A Survey,
PAMI(24), No. 2, February 2002, pp. 237-267.
IEEE DOI 0202
Mobile Robots. Survey, Mobile Robots. Reviews 20 years of work. BibRef

Broggi, A., Ikeuchi, K., Thorpe, C.E.,
Special issue on vision applications and technology for intelligent vehicles: part I-infrastructure,
ITS(1), No. 2, June 2000, pp. 69-71.
IEEE Abstract. 0402
BibRef

Broggi, A.,
Special issue on vision applications and technology for intelligent vehicles: Part II - vehicles,
ITS(1), No. 3, September 2000, pp. 133-134.
IEEE Abstract. 0402
BibRef

Tsotsos, J.K.[John K.], Crisman, J.D.[Jill D.],
Introduction, Vision-Based Aids for the Disabled,
IVC(16), No. 4, March 1998, pp. 223.
Elsevier DOI 0401
BibRef

Murphy, R.R., Rogers, E.,
Introduction to the Special Issue on Human-Robot Interaction,
SMC-C(34), No. 2, May 2004, pp. 101-102.
IEEE Abstract. 0407
BibRef

Burke, J.L., Murphy, R.R., Rogers, E., Lumelsky, V.J., Scholtz, J.,
Final report for the DARPA/NSF interdisciplinary study on human-robot interaction,
SMC-C(34), No. 2, May 2004, pp. 103-112.
IEEE Abstract. 0407
BibRef

Barnes, N.M., and Liu, Z.Q.[Zhi-Qiang],
Knowledge-based Vision-Guided Robots,
Physica-Verlag2002. ISBN 3-7908-1494-6. BibRef 0200

Sanz, P.J., Marin, R., Sanchez, J.S.,
Special Issue on 'Pattern Recognition for Autonomous Manipulation in Robotic Systems',
SMC-C(35), No. 1, February 2005, pp. 1-3.
IEEE Abstract. 0501
BibRef

Wang, F.Y., Mirchandani, P.B., Tang, S.,
Guest Editorial Advanced Traveler Information Systems and Vision-Based Techniques for ITS,
ITS(6), No. 1, March 2005, pp. 1-4.
IEEE Abstract. 0501
BibRef

Seetharaman, G., Lakhotia, A., Blasch, E.P.,
Unmanned Vehicles Come of Age: The DARPA Grand Challenge,
Computer(39), No. 12, December 2006, pp. 26-29.
IEEE DOI 0612
BibRef

Matthies, L.H.[Larry H.], Maimone, M.W.[Mark W.], Johnson, A.[Andrew], Cheng, Y.[Yang], Willson, R.[Reg], Villalpando, C.[Carlos], Goldberg, S.[Steve], Huertas, A.[Andres], Stein, A.[Andrew], Angelova, A.[Anelia],
Computer Vision on Mars,
IJCV(75), No. 1, October 2007, pp. 67-92.
Springer DOI 0709
Mars rovers, etc. BibRef

Di, K., Wang, J., He, S., Wu, B., Chen, W., Li, R., Matthies, L.H., Howard, A.B.,
Towards Autonomous Mars Rover Localization: Operations in 2003 MER Mission and New Developments for Future Missions,
ISPRS08(B1: 957 ff).
PDF File. 0807
BibRef

Nunes, U., Laugier, C., Trivedi, M.M.,
Guest Editorial Introducing Perception, Planning, and Navigation for Intelligent Vehicles,
ITS(10), No. 3, September 2009, pp. 375-379.
IEEE DOI 0909
Special issue intro. BibRef

Bechar, A., Meyer, J., Edan, Y.,
An Objective Function to Evaluate Performance of Human-Robot Collaboration in Target Recognition Tasks,
SMC-C(39), No. 6, November 2009, pp. 611-620.
IEEE DOI 0911
Evaluation of different levels of interaction. BibRef

Wright, A.[Alex],
Automotive Autonomy,
CACM(54), No. 7, July 2011, pp. 16-18.
DOI Link 1107
Survey article. Self-driving cars are inching closer to the assembly line, thanks to promising new projects from Google and the European Union. BibRef

Tkach, I., Bechar, A., Edan, Y.,
Switching Between Collaboration Levels in a Human-Robot Target Recognition System,
SMC-C(41), No. 6, November 2011, pp. 955-967.
IEEE DOI 1110
real-time switching. Adapt to changes in environment. BibRef

Diosi, A., Segvic, S., Remazeilles, A., Chaumette, F.,
Experimental Evaluation of Autonomous Driving Based on Visual Memory and Image-Based Visual Servoing,
ITS(12), No. 3, September 2011, pp. 870-883.
IEEE DOI 1109
BibRef

Cheng, H.[Hong],
Autonomous Intelligent Vehicles: Theory, Algorithms, and Implementation,
SpringerNew-York, 2011. ISBN: 978-1-4471-2279-1
WWW Link. Buy this book: Autonomous Intelligent Vehicles: Theory, Algorithms, and Implementation (Advances in Computer Vision and Pattern Recognition) 1111
BibRef

Ploeg, J., Shladover, S.E., Nijmeijer, H., van de Wouw, N.,
Introduction to the Special Issue on the 2011 Grand Cooperative Driving Challenge,
ITS(13), No. 3, September 2012, pp. 989-993.
IEEE DOI 1209
BibRef

Ploeg, J., Englund, C., Nijmeijer, H., Semsar-Kazerooni, E., Shladover, S.E., Voronov, A., van de Wouw, N.,
Guest Editorial Introduction to the Special Issue on the 2016 Grand Cooperative Driving Challenge,
ITS(19), No. 4, April 2018, pp. 1208-1212.
IEEE DOI 1804
BibRef

Broggi, A., Cerri, P., Debattisti, S., Laghi, M.C., Medici, P., Molinari, D., Panciroli, M., Prioletti, A.,
PROUD: Public Road Urban Driverless-Car Test,
ITS(16), No. 6, December 2015, pp. 3508-3519.
IEEE DOI 1512
Autonomous automobiles BibRef

Kirkpatrick, K.[Keith],
The Moral Challenges of Driverless Cars,
CACM(58), No. 8, August 2015, pp 19-20.
DOI Link 1507
BibRef

Greenblatt, N.A.,
Self-driving cars and the law,
Spectrum(53), No. 2, February 2016, pp. 46-51.
IEEE DOI 1603
Accidents; Autonomous automobiles; Legal aspects BibRef

Gomes, L.,
When will Google's self-driving car really be ready? It depends on where you live and what you mean by 'ready',
Spectrum(53), No. 5, May 2016, pp. 13-14.
IEEE DOI 1605
News BibRef

Li, L., Hu, D.,
Introduction to the Special Issue on Unmanned Intelligent Vehicles in China,
ITS(17), No. 7, July 2016, pp. 2020-2021.
IEEE DOI 1608
China;Special issues and sections;Unmanned aerial vehicles BibRef

Brooks, R.,
Robotic cars won't understand us, and we won't cut them much slack,
Spectrum(54), No. 8, August 2017, pp. 34-51.
IEEE DOI 1708
Survey, Autonomous Vehicles. Automobiles, Autonomous automobiles, Legged locomotion, Roads, Urban areas BibRef

Edwards, J.,
Signal Processing Improves Autonomous Vehicle Navigation Accuracy: Guidance Innovations Promise Safer and More Reliable Autonomous Vehicle Operation,
SPMag(36), No. 2, March 2019, pp. 15-18.
IEEE DOI 1903
[Special Reports] mobile robots, navigation, remotely operated vehicles, signal processing, telecommunication network reliability, Autonomous vehicles BibRef

Autonomous trucks need people,
Spectrum(56), No. 3, March 2019, pp. 21-21.
IEEE DOI 1904
[Opinion] BibRef

Zhou, Z.Q.[Zhi Quan], Sun, L.Q.[Li-Qun],
Metamorphic Testing of Driverless Cars,
CACM(62), No. 3, March 2019, pp. 61-67.
DOI Link 1906
BibRef

Authors, N.[No],
Guest Editorial: Recent Advances on Vehicle to Everything (V2X): Emerging Applications and Technologies,
IET-ITS(13), No. 6, June 2019, pp. 925-926.
DOI Link 1906
BibRef

Malone, K.M.[Kerry M.], Soekroella, A.M.G.[Aroen M.G.],
Estimating benefits of C-ITS deployment, when legacy roadside systems are present,
IET-ITS(13), No. 5, May 2019, pp. 915-924.
DOI Link 1906
BibRef

Curry, E., Sheth, A.,
Next-Generation Smart Environments: From System of Systems to Data Ecosystems,
IEEE_Int_Sys(33), No. 3, May-June 2018, pp. 69-76.
IEEE DOI 1908
Digital environments supporting smart cars, etc. BibRef

Laplante, P.[Phil],
My Mother the Car (or Why It's a Bad Idea to Give Your Car a Personality),
IT Professional(21), No. 2, 2019.
WWW Link. 1908
BibRef

AI Engineers: The autonomous-vehicle industry wants you: Cruise's AI chief, Hussein Mehanna, talks jobs, careers, and self-driving cars,
Spectrum(56), No. 09, September 2019, pp. 4-4.
IEEE DOI 1909
News item, Spectral Lines. BibRef

Regazzoni, C., Pitas, I.,
Perspectives in Autonomous Systems Research,
SPMag(36), No. 5, September 2019, pp. 148-147.
IEEE DOI 1909
[In the Spotlight Section] Autonomous systems, Sensors, Task analysis, Actuators, Statistical analysis BibRef

Winkler, S.[Stephanie], Zeaedally, S.[Sherali], Evans, K.[Katrine],
Privacy and Civilian Drone Use: The Need for Further Regulation,
SecurityPrivacy(16), No. 5, 2018.
WWW Link. 1912
Discussion of legal needs. BibRef

Xu, S.B.[Shao-Bing], Peng, H.[Huei],
Design, Analysis, and Experiments of Preview Path Tracking Control for Autonomous Vehicles,
ITS(21), No. 1, January 2020, pp. 48-58.
IEEE DOI 2001
Vehicle dynamics, Roads, Trajectory, Optimization, Heuristic algorithms, Frequency-domain analysis, vehicle dynamics control BibRef

Xu, S.B.[Shao-Bing], Peng, H.[Huei], Tang, Y.F.[Yi-Fan],
Preview Path Tracking Control With Delay Compensation for Autonomous Vehicles,
ITS(22), No. 5, May 2021, pp. 2979-2989.
IEEE DOI 2105
Delays, Stability analysis, Tracking, Vehicle dynamics, Delay effects, Roads, Control design, Autonomous vehicles, preview control BibRef

Perry, T.S.,
Here comes driverless ride sharing: Cruise unveils the origin, a fully autonomous SUV designed for app-controlled urban transportation,
Spectrum(57), No. 3, March 2020, pp. 4-4.
IEEE DOI 2003
Spectral Lines. News item. BibRef

Liu, S., Gaudiot, J.,
Autonomous vehicles lite self-driving technologies should start small, go slow,
Spectrum(57), No. 3, March 2020, pp. 36-49.
IEEE DOI 2003
BibRef

Edwards, J.,
Robotics Rolls Into High Gear With Signal Processing: A robotics revolution promises to transform global industries and services, and signal processing is at the forefront,
SPMag(37), No. 2, March 2020, pp. 10-13.
IEEE DOI 2003
[Special Reports] Drones, Robots, Robot sensing systems, Signal processing. BibRef

Zhou, W., Berrio, J.S., Worrall, S., Nebot, E.,
Automated Evaluation of Semantic Segmentation Robustness for Autonomous Driving,
ITS(21), No. 5, May 2020, pp. 1951-1963.
IEEE DOI 2005
System validation, semantic segmentation, autonomous driving BibRef

Anderson, M.,
The road ahead for self-driving cars: The AV industry has had to reset expectations, as it shifts its focus to level 4 autonomy,
Spectrum(57), No. 5, May 2020, pp. 8-9.
IEEE DOI 2005
[News] BibRef

Claussmann, L., Revilloud, M., Gruyer, D., Glaser, S.,
A Review of Motion Planning for Highway Autonomous Driving,
ITS(21), No. 5, May 2020, pp. 1826-1848.
IEEE DOI 2005
Planning, Autonomous vehicles, Roads, Automotive engineering, Automobiles, Advanced driver assistance systems, path planning BibRef

Skrickij, V.[Viktor], Šabanovic, E.[Eldar], Žuraulis, V.[Vidas],
Autonomous road vehicles: recent issues and expectations,
IET-ITS(14), No. 6, June 2020, pp. 471-479.
DOI Link 2005
BibRef

Yasuda, Y.D.V.[Yuri D. V.], Martins, L.E.G.[Luiz Eduardo G.], Cappabianco, F.A.M.[Fabio A. M.],
Autonomous Visual Navigation for Mobile Robots: A Systematic Literature Review,
Surveys(53), No. 1, February 2020, pp. xx-yy.
DOI Link 2006
Survey, Autonomous Navigation. Mobile robots, visual navigation, systematic literature review, autonomous navigation BibRef

Karam, L.J., Katupitiya, J., Milanes, V., Pitas, I., Ye, J.,
Autonomous Driving: Part 1-Sensing and Perception,
SPMag(37), No. 4, July 2020, pp. 11-13.
IEEE DOI 2007
[From the Guest Editors] Special issue and sections, Autonomous vehicles, Laser radar, Market research, Ultrasonic imaging, Safety BibRef

Heath, R.W.,
Communications and Sensing: An Opportunity for Automotive Systems,
SPMag(37), No. 4, July 2020, pp. 3-13.
IEEE DOI 2007
[From the Editor] BibRef

Janai, J.[Joel], Güney, F.[Fatma], Behl, A.[Aseem], Geiger, A.[Andreas],
Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art,
FTCGV(12), No. 1-3, 2020, pp. 1-308.
DOI Link 2007
Survey, Autonomous Vehicles. BibRef

Abdulsattar, H.[Harith], Siam, M.R.K.[Mohammad Rayeedul Kalam], Wang, H.Z.[Hai-Zhong],
Characterisation of the impacts of autonomous driving on highway capacity in a mixed traffic environment: an agent-based approach,
IET-ITS(14), No. 9, September 2020, pp. 1132-1141.
DOI Link 2008
BibRef

Thomas, E.[Elena], McCrudden, C.[Connie], Wharton, Z.[Zachary], Behera, A.[Ardhendu],
Perception of autonomous vehicles by the modern society: a survey,
IET-ITS(14), No. 10, October 2020, pp. 1228-1239.
DOI Link 2009
BibRef

Cusumano, M.A.[Michael A.],
Self-Driving Vehicle Technology: Progress and Promises,
CACM(63), No. 10, October 2020, pp. 20-22.
DOI Link 2009
Review, news item. BibRef

Marcano, M., Díaz, S., Pérez, J., Irigoyen, E.,
A Review of Shared Control for Automated Vehicles: Theory and Applications,
HMS(50), No. 6, December 2020, pp. 475-491.
IEEE DOI 2011
Cooperative systems, Automation, Haptic interfaces, Advanced driver assistance systems, Human-robot interaction, shared control BibRef

Fischer, C.[Colin], Sester, M.[Monika], Schön, S.[Steffen],
Spatio-Temporal Research Data Infrastructure in the Context of Autonomous Driving,
IJGI(9), No. 11, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Bar, A., Lohdefink, J., Kapoor, N., Varghese, S.J., Huger, F., Schlicht, P., Fingscheidt, T.,
The Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing,
SPMag(38), No. 1, January 2021, pp. 42-52.
IEEE DOI 2012
Image segmentation, Perturbation methods, Semantics, Cameras, Sensors, Task analysis, Autonomous vehicles BibRef

Deter, D., Wang, C., Cook, A., Perry, N.K.,
Simulating the Autonomous Future: A Look at Virtual Vehicle Environments and How to Validate Simulation Using Public Data Sets,
SPMag(38), No. 1, January 2021, pp. 111-121.
IEEE DOI 2012
Measurement, Heuristic algorithms, Signal processing algorithms, Virtual environments, Tutorials, Tools, Vehicle dynamics BibRef

Ackerman, E.,
Robot Trucks Overtake Robot Cars: This year, trucks will drive themselves on public roads with no one on board,
Spectrum(58), No. 1, January 2021, pp. 42-43.
IEEE DOI 2101
Transportation, Companies, Autonomous automobiles, Automobiles, Autonomous vehicles BibRef

Wang, X., Zheng, X., Chen, W., Wang, F.Y.,
Visual Human-Computer Interactions for Intelligent Vehicles and Intelligent Transportation Systems: The State of the Art and Future Directions,
SMCS(51), No. 1, January 2021, pp. 253-265.
IEEE DOI 2101
Intelligent vehicles, Vehicles, Vehicle dynamics, Automation, Safety, Roads, Wheels, Augmented reality (AR), federated learning, visualization BibRef

Kuutti, S., Bowden, R., Jin, Y., Barber, P., Fallah, S.,
A Survey of Deep Learning Applications to Autonomous Vehicle Control,
ITS(22), No. 2, February 2021, pp. 712-733.
IEEE DOI 2102
Autonomous vehicles, Deep learning, Task analysis, Training, Neural networks, Sensors, Reinforcement learning, Machine learning, autonomous vehicles BibRef

Eskandarian, A., Wu, C., Sun, C.,
Research Advances and Challenges of Autonomous and Connected Ground Vehicles,
ITS(22), No. 2, February 2021, pp. 683-711.
IEEE DOI 2102
Sensor fusion, Wheels, Planning, Laser radar, Radar measurements, Connected autonomous vehicles, vehicle connectivity, vehicle control BibRef

Feng, D., Haase-Schütz, C., Rosenbaum, L., Hertlein, H., Gläser, C., Timm, F., Wiesbeck, W., Dietmayer, K.,
Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges,
ITS(22), No. 3, March 2021, pp. 1341-1360.
IEEE DOI 2103
Autonomous vehicles, Object detection, Cameras, Sensors, Laser radar, Fuses, Multi-modality, object detection, semantic segmentation, autonomous driving BibRef

Rokonuzzaman, M.[Mohammad], Mohajer, N.[Navid], Nahavandi, S.[Saeid], Mohamed, S.[Shady],
Review and performance evaluation of path tracking controllers of autonomous vehicles,
IET-ITS(15), No. 5, 2021, pp. 646-670.
DOI Link 2106
BibRef

Suchan, J.[Jakob], Bhatt, M.[Mehul], Varadarajan, S.[Srikrishna],
Commonsense visual sensemaking for autonomous driving: On generalised neurosymbolic online abduction integrating vision and semantics,
AI(299), 2021, pp. 103522.
Elsevier DOI 2108
Cognitive vision, Deep semantics, Declarative spatial reasoning, Spatial cognition and AI BibRef

Chen, R.[Rui], Arief, M.[Mansur], Zhang, W.Y.[Wei-Yang], Zhao, D.[Ding],
How to Evaluate Proving Grounds for Self-Driving? A Quantitative Approach,
ITS(22), No. 9, September 2021, pp. 5737-5748.
IEEE DOI 2109
Testing, Roads, Trajectory, Data mining, Measurement, Vehicle dynamics, Self-driving, testing, proving ground, design, unsupervised learning BibRef

Uskova, O.[Olga],
On Russian Farms, the Robotic Revolution Has Begun: Hundreds of Aftermarket AIs are Harvesting Grain,
Spectrum(58), No. 9, September 2021, pp. 40-45.
IEEE DOI 2109
Satellites, Receivers, Agricultural machinery, Rocks, Robot sensing systems, Reliability, Sun BibRef

Wu, Z.Y.[Zhong-Yi], Zhou, H.M.[Hong-Mei], Xi, H.J.[Hai-Jiao], Wu, N.[Nan],
Analysing public acceptance of autonomous buses based on an extended TAM model,
IET-ITS(15), No. 10, 2021, pp. 1318-1330.
DOI Link 2109
BibRef

Jiang, K.[Kun], Yang, D.[Diange], Wijaya, B.[Benny], Zhang, B.[Bowei], Yang, M.M.[Meng-Meng], Zhang, K.[Kai], Tang, X.W.[Xue-Wei],
Adding ears to intelligent connected vehicles by combining microphone arrays and high definition map,
IET-ITS(15), No. 10, 2021, pp. 1228-1240.
DOI Link 2109
BibRef

Li, L.[Li], Zheng, N.N.[Nan-Ning], Wang, F.Y.[Fei-Yue],
A Theoretical Foundation of Intelligence Testing and Its Application for Intelligent Vehicles,
ITS(22), No. 10, October 2021, pp. 6297-6306.
IEEE DOI 2110
Testing, Intelligent vehicles, Picture archiving and communication systems, probably approximately correct (PAC) learning BibRef

Chattopadhyay, A.[Anupam], Lam, K.Y.[Kwok-Yan], Tavva, Y.[Yaswanth],
Autonomous Vehicle: Security by Design,
ITS(22), No. 11, November 2021, pp. 7015-7029.
IEEE DOI 2112
Security, Automobiles, Sensor systems, Actuators, Standards, Autonomous vehicles, Autonomous vehicles (AV), security, sociotechnical systems BibRef

Yu, T.F.[Teng-Fei], Huang, H.[He], Jiang, N.[Nana], Acharya, T.D.[Tri Dev],
Study on Relative Accuracy and Verification Method of High-Definition Maps for Autonomous Driving,
IJGI(10), No. 11, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Thombre, S.[Sarang], Zhao, Z.[Zheng], Ramm-Schmidt, H.[Henrik], García, J.M.V.[José M. Vallet], Malkamäki, T.[Tuomo], Nikolskiy, S.[Sergey], Hammarberg, T.[Toni], Nuortie, H.[Hiski], Bhuiyan, M.Z.H.[M. Zahidul H.], Särkkä, S.[Simo], Lehtola, V.V.[Ville V.],
Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review,
ITS(23), No. 1, January 2022, pp. 64-83.
IEEE DOI 2201
Marine vehicles, Sensor systems, Artificial intelligence, Intelligent sensors, Global navigation satellite system, maritime BibRef

Mozaffari, S.[Sajjad], Al-Jarrah, O.Y.[Omar Y.], Dianati, M.[Mehrdad], Jennings, P.[Paul], Mouzakitis, A.[Alexandros],
Deep Learning-Based Vehicle Behavior Prediction for Autonomous Driving Applications: A Review,
ITS(23), No. 1, January 2022, pp. 33-47.
IEEE DOI 2201
TV, Autonomous vehicles, Sensors, Roads, History, Machine learning, Trajectory, Vehicle behaviour prediction, trajectory prediction, deep learning BibRef

Liu, S.S.[Shao-Shan], Gaudiot, J.L.[Jean-Luc],
Rise of the Autonomous Machines,
Computer(55), No. 1, January 2022, pp. 64-73.
IEEE DOI 2201
Planning, Cameras, Location awareness, Semantics, Synchronization, Software BibRef

Ross, P.E.[Philip E.],
Flying Pallets Without Pilots: A drone startup will test a radical new vision of long-range cargo transport in Europe,
Spectrum(59), No. 1, January 2022, pp. 30-31.
IEEE DOI 2201
Airplanes, Atmospheric modeling, Pallets, Drones BibRef

Tu, Y.J.T.[Yu-Ju Tony], Shang, S.S.[Shari S.], Wu, J.[Junyi],
Is Your Autonomous Vehicle as Smart as You Expected?,
CACM(65), No. 2, February 2022, pp. 31-34.
DOI Link 2202
BibRef

Bonnefon, J.F., Shariff, A., Rahwan, I.,
The social dilemma of autonomous vehicles,
Science(352), No. 6293, 2016, pp. 1573-1576.
DOI Link BibRef 1600

Aradi, S.[Szilárd],
Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles,
ITS(23), No. 2, February 2022, pp. 740-759.
IEEE DOI 2202
Planning, Autonomous vehicles, Learning (artificial intelligence), Machine learning, Trajectory, reinforcement learning BibRef

Minhas, S.[Saad], Hernández-Sabaté, A.[Aura], Ehsan, S.[Shoaib], McDonald-Maier, K.D.[Klaus D.],
Effects of Non-Driving Related Tasks During Self-Driving Mode,
ITS(23), No. 2, February 2022, pp. 1391-1399.
IEEE DOI 2202
Automobiles, Roads, Task analysis, Manuals, Autonomous vehicles, Computer crashes, Mental work capacity, virtual environment BibRef

Machida, T.[Takashi], Shitaoka, K.[Kazuya],
Test Coverage Index for ADAS/ADS Assessment Based on Various Real-World Information Points,
ITS(23), No. 2, February 2022, pp. 1443-1455.
IEEE DOI 2202
How to assess Advanced Driving Assistance System/Autonomous Driving System. Indexes, Roads, Search problems, Accidents, Sensors, Cameras, ADAS/ADS assessment, test coverage index, real world modeling, FOT BibRef

Gao, H.B.[Hong-Bo], Zhu, J.[Juping], Zhang, T.[Tong], Xie, G.[Guotao], Kan, Z.[Zhen], Hao, Z.Y.[Zheng-Yuan], Liu, K.[Kang],
Situational Assessment for Intelligent Vehicles Based on Stochastic Model and Gaussian Distributions in Typical Traffic Scenarios,
SMCS(52), No. 3, March 2022, pp. 1426-1436.
IEEE DOI 2202
Uncertainty, Stochastic processes, Risk management, Trajectory, Vehicle dynamics, Intelligent vehicles, Predictive models, uncertainty risk awareness BibRef

Kirkpatrick, K.[Keith],
Still Waiting for Self-Driving Cars,
CACM(65), No. 4, April 2022, pp. 12-14.
DOI Link 2204
News analysis of why. BibRef

Kuhn, C.B.[Christopher B.], Hofbauer, M.[Markus], Petrovic, G.[Goran], Steinbach, E.[Eckehard],
Introspective Failure Prediction for Autonomous Driving Using Late Fusion of State and Camera Information,
ITS(23), No. 5, May 2022, pp. 4445-4459.
IEEE DOI 2205
Uncertainty, Autonomous vehicles, Automobiles, Predictive models, Accidents, Data models, Estimation, Failure prediction, introspection BibRef

Kiran, B.R.[B. Ravi], Sobh, I.[Ibrahim], Talpaert, V.[Victor], Mannion, P.[Patrick], Al Sallab, A.A.[Ahmad A.], Yogamani, S.[Senthil], Pérez, P.[Patrick],
Deep Reinforcement Learning for Autonomous Driving: A Survey,
ITS(23), No. 6, June 2022, pp. 4909-4926.
IEEE DOI 2206
Reinforcement learning, Autonomous vehicles, Task analysis, Planning, Robot sensing systems, Pipelines, Decision making, safe reinforcement learning BibRef

Qian, R.[Rui], Lai, X.[Xin], Li, X.R.[Xi-Rong],
3D Object Detection for Autonomous Driving: A Survey,
PR(130), 2022, pp. 108796.
Elsevier DOI 2206
Survey, Object Detection. 3D object detection, Autonomous driving, Point clouds BibRef

Cao, Z.[Zhong], Xu, S.B.[Shao-Bing], Peng, H.[Huei], Yang, D.[Diange], Zidek, R.[Robert],
Confidence-Aware Reinforcement Learning for Self-Driving Cars,
ITS(23), No. 7, July 2022, pp. 7419-7430.
IEEE DOI 2207
Training, Reinforcement learning, Autonomous vehicles, Training data, Markov processes, Trajectory, Planning, motion planning BibRef

Zhu, B.[Bing], Zhang, P.X.[Pei-Xing], Zhao, J.[Jian], Deng, W.W.[Wei-Wen],
Hazardous Scenario Enhanced Generation for Automated Vehicle Testing Based on Optimization Searching Method,
ITS(23), No. 7, July 2022, pp. 7321-7331.
IEEE DOI 2207
Testing, Space exploration, Optimization, Security, Probability, Monte Carlo methods, Life estimation, Automated vehicles, Optimization Searching BibRef

Xu, S.[Sihan], Wang, Z.[Zhiyu], Fan, L.L.[Ling-Ling], Cai, X.[Xiangrui], Ji, H.[Hua], Khoo, S.C.[Siau-Cheng], Gupta, B.B.[Brij Bhooshan],
DeepSuite: A Test Suite Optimizer for Autonomous Vehicles,
ITS(23), No. 7, July 2022, pp. 9506-9517.
IEEE DOI 2207
Testing, Autonomous vehicles, Neurons, Deep learning, Labeling, Fuzzing, Statistics, Autonomous vehicles, data collection, test suite optimization BibRef

Derakhshan, S.[Shadi], Nezami, F.N.[Farbod Nosrat], Wächter, M.A.[Maximilian Alexander], Czeszumski, A.[Artur], Keshava, A.[Ashima], Lukanov, H.[Hristofor], de Palol, M.V.[Marc Vidal], Pipa, G.[Gordon], König, P.[Peter],
Talking Cars, Doubtful Users: A Population Study in Virtual Reality,
HMS(52), No. 4, August 2022, pp. 602-612.
IEEE DOI 2208
Automobiles, Magnetic heads, Angular velocity, Analysis of variance, Resists, Correlation, Visualization, virtual reality (VR) BibRef

Su, H.T.[Hao-Tian], Jia, Y.[Yunyi],
Study of Human Comfort in Autonomous Vehicles Using Wearable Sensors,
ITS(23), No. 8, August 2022, pp. 11490-11504.
IEEE DOI 2208
Physiology, Roads, Autonomous vehicles, Wearable sensors, Software, Psychology, Brain modeling, Autonomous vehicles, virtual environment BibRef

Omeiza, D.[Daniel], Webb, H.[Helena], Jirotka, M.[Marina], Kunze, L.[Lars],
Explanations in Autonomous Driving: A Survey,
ITS(23), No. 8, August 2022, pp. 10142-10162.
IEEE DOI 2208
Autonomous vehicles, Stakeholders, Automation, Regulation, Artificial intelligence, Accidents, Standards, Explanations, standards BibRef

Yu, B.[Bo], Chen, C.[Chongyu], Tang, J.[Jie], Liu, S.S.[Shao-Shan], Gaudiot, J.L.[Jean-Luc],
Autonomous Vehicles Digital Twin: A Practical Paradigm for Autonomous Driving System Development,
Computer(55), No. 9, September 2022, pp. 26-34.
IEEE DOI 2209
Digital twins, Reliability, Autonomous vehicles, Performance evaluation, Testing BibRef

Li, A.[Ao], Chen, S.T.[Shi-Tao], Sun, L.[Liting], Zheng, N.N.[Nan-Ning], Tomizuka, M.[Masayoshi], Zhan, W.[Wei],
SceGene: Bio-Inspired Traffic Scenario Generation for Autonomous Driving Testing,
ITS(23), No. 9, September 2022, pp. 14859-14874.
IEEE DOI 2209
Mathematical models, Biological information theory, Testing, Microscopy, Evolution (biology), Biological system modeling, scenario generation BibRef

Zablocki, É.[Éloi], Ben-Younes, H.[Hédi], Pérez, P.[Patrick], Cord, M.[Matthieu],
Explainability of Deep Vision-Based Autonomous Driving Systems: Review and Challenges,
IJCV(130), No. 10, October 2022, pp. 2425-2452.
Springer DOI 2209
BibRef

Le Mero, L.[Luc], Yi, D.[Dewei], Dianati, M.[Mehrdad], Mouzakitis, A.[Alexandros],
A Survey on Imitation Learning Techniques for End-to-End Autonomous Vehicles,
ITS(23), No. 9, September 2022, pp. 14128-14147.
IEEE DOI 2209
Autonomous vehicles, Task analysis, Cloning, Training, Deep learning, Cameras, Uncertainty, Intelligent vehicles, autonomous vehicles, neural networks BibRef

Caillot, A.[Antoine], Ouerghi, S.[Safa], Vasseur, P.[Pascal], Boutteau, R.[Rémi], Dupuis, Y.[Yohan],
Survey on Cooperative Perception in an Automotive Context,
ITS(23), No. 9, September 2022, pp. 14204-14223.
IEEE DOI 2209
Sensors, Global Positioning System, Cameras, Task analysis, Satellites, Location awareness, tracking BibRef

Eising, C.[Ciarán], Horgan, J.[Jonathan], Yogamani, S.[Senthil],
Near-Field Perception for Low-Speed Vehicle Automation Using Surround-View Fisheye Cameras,
ITS(23), No. 9, September 2022, pp. 13976-13993.
IEEE DOI 2209
Cameras, Automotive engineering, Sensors, Roads, Machine vision, Autonomous vehicles, fisheye camera, 4Rs BibRef

Rashed, H.[Hazem], Mohamed, E.[Eslam], Sistu, G.[Ganesh], Kumar, V.R.[Varun Ravi], Eising, C.[Ciarán], El-Sallab, A.[Ahmad], Yogamani, S.[Senthil],
Generalized Object Detection on Fisheye Cameras for Autonomous Driving: Dataset, Representations and Baseline,
WACV21(2271-2279)
IEEE DOI
PDF File. Results:
WWW Link. 2106
Measurement, Adaptation models, Image segmentation, Object detection, Cameras, Sampling methods BibRef

Jiao, X.Y.[Xin-Yu], Cao, Z.[Zhong], Chen, J.J.[Jun-Jie], Jiang, K.[Kun], Yang, D.[Diange],
A General Autonomous Driving Planner Adaptive to Scenario Characteristics,
ITS(23), No. 11, November 2022, pp. 21228-21240.
IEEE DOI 2212
Autonomous vehicles, Planning, Complexity theory, Adaptation models, Semantics, Roads, Market research, multi-scenario driving decision BibRef

Hacohen, S.[Shlomi], Medina, O.[Oded], Shoval, S.[Shraga],
Autonomous Driving: A Survey of Technological Gaps Using Google Scholar and Web of Science Trend Analysis,
ITS(23), No. 11, November 2022, pp. 21241-21258.
IEEE DOI 2212
Location awareness, Roads, Sensors, Autonomous vehicles, Market research, Internet, Laser radar, Autonomous vehicle, Google trends BibRef

Zhou, M.T.[Ming-Ting], Sui, H.G.[Hai-Gang], Chen, S.X.[Shan-Xiong], Chen, X.[Xu], Wang, W.Q.[Wen-Qing], Wang, J.X.[Jian-Xun], Liu, J.[Junyi],
UGRoadUpd: An Unchanged-Guided Historical Road Database Updating Framework Based on Bi-Temporal Remote Sensing Images,
ITS(23), No. 11, November 2022, pp. 21465-21477.
IEEE DOI 2212
Roads, Databases, Feature extraction, Image segmentation, Remote sensing, Deep learning, Benchmark testing, road change detection datasets BibRef

Khan, M.A.[Manzoor Ahmed], Sayed, H.E.[Hesham El], Malik, S.[Sumbal], Zia, T.[Talha], Khan, J.[Jalal], Alkaabi, N.[Najla], Ignatious, H.[Henry],
Level-5 Autonomous Driving: Are We There Yet? A Review of Research Literature,
Surveys(55), No. 2, February 2023, pp. xx-yy.
DOI Link 2212
Autonomous driving, mobile networks, sensor fusion, platooning, 5G BibRef

Singh, G.[Gurkirt], Akrigg, S.[Stephen], di Maio, M.[Manuele], Fontana, V.[Valentina], Alitappeh, R.J.[Reza Javanmard], Khan, S.[Salman], Saha, S.[Suman], Jeddisaravi, K.[Kossar], Yousefi, F.[Farzad], Culley, J.[Jacob], Nicholson, T.[Tom], Omokeowa, J.[Jordan], Grazioso, S.[Stanislao], Bradley, A.[Andrew], di Gironimo, G.[Giuseppe], Cuzzolin, F.[Fabio],
ROAD: The Road Event Awareness Dataset for Autonomous Driving,
PAMI(45), No. 1, January 2023, pp. 1036-1054.
IEEE DOI 2212
Dataset, Autonomous Driving. Roads, Autonomous vehicles, Task analysis, Videos, Benchmark testing, Decision making, Vehicle dynamics, Autonomous driving, decision making BibRef

Roy, K.[Keya], Hoang, N.H.[Nam Hong], Vu, H.L.[Hai L.],
Modeling Autonomous Vehicles Deployment in a Multilane AV Zone With Mixed Traffic,
ITS(23), No. 12, December 2022, pp. 23708-23720.
IEEE DOI 2212
Routing, Transportation, Autonomous vehicles, Safety, Analytical models, Urban areas, Transforms, Autonomous vehicle, optimal AV deployment BibRef

Gu, N.[Nan], Wang, D.[Dan], Peng, Z.H.[Zhou-Hua], Wang, J.[Jun], Han, Q.L.[Qing-Long],
Advances in Line-of-Sight Guidance for Path Following of Autonomous Marine Vehicles: An Overview,
SMCS(53), No. 1, January 2023, pp. 12-28.
IEEE DOI 2301
Sea measurements, Motion control, Kinematics, Sea surface, Current measurement, Attitude control, Acoustic measurements, line-of-sight (LOS) guidance BibRef

Li, Q.Y.[Quan-Yi], Peng, Z.H.[Zheng-Hao], Feng, L.[Lan], Zhang, Q.H.[Qi-Hang], Xue, Z.H.[Zheng-Hai], Zhou, B.[Bolei],
MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning,
PAMI(45), No. 3, March 2023, pp. 3461-3475.
IEEE DOI 2302
Task analysis, Roads, Reinforcement learning, Benchmark testing, Training, Safety, Autonomous vehicles, Reinforcement learning, simulation BibRef

Khosravian, A.[Amir], Amirkhani, A.[Abdollah], Masih-Tehrani, M.[Masoud], Yazdanijoo, A.[Alireza],
Multi-domain autonomous driving dataset: Towards enhancing the generalization of the convolutional neural networks in new environments,
IET-IPR(17), No. 4, 2023, pp. 1253-1266.
DOI Link 2303
convolutional neural nets, database indexing, image annotation, vehicles BibRef

Qiao, Y.Y.[Yuan-Yuan], Yin, J.X.[Jia-Xin], Wang, W.[Wei], Duarte, F.[Fábio], Yang, J.[Jie], Ratti, C.[Carlo],
Survey of Deep Learning for Autonomous Surface Vehicles in Marine Environments,
ITS(24), No. 4, April 2023, pp. 3678-3701.
IEEE DOI 2304
Sensors, Sea surface, Sensor systems, Marine vehicles, Control systems, Deep learning, Task analysis, neural network BibRef

Farah, H.[Haneen], Olstam, J.[Johan], Zheng, Z.[Zuduo],
Guest Editorial Introduction to the Special Issue on Deployment of Connected and Automated Vehicles in Mixed Traffic Environment and the Implications on Traffic Safety and Efficiency,
ITS(24), No. 6, June 2023, pp. 6432-6435.
IEEE DOI 2306
Special issues and sections, Autonomous vehicles, Connected vehicles, Traffic control, Vehicle safety BibRef

Testolina, P.[Paolo], Barbato, F.[Francesco], Michieli, U.[Umberto], Giordani, M.[Marco], Zanuttigh, P.[Pietro], Zorzi, M.[Michele],
SELMA: SEmantic Large-Scale Multimodal Acquisitions in Variable Weather, Daytime and Viewpoints,
ITS(24), No. 7, July 2023, pp. 7012-7024.
IEEE DOI 2307
Cameras, Sensors, Semantics, Meteorology, Autonomous vehicles, Task analysis, Synthetic data, Synthetic dataset, CARLA, sensor fusion BibRef

Wang, Y.J.[Ying-Jie], Mao, Q.Y.[Qiu-Yu], Zhu, H.Q.[Han-Qi], Deng, J.J.[Jia-Jun], Zhang, Y.[Yu], Ji, J.M.[Jian-Min], Li, H.Q.[Hou-Qiang], Zhang, Y.Y.[Yan-Yong],
Multi-Modal 3D Object Detection in Autonomous Driving: A Survey,
IJCV(131), No. 8, August 2023, pp. 2122-2152.
Springer DOI 2307
BibRef

Mao, R.Q.[Rui-Qing], Guo, J.Y.[Jing-Yu], Jia, Y.[Yukuan], Sun, Y.X.[Yu-Xuan], Zhou, S.[Sheng], Niu, Z.S.[Zhi-Sheng],
Dolphins: Dataset for Collaborative Perception Enabled Harmonious and Interconnected Self-driving,
ACCV22(V:495-511).
Springer DOI 2307
BibRef

Mao, J.G.[Jia-Geng], Shi, S.S.[Shao-Shuai], Wang, X.G.[Xiao-Gang], Li, H.S.[Hong-Sheng],
3D Object Detection for Autonomous Driving: A Comprehensive Survey,
IJCV(131), No. 8, August 2023, pp. 1909-1963.
Springer DOI 2307
Survey, Object Detection. BibRef

Rahmani, S.[Saeed], Baghbani, A.[Asiye], Bouguila, N.[Nizar], Patterson, Z.[Zachary],
Graph Neural Networks for Intelligent Transportation Systems: A Survey,
ITS(24), No. 8, August 2023, pp. 8846-8885.
IEEE DOI 2308
Transportation, Forecasting, Graph neural networks, Deep learning, Search problems, Laplace equations, Safety, Graph neural networks, ITS BibRef

Chen, L.[Long], Li, Y.C.[Yu-Chen], Huang, C.[Chao], Xing, Y.[Yang], Tian, D.X.[Da-Xin], Li, L.[Li], Hu, Z.X.[Zhong-Xu], Teng, S.[Siyu], Lv, C.[Chen], Wang, J.J.[Jin-Jun], Cao, D.[Dongpu], Zheng, N.N.[Nan-Ning], Wang, F.Y.[Fei-Yue],
Milestones in Autonomous Driving and Intelligent Vehicles: Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors,
SMCS(53), No. 9, September 2023, pp. 5831-5847.
IEEE DOI 2309
BibRef

Chen, L.[Long], Teng, S.[Siyu], Li, B.[Bai], Na, X.X.[Xiao-Xiang], Li, Y.C.[Yu-Chen], Li, Z.X.[Zi-Xuan], Wang, J.J.[Jin-Jun], Cao, D.[Dongpu], Zheng, N.N.[Nan-Ning], Wang, F.Y.[Fei-Yue],
Milestones in Autonomous Driving and Intelligent Vehicles: Part II: Perception and Planning,
SMCS(53), No. 10, October 2023, pp. 6401-6415.
IEEE DOI 2310
BibRef

Wang, Y.N.[Yu-Ning], Jiang, J.[Junkai], Li, S.[Shangyi], Li, R.[Ruochen], Xu, S.B.[Shao-Bing], Wang, J.Q.[Jian-Qiang], Li, K.Q.[Ke-Qiang],
Decision-Making Driven by Driver Intelligence and Environment Reasoning for High-Level Autonomous Vehicles: A Survey,
ITS(24), No. 10, October 2023, pp. 10362-10381.
IEEE DOI 2310
BibRef

Yang, K.[Kai], Tang, X.L.[Xiao-Lin], Li, J.[Jun], Wang, H.[Hong], Zhong, G.[Guichuan], Chen, J.X.[Jia-Xin], Cao, D.[Dongpu],
Uncertainties in Onboard Algorithms for Autonomous Vehicles: Challenges, Mitigation, and Perspectives,
ITS(24), No. 9, September 2023, pp. 8963-8987.
IEEE DOI 2310
BibRef

Huang, J.C.[Jian-Chang], Song, G.H.[Guo-Hua], He, F.[Feng], Tan, Z.[Zhe],
Energetic Impacts of Autonomous Vehicles in Real-World Traffic Conditions From Nine Open-Source Datasets,
ITS(24), No. 9, September 2023, pp. 9901-9914.
IEEE DOI 2310
BibRef

Zhu, Y.[Yu], Wang, J.[Jian], Guo, X.Y.[Xin-Yu], Meng, F.[Fan=Qiang], Liu, T.T.[Tong-Tao],
Functional Testing Scenario Library Generation Framework for Connected and Automated Vehicles,
ITS(24), No. 9, September 2023, pp. 9712-9724.
IEEE DOI 2310
BibRef

Manikandan, N.S., Ganesan, K.,
Energy-aware automatic video annotation tool for autonomous vehicle,
IJCVR(13), No. 5, 2023, pp. 510-532.
DOI Link 2310
BibRef

Rabiee, S.[Sadegh], Biswas, J.[Joydeep],
Introspective perception for mobile robots,
AI(324), 2023, pp. 103999.
Elsevier DOI 2312
Competence-aware perception, Introspection, Mobile robots BibRef

Zhang, C.[Ce], Eskandarian, A.[Azim],
A Quality Index Metric and Method for Online Self-Assessment of Autonomous Vehicles Sensory Perception,
ITS(24), No. 12, December 2023, pp. 13801-13812.
IEEE DOI 2312
BibRef

Tengilimoglu, O.[Oguz], Carsten, O.[Oliver], Wadud, Z.[Zia],
Infrastructure-related challenges in implementing connected and automated vehicles on urban roads: Insights from experts and stakeholders,
IET-ITS(17), No. 12, 2023, pp. 2352-2368.
DOI Link 2312
automated driving, challenges, connected and automated vehicles, financial requirements, urban network BibRef

Xie, Y.Z.[Yi-Zhou], Zhang, Y.[Yong], Dai, K.[Kunpeng], Yin, C.[Chengliang],
A real-time critical-scenario-generation framework for defect detection of autonomous driving system,
IET-ITS(18), No. 1, 2024, pp. 114-128.
DOI Link 2401
accident prevention, adaptive control, automated driving and intelligent vehicles, safety, SOTIF BibRef

Chen, Y.L.[Yi-Lun], Shiwakoti, N.[Nirajan], Stasinopoulos, P.[Peter], Khan, S.K.[Shah Khalid], Aghabayk, K.[Kayvan],
Exploring the association between socio-demographic factors and public acceptance towards fully automated vehicles: Insights from a survey in Australia,
IET-ITS(18), No. 1, 2024, pp. 154-172.
DOI Link 2401
automated driving and intelligent vehicles, intelligent transportation systems, socio-economic effects, user experience BibRef

Yatbaz, H.Y.[Hakan Yekta], Dianati, M.[Mehrdad], Woodman, R.[Roger],
Introspection of DNN-Based Perception Functions in Automated Driving Systems: State-of-the-Art and Open Research Challenges,
ITS(25), No. 2, February 2024, pp. 1112-1130.
IEEE DOI 2402
Safety, Sensors, Object detection, Monitoring, Artificial neural networks, Semantics, Semantic segmentation, deep learning BibRef

Khalid, A.[Adnan], Mushtaq, Z.[Zohaib], Arif, S.[Saad], Zeb, K.[Kamran], Khan, M.A.[Muhammad Attique], Bakshi, S.[Sambit],
Control Schemes for Quadrotor UAV: Taxonomy and Survey,
Surveys(56), No. 5, November 2023, pp. xx-yy.
DOI Link 2402
Fuzzy Logic Control, Sliding Mode Control, Linear Quadratic Regulator, PID, Quadrotor, Unmanned Aerial Vehicles BibRef

Li, H.Y.[Hong-Yang], Sima, C.H.[Chong-Hao], Dai, J.F.[Ji-Feng], Wang, W.H.[Wen-Hai], Lu, L.W.[Le-Wei], Wang, H.J.[Hui-Jie], Zeng, J.[Jia], Li, Z.Q.[Zhi-Qi], Yang, J.Z.[Jia-Zhi], Deng, H.M.[Han-Ming], Tian, H.[Hao], Xie, E.[Enze], Xie, J.W.[Jiang-Wei], Chen, L.[Li], Li, T.Y.[Tian-Yu], Li, Y.[Yang], Gao, Y.[Yulu], Jia, X.S.[Xiao-Song], Liu, S.[Si], Shi, J.P.[Jian-Ping], Lin, D.[Dahua], Qiao, Y.[Yu],
Delving Into the Devils of Bird's-Eye-View Perception: A Review, Evaluation and Recipe,
PAMI(46), No. 4, April 2024, pp. 2151-2170.
IEEE DOI 2403
For driving applications. Laser radar, Cameras, Task analysis, Autonomous vehicles, Pipelines, Surveys, 3D detection and segmentation, bird's-eye-view (BEV) perception BibRef


Alibeigi, M.[Mina], Ljungbergh, W.[William], Tonderski, A.[Adam], Hess, G.[Georg], Lilja, A.[Adam], Lindström, C.[Carl], Motorniuk, D.[Daria], Fu, J.S.[Jun-Sheng], Widahl, J.[Jenny], Petersson, C.[Christoffer],
Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous driving,
ICCV23(20121-20131)
IEEE DOI 2401
BibRef

Yatbaz, H.Y.[Hakan Yekta], Dianati, M.[Mehrdad], Koufos, K.[Konstantinos], Woodman, R.[Roger],
Introspection of 2D Object Detection using Processed Neural Activation Patterns in Automated Driving Systems,
BRAVO23(4049-4056)
IEEE DOI 2401
BibRef

Singh, A.[Apoorv],
Transformer-Based Sensor Fusion for Autonomous Driving: A Survey,
VCL23(3304-3309)
IEEE DOI 2401
BibRef

Wang, X.F.[Xiao-Feng], Zhu, Z.[Zheng], Zhang, Y.P.[Yun-Peng], Huang, G.[Guan], Ye, Y.[Yun], Xu, W.B.[Wen-Bo], Chen, Z.W.[Zi-Wei], Wang, X.G.[Xin-Gang],
Are We Ready for Vision-Centric Driving Streaming Perception? The ASAP Benchmark,
CVPR23(9600-9610)
IEEE DOI 2309
BibRef

Marathe, A.[Aboli], Ramanan, D.[Deva], Walambe, R.[Rahee], Kotecha, K.[Ketan],
WEDGE: A multi-weather autonomous driving dataset built from generative vision-language models,
VDU23(3318-3327)
IEEE DOI 2309
BibRef

Cao, P.[Pei], Chen, H.[Hao], Zhang, Y.[Ye], Wang, G.[Gang],
Multi-View Frustum Pointnet for Object Detection in Autonomous Driving,
ICIP19(3896-3899)
IEEE DOI 1910
multi-view frustum PointNet (MVFP), bird's eye view (BEV), missed detection, recall BibRef

Dokania, S.[Shubham], Hafez, A.H.A.[A. H. Abdul], Subramanian, A.[Anbumani], Chandraker, M.[Manmohan], Jawahar, C.V.,
IDD-3D: Indian Driving Dataset for 3D Unstructured Road Scenes,
WACV23(4471-4480)
IEEE DOI 2302
Training, Adaptation models, Laser radar, Roads, Urban areas, Layout, Applications: Robotics, 3D computer vision, visual reasoning BibRef

Li, Y.C.[Yu-Chen], Li, Z.X.[Zi-Xuan], Teng, S.Y.[Si-Yu], Zhang, Y.[Yu], Zhou, Y.H.[Yu-Hang], Zhu, Y.C.[Yu-Chang], Cao, D.[Dongpu], Tian, B.[Bin], Ai, Y.F.[Yun-Feng], Zhe, X.Y.[Xuan-Yuan], Chen, L.[Long],
AutoMine: An Unmanned Mine Dataset,
CVPR22(21276-21285)
IEEE DOI 2210
Location awareness, Snow, Roads, Sensors, Data mining, Task analysis, Datasets and evaluation, 3D from multi-view and sensors, Navigation and autonomous driving BibRef

Klingner, M.[Marvin], Müller, K.[Konstantin], Mirzaie, M.[Mona], Breitenstein, J.[Jasmin], Termöhlen, J.A.[Jan-Aike], Fingscheidt, T.[Tim],
On the Choice of Data for Efficient Training and Validation of End-to-End Driving Models,
VDU22(4802-4811)
IEEE DOI 2210
Training, Machine learning algorithms, Correlation, Training data, Machine learning, Data models BibRef

Bogdoll, D.[Daniel], Guneshka, S.[Stefani], Zöllner, J.M.[J. Marius],
One Ontology to Rule Them All: Corner Case Scenarios for Autonomous Driving,
SafeDrive22(409-425).
Springer DOI 2304
BibRef

Bogdoll, D.[Daniel], Nitsche, M.[Maximilian], Zöllner, J.M.[J. Marius],
Anomaly Detection in Autonomous Driving: A Survey,
WAD22(4487-4498)
IEEE DOI 2210
Laser radar, Roads, Radar detection, Benchmark testing, Cameras BibRef

Cui, Y.M.[Yi-Ming], Cao, Z.W.[Zhi-Wen], Xie, Y.X.[Yi-Xin], Jiang, X.Y.[Xing-Yu], Tao, F.[Feng], Chen, Y.J.V.[Ying-Jie Victor], Li, L.[Lin], Liu, D.F.[Dong-Fang],
DG-Labeler and DGL-MOTS Dataset: Boost the Autonomous Driving Perception,
WACV22(3411-3420)
IEEE DOI 2202
Training, Annotations, Pipelines, Transportation, Training data, Task analysis, Vision for Aerial/Drone/Underwater/Ground Vehicles BibRef

Brunel, A.[Anthony], Bourki, A.[Amine], Strauss, O.[Olivier], Demonceaux, C.[Cédric],
FLYBO: A Unified Benchmark Environment for Autonomous Flying Robots,
3DV21(1420-1431)
IEEE DOI 2201
Surface reconstruction, Codes, Benchmark testing, Inspection, Sensors, Complexity theory, Robotic Vision, Datasets, 3D Reconstruction BibRef

Li, L.[Li], Ismail, K.N.[Khalid N.], Shum, H.P.H.[Hubert P. H.], Breckon, T.P.[Toby P.],
DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications,
3DV21(1227-1237)
IEEE DOI 2201
Dataset, Autonomous Driving. Reflectivity, Laser radar, Image resolution, Supervised learning, Estimation, Benchmark testing, autonomous driving, dataset, three dimensional BibRef

Yuan, Y., Sester, M.,
Comap: a Synthetic Dataset for Collective Multi-agent Perception of Autonomous Driving,
ISPRS21(B2-2021: 255-263).
DOI Link 2201
BibRef

Jin, J.C.[Jiong-Chao], Fatemi, A.[Arezou], Lira, W.M.P.[Wallace Michel Pinto], Yu, F.G.[Feng-Gen], Leng, B.[Biao], Ma, R.[Rui], Mahdavi-Amiri, A.[Ali], Zhang, H.[Hao],
RaidaR: A Rich Annotated Image Dataset of Rainy Street Scenes,
AVVision21(2951-2961)
IEEE DOI 2112
Image segmentation, Rain, Annotations, Roads, Semantics BibRef

Xu, Q.[Qi], Ma, Y.[Yinan], Wu, J.[Jing], Long, C.[Chengnian], Huang, X.L.[Xiao-Lin],
CDAda: A Curriculum Domain Adaptation for Nighttime Semantic Segmentation,
AVVision21(2962-2971)
IEEE DOI 2112
Training, Adaptation models, Image segmentation, Computational modeling, Semantics, Training data, Entropy BibRef

Siam, M.[Mennatullah], Kendall, A.[Alex], Jagersand, M.[Martin],
Video Class Agnostic Segmentation Benchmark for Autonomous Driving,
WAD21(2819-2828)
IEEE DOI 2109
Training, Tracking, Motion segmentation, Semantics, Video sequences, Benchmark testing BibRef

Swan, R.M.[R. Michael], Atha, D.[Deegan], Leopold, H.A.[Henry A.], Gildner, M.[Matthew], Oij, S.[Stephanie], Chiu, C.[Cindy], Ono, M.[Masahiro],
AI4MARS: A Dataset for Terrain-Aware Autonomous Driving on Mars,
AI4Space21(1982-1991)
IEEE DOI 2109
Training, Space vehicles, Deep learning, Productivity, Mars, Image segmentation, Semantics BibRef

Thoduka, S.[Santosh], Hochgeschwender, N.[Nico],
Benchmarking Robots by Inducing Failures in Competition Scenarios,
DHM21(II:263-276).
Springer DOI 2108
BibRef

Papachristodoulou, A.[Andreas], Kyrkou, C.[Christos], Theocharides, T.[Theocharis],
DriveGuard: Robustification of Automated Driving Systems with Deep Spatio-Temporal Convolutional Autoencoder,
WACVW21(107-116) Autonomous Vehicle Vision
IEEE DOI 2105
Image segmentation, Computational modeling, Semantics, Computer architecture, Cameras BibRef

Xu, W.[Weihuang], Souly, N.[Nasim], Brahma, P.P.[Pratik Prabhanjan],
Reliability of GAN Generated Data to Train and Validate Perception Systems for Autonomous Vehicles,
WACVW21(171-180) Autonomous Vehicle Vision
IEEE DOI 2105
Training, Training data, Object detection, Tools, Generative adversarial networks, Data models BibRef

Rosano, M.[Marco], Furnari, A.[Antonino], Gulino, L.[Luigi], Farinella, G.M.[Giovanni Maria],
On Embodied Visual Navigation in Real Environments Through Habitat,
ICPR21(9740-9747)
IEEE DOI 2105
Simulators to generat navagiation data. Deep learning, Visualization, Adaptation models, Actuators, Navigation, Virtual environments, Reinforcement learning BibRef

Koilias, A.[Alexandros], Mousas, C.[Christos], Rekabdar, B.[Banafsheh], Anagnostopoulos, C.N.[Christos-Nikolaos],
Passenger Anxiety About Virtual Driver Awareness During a Trip with a Virtual Autonomous Vehicle,
ISVC20(I:654-665).
Springer DOI 2103
BibRef

Sun, B., Sha, H., Rafie, M., Yang, L.,
CDVA/VCM: Language for Intelligent and Autonomous Vehicles,
ICIP20(3104-3108)
IEEE DOI 2011
Navigation, Transform coding, Standards, Autonomous vehicles, Natural languages, Feature extraction, Roads, CDVA, VCM, Language, Autonomous Vehicles BibRef

Zhang, S., Peng, H., Nageshrao, S., Tseng, H.E.,
Generating Socially Acceptable Perturbations for Efficient Evaluation of Autonomous Vehicles,
SAIAD20(1341-1347)
IEEE DOI 2008
Perturbation methods, Learning (artificial intelligence), Training, Machine learning, Games, Mathematical model, Autonomous vehicles BibRef

Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., Beijbom, O.,
nuScenes: A Multimodal Dataset for Autonomous Driving,
CVPR20(11618-11628)
IEEE DOI 2008
Sensors, Laser radar, Cameras, Radar tracking, Autonomous vehicles BibRef

Ettinger, S.[Scott], Cheng, S.Y.[Shu-Yang], Caine, B.[Benjamin], Liu, C.X.[Chen-Xi], Zhao, H.[Hang], Pradhan, S.[Sabeek], Chai, Y.N.[Yu-Ning], Sapp, B.[Ben], Qi, C.[Charles], Zhou, Y.[Yin], Yang, Z.[Zoey], Chouard, A.[Aurélien], Sun, P.[Pei], Ngiam, J.[Jiquan], Vasudevan, V.[Vijay], McCauley, A.[Alexander], Shlens, J.[Jonathon], Anguelov, D.[Dragomir],
Large Scale Interactive Motion Forecasting for Autonomous Driving: The Waymo Open Motion Dataset,
ICCV21(9690-9699)
IEEE DOI 2203
Measurement, Computational modeling, Roads, Urban areas, Predictive models, Data models, Datasets and evaluation, Motion and tracking BibRef

Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., Caine, B., Vasudevan, V., Han, W., Ngiam, J., Zhao, H., Timofeev, A., Ettinger, S., Krivokon, M., Gao, A., Joshi, A., Zhang, Y., Shlens, J., Chen, Z., Anguelov, D.[Dragomir],
Scalability in Perception for Autonomous Driving: Waymo Open Dataset,
CVPR20(2443-2451)
IEEE DOI 2008
Laser radar, Cameras, Autonomous vehicles, Radar tracking, Semantics BibRef

Yogamani, S., Hughes, C., Horgan, J., Sistu, G., Chennupati, S., Uricar, M., Milz, S., Simon, M., Amende, K., Witt, C., Rashed, H., Nayak, S., Mansoor, S., Varley, P., Perrotton, X., Odea, D., Pérez, P.,
WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving,
ICCV19(9307-9317)
IEEE DOI
WWW Link. 2004
Dataset, Autonomous Driving. automotive electronics, cameras, driver information systems, image annotation, Nonlinear distortion BibRef

Lakshminarayana, N.,
Large Scale Multimodal Data Capture, Evaluation and Maintenance Framework for Autonomous Driving Datasets,
AutoNUE19(4302-4309)
IEEE DOI 2004
learning (artificial intelligence), sensor fusion, traffic engineering computing, open-source framework, framework BibRef

Yang, G.R.[Guo-Run], Song, X.[Xiao], Huang, C.Q.[Chao-Qin], Deng, Z.D.[Zhi-Dong], Shi, J.P.[Jian-Ping], Zhou, B.[Bolei],
DrivingStereo: A Large-Scale Dataset for Stereo Matching in Autonomous Driving Scenarios,
CVPR19(899-908).
IEEE DOI 2002
BibRef

Varma, G., Subramanian, A., Namboodiri, A., Chandraker, M., Jawahar, C.V.,
IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments,
WACV19(1743-1751)
IEEE DOI 1904
image segmentation, learning (artificial intelligence), mobile robots, path planning, road traffic, robot vision, Motorcycles BibRef

Loquercio, A.[Antonio], Kaufmann, E.[Elia], Ranflt, R.[Rene], Mueller, M.[Matthias], Koltun, V.[Vladlen], Scaramuzza, D.[Davide],
Learning High-Speed Flight in the Wild,
Science Robotics2021.
WWW Link. Project page:
HTML Version. Code, dataset page:
WWW Link. Code, Drone Control. BibRef 2100

Codevilla, F.[Felipe], López, A.M.[Antonio M.], Koltun, V.[Vladlen], Dosovitskiy, A.[Alexey],
On Offline Evaluation of Vision-Based Driving Models,
ECCV18(XV: 246-262).
Springer DOI 1810
BibRef

Geiger, A.[Andreas], Lenz, P.[Philip], Urtasun, R.[Raquel],
Are we ready for autonomous driving? The KITTI vision benchmark suite,
CVPR12(3354-3361).
IEEE DOI 1208
BibRef

Leonard, J.J.[John J.],
Challenges for Autonomous Mobile Robots,
IMVIP07(4-4).
IEEE DOI 0709
BibRef

Jolic, M.S.N.,
Modelling the Robotised Multiterminal Port System: RMT-PS,
IVS04(222-225).
IEEE DOI 0411
Analysis of the cargo handling system. BibRef

Manduchi, R., Matthies, L.H., Pollara, F.,
From cross-country autonomous navigation to intelligent deep space communications: visual sensor processing at JPL,
CIAP01(472-477).
IEEE DOI 0210
BibRef

Chapter on Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following continues in
Driver Assistance Systems and Techniques .


Last update:Apr 10, 2024 at 09:54:40