15.3.1.4 Autonomous Vehicles, Surveys, Collections, Overviews

Chapter Contents (Back)
Survey, Autonomous Vehicles. Autonomous Vehicles.

UZH-FPV Drone Racing Dataset,
2019.
HTML Version. Dataset, Visual Odometry. 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.

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

Masaki, I.,
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AdvRob(9), No. 4, 1995, pp. 417-427. BibRef 9500

Meyrowitz, A.L., Blidberg, D.R., and Michelson, R.C.,
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Aloimonos, Y., (Ed.),
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Broggi, A.,
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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
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Coradeschi, S.[Silvia], Karlsson, L.[Lars], Stone, P.[Peter], Balch, T.[Tucker], Kraetzschmar, G.K.[Gerhard K.], Asada, M.[Minoru],
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AIMag(21), No. 3, Fall 2000, pp. 11-18. Survey of the event. The winners have articles in the issue. 0009
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Dudek, G.[Gregory], Jenkin, M.R.M.[Michael R.M.], Milios, E.E.[Evangelos E.],
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Vision for Mobile Robot Navigation: A Survey,
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IEEE DOI 0202
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Broggi, A., Ikeuchi, K., Thorpe, C.E.,
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Broggi, A.,
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Murphy, R.R., Rogers, E.,
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IEEE Abstract. 0407
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IEEE Abstract. 0407
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Physica-Verlag2002. ISBN 3-7908-1494-6. BibRef 0200

Sanz, P.J., Marin, R., Sanchez, J.S.,
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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],
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Springer DOI 0709
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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).
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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
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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

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

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
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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

Bila, C., Sivrikaya, F., Khan, M.A., Albayrak, S.,
Vehicles of the Future: A Survey of Research on Safety Issues,
ITS(18), No. 5, May 2017, pp. 1046-1065.
IEEE DOI 1705
Survey, Driver Assistance. Collision avoidance, Roads, Safety, Stereo vision, Taxonomy, Vehicles, Advanced driver assistance systems, collision avoidance, connected vehicles, intelligent vehicles, vehicle detection, vehicle, safety BibRef

Ross, P.E.,
The curious incident of the robocar in the night-time,
Spectrum(55), No. 1, January 2018, pp. 44-45.
IEEE DOI 1801
automobiles, mobile robots, actual self-driving cars, level 0, level 1, level 2, level 3, level 4 autonomy, level 5, night-time, Safety BibRef

Coelingh, E., Nilsson, J., Buffum, J.,
Driving tests for self-driving cars,
Spectrum(55), No. 3, March 2018, pp. 40-45.
IEEE DOI 1804
automobiles, mobile robots, road safety, testing, Autoliv, Gothenburg, Sweden, Swedish auto-safety company, Volvo, Zenuity, Sensors 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

Bonnefon, J., Shariff, A., Rahwan, I.,
The trolley, the bull bar, and why engineers should care about the ethics of autonomous cars,
PIEEE(107), No. 3, March 2019, pp. 502-504.
IEEE DOI 1903
[point of view]. Autonomous automobiles, Autonomous vehicles, Ethics, Accidents, Road traffic, Vehicle safety, Market opportunities, Design methodology BibRef

Adamson, G., Havens, J.C., Chatila, R.,
Designing a Value-Driven Future for Ethical Autonomous and Intelligent Systems,
PIEEE(107), No. 3, March 2019, pp. 518-525.
IEEE DOI 1903
Ethics, Intelligent systems, Organizations, Robots, Autonomous veicles, Accidents, Social implications of technology, technology and society 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
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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
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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
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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
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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,
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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
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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,
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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
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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
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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 [From the Editor],
SPMag(37), No. 4, July 2020, pp. 3-13.
IEEE DOI 2007
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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
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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
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Cusumano, M.A.[Michael A.],
Self-Driving Vehicle Technology: Progress and Promises,
CACM(63), No. 10, October 2020, pp. 20-22.
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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

Nascimento, A.M., Vismari, L.F., Molina, C.B.S.T., Cugnasca, P.S., Camargo, J.B., de Almeida, J.R., Inam, R., Fersman, E., Marquezini, M.V., Hata, A.Y.,
A Systematic Literature Review About the Impact of Artificial Intelligence on Autonomous Vehicle Safety,
ITS(21), No. 12, December 2020, pp. 4928-4946.
IEEE DOI 2012
Safety, Autonomous vehicles, Databases, Systematics, Bibliographies, Autonomous vehicles, safety, artificial intelligence, machine intelligence 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.
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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

Ryan, C., Murphy, F., Mullins, M.,
End-to-End Autonomous Driving Risk Analysis: A Behavioural Anomaly Detection Approach,
ITS(22), No. 3, March 2021, pp. 1650-1662.
IEEE DOI 2103
Anomaly detection, Accidents, Safety, Autonomous vehicles, Regulators, Roads, Autonomous vehicle, accident risk, Gaussian process BibRef

Xie, G.Q.[Guo-Qi], Wu, W.[Wei], Zeng, G.[Gang], Li, R.[Renfa], Hu, S.Y.[Shi-Yan],
Risk Assessment and Development Cost Optimization in Software Defined Vehicles,
ITS(22), No. 6, June 2021, pp. 3675-3686.
IEEE DOI 2106
Safety, Reliability, Software, Task analysis, Optimization, ISO Standards, Real-time systems, Software defined vehicles, cost, reliability 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
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Karmakar, G.[Gour], Chowdhury, A.[Abdullahi], Das, R.[Rajkumar], Kamruzzaman, J.[Joarder], Islam, S.[Syed],
Assessing Trust Level of a Driverless Car Using Deep Learning,
ITS(22), No. 7, July 2021, pp. 4457-4466.
IEEE DOI 2107
Autonomous automobiles, Cameras, Automobiles, Security, Global Positioning System, Safety, Laser radar, Driverless car, intelligent transportation system BibRef

Motwani, S.[Sachin], Sharma, T.[Tarun], Gupta, A.[Anubha],
Ethics in Autonomous Vehicle Software: The Dilemmas,
Computer(54), No. 8, August 2021, pp. 46-55.
IEEE DOI 2108
Industries, Ethics, Autonomous vehicles, Reliability engineering, Software reliability, Automobiles BibRef

Burton, S.[Simon], McDermid, J.A.[John Alexander], Garnett, P.[Philip], Weaver, R.[Rob],
Safety, Complexity, and Automated Driving: Holistic Perspectives on Safety Assurance,
Computer(54), No. 8, August 2021, pp. 22-32.
IEEE DOI 2108
Cognition, Vehicle safety, Complexity theory, Autonomous vehicles, Quality assurance BibRef

Hu, L.[Lin], Zhou, X.[Xiqin], Zhang, X.[Xin], Wang, F.[Fang], Li, Q.[Qiqi], Wu, W.[Wenguang],
A review on key challenges in intelligent vehicles: Safety and driver-oriented features,
IET-ITS(15), No. 9, 2021, pp. 1093-1105.
DOI Link 2108
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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.[Weiyang], 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

Liu, M.[Mushuang], Wan, Y.[Yan], Lewis, F.L.[Frank L.], Atkins, E.[Ella], Wu, D.P.O.[Da-Peng Oliver],
Statistical Properties and Airspace Capacity for Unmanned Aerial Vehicle Networks Subject to Sense-and-Avoid Safety Protocols,
ITS(22), No. 9, September 2021, pp. 5890-5903.
IEEE DOI 2109
Atmospheric modeling, Protocols, Safety, Analytical models, Capacity planning, Markov processes, Aircraft, airspace capacity management 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.[Xuewei],
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


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

Wong, K.[Kelvin], Zhang, Q.A.[Qi-Ang], Liang, M.[Ming], Yang, B.[Bin], Liao, R.J.[Ren-Jie], Sadat, A.[Abbas], Urtasun, R.[Raquel],
Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction,
ECCV20(XXVI:312-329).
Springer DOI 2011
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

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.,
Scalability in Perception for Autonomous Driving: Waymo Open Dataset,
CVPR20(2443-2451)
IEEE DOI 2008
Laser radar, Cameras, Autonomous vehicles, Radar tracking, Semantics 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

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, computer vision, 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.[Guorun], Song, X.[Xiao], Huang, C.[Chaoqin], 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

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:Sep 12, 2021 at 22:38:33