Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 1,209

The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.

Self-driving cars: A survey

Open access
Badue, Claudine, Guidolini, Rânik, Carneiro, Raphael Vivacqua, Azevedo, Pedro, Cardoso, Vinicius B., Forechi, Avelino, Jesus, Luan, Berriel, Rodrigo, Paixão, Thiago M., Mutz, Filipe, de Paula Veronese, Lucas, Oliveira-Santos, Thiago, De Souza, Alberto F.·2021·Expert Systems with Applications
DOI 10.1016/j.eswa.2020.113816
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.eswa.2020.113816

Cites

12 of 12 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 12 references

Research works printed but not linked: 96

  1. Aeberhard, 2015, Experience, results and lessons learned from automated driving on germany’s highways, IEEE Intelligent Transportation Systems Magazine, 7, 42, 10.1109/MITS.2014.2360306 · DOI · Paper page
  2. Arnay, 2016, Safe and reliable path planning for the autonomous vehicle verdino, IEEE Intelligent Transportation Systems Magazine, 8, 22, 10.1109/MITS.2015.2504393 · doi:10.1109/mits.2015.2504393
  3. Bacha, 2008, Odin: Team victortango’s entry in the darpa urban challenge, Journal of Field Robotics, 25, 467, 10.1002/rob.20248 · DOI · Paper page
  4. Barnes, 2008, Real-time speed sign detection using the radial symmetry detector, IEEE Transactions on Intelligent Transportation Systems, 9, 322, 10.1109/TITS.2008.922935 · DOI · Paper page
  5. Bastani, F., He, S., Abbar, S., Alizadeh, M., Balakrishnan, H., Chawla, S., Madden, S., & DeWitt, D. (2018). Roadtracer: Automatic extraction of road networks from aerial images. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4720–4728). · DOI · Paper page
  6. Bauer, 2010, Combining hierarchical and goal-directed speed-up techniques for dijkstra’s algorithm, ACM Journal of Experimental Algorithmics, 15
  7. Berger, 2013, Traffic sign recognition with wisard and vg-ram weightless neural networks, Journal of Network and Innovative Computing, 1, 87
  8. Berriel, 2017, Ego-lane analysis system (elas): Dataset and algorithms, Image and Vision Computing, 68, 64, 10.1016/j.imavis.2017.07.005 · DOI
  9. Berriel, 2017, Automatic large-scale data acquisition via crowdsourcing for crosswalk classification: A deep learning approach, Computers & Graphics, 68, 32, 10.1016/j.cag.2017.08.004 · DOI
  10. Broggi, 1999, Argo and the millemiglia in automatico tour, IEEE Intelligent Systems and their Applications, 14, 55, 10.1109/5254.747906 · doi:10.1109/5254.747906
  11. Broggi, 2015, Proud—public road urban driverless-car test, IEEE Transactions on Intelligent Transportation Systems, 16, 3508, 10.1109/TITS.2015.2477556 · DOI
  12. Broggi, 2012, The vislab intercontinental autonomous challenge: an extensive test for a platoon of intelligent vehicles, International Journal of Vehicle Autonomous Systems, 10, 147, 10.1504/IJVAS.2012.051250 · DOI
  13. Brown, 2017, Safe driving envelopes for path tracking in autonomous vehicles, Control Engineering Practice, 61, 307, 10.1016/j.conengprac.2016.04.013 · DOI · Paper page
  14. Brubaker, 2015, Map-based probabilistic visual self-localization, IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 652, 10.1109/TPAMI.2015.2453975 · DOI
  15. Cao, 2016, Simulation research on emergency path planning of an active collision avoidance system combined with longitudinal control for an autonomous vehicle, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of automobile engineering, 230, 1624
  16. Chen, 2017, Moving-object detection from consecutive stereo pairs using slanted plane smoothing, IEEE Transactions on Intelligent Transportation Systems, 18, 3093, 10.1109/TITS.2017.2680538 · DOI
  17. Chu, 2015, Real-time path planning of autonomous vehicles for unstructured road navigation, International Journal of Automotive Technology, 16, 653, 10.1007/s12239-015-0067-5 · DOI
  18. Chu, 2012, Local path planning for off-road autonomous driving with avoidance of static obstacles, IEEE Transactions on Intelligent Transportation Systems, 13, 1599, 10.1109/TITS.2012.2198214 · DOI · Paper page
  19. Darms, 2009, Obstacle detection and tracking for the urban challenge, IEEE Transactions on Intelligent Transportation Systems, 10, 475, 10.1109/TITS.2009.2018319 · DOI
  20. De Lima, 2013, Navigation of an autonomous car using vector fields and the dynamic window approach, Journal of Control, Automation and Electrical Systems, 24, 106, 10.1007/s40313-013-0006-5 · doi:10.1007/s40313-013-0006-5
  21. Delling, 2013, Phast: Hardware-accelerated shortest path trees, Journal of Parallel and Distributed Computing, 73, 940, 10.1016/j.jpdc.2012.02.007 · DOI
  22. Delling, 2015, Customizable route planning in road networks, Transportation Science, 51, 566, 10.1287/trsc.2014.0579 · DOI
  23. Delling, 2009, High-performance multi-level routing, The Shortest Path Problem: Ninth DIMACS Implementation Challenge, 74, 73, 10.1090/dimacs/074/04 · DOI
  24. Dias, 2014, Longitudinal model identification and velocity control of an autonomous car, IEEE Transactions on Intelligent Transportation Systems, 16, 776
  25. Diaz-Cabrera, 2015, Robust real-time traffic light detection and distance estimation using a single camera, Expert Systems with Applications, 42, 3911, 10.1016/j.eswa.2014.12.037 · DOI
  26. Dijkstra, 1959, A note on two problems in connexion with graphs, Numerische Mathematik, 1, 269, 10.1007/BF01386390 · DOI · Paper page
  27. Dolgov, 2010, Path planning for autonomous vehicles in unknown semi-structured environments, International Journal of Robotics Research, 29, 485, 10.1177/0278364909359210 · DOI · Paper page
  28. Droeschel, 2017, Continuous mapping and localization for autonomous navigation in rough terrain using a 3d laser scanner, Robotics and Autonomous Systems, 88, 104, 10.1016/j.robot.2016.10.017 · DOI
  29. Du, 2016, Drivers’ visual behavior-guided rrt motion planner for autonomous on-road driving, Sensors, 16, 102, 10.3390/s16010102 · doi:10.3390/s16010102
  30. Englund, 2016, The grand cooperative driving challenge 2016: boosting the introduction of cooperative automated vehicles, IEEE Wireless Communications, 23, 146, 10.1109/MWC.2016.7553038 · DOI
  31. Ess, 2010, Object detection and tracking for autonomous navigation in dynamic environments, International Journal of Robotics Research, 29, 1707, 10.1177/0278364910365417 · DOI · Paper page
  32. Ferguson, 2008, Motion planning in urban environments, Journal of Field Robotics, 25, 939, 10.1002/rob.20265 · DOI
  33. Fernandes, 2014, Carina intelligent robotic car: architectural design and applications, Journal of Systems Architecture, 60, 372, 10.1016/j.sysarc.2013.12.003 · DOI
  34. Galceran, 2017, Multipolicy decision-making for autonomous driving via changepoint-based behavior prediction: Theory and experiment, Autonomous Robots, 41, 1367, 10.1007/s10514-017-9619-z · DOI · Paper page
  35. Gao, 2006, Recognition of traffic signs based on their colour and shape features extracted using human vision models, Journal of Visual Communication and Image Representation, 17, 675, 10.1016/j.jvcir.2005.10.003 · DOI · Paper page
  36. Garcia-Fidalgo, 2015, Vision-based topological mapping and localization methods: A survey, Robotics and Autonomous Systems, 64, 1, 10.1016/j.robot.2014.11.009 · DOI
  37. Geisberger, 2012, Exact routing in large road networks using contraction hierarchies, Transportation Science, 46, 388, 10.1287/trsc.1110.0401 · DOI · Paper page
  38. Girao, 2016, 3d object tracking in driving environment: a short review and a benchmark dataset, 7
  39. González, 2015, A review of motion planning techniques for automated vehicles, IEEE Transactions on Intelligent Transportation Systems, 17, 1135, 10.1109/TITS.2015.2498841 · DOI · Paper page
  40. Gregor, 2002, Ems-vision: A perceptual system for autonomous vehicles, IEEE Transactions on Intelligent Transportation Systems, 3, 48, 10.1109/6979.994795 · doi:10.1109/6979.994795
  41. Gudigar, 2016, A review on automatic detection and recognition of traffic sign, Multimedia Tools and Applications, 75, 333, 10.1007/s11042-014-2293-7 · doi:10.1007/s11042-014-2293-7
  42. Gurghian, A., Koduri, T., Bailur, S. V., Carey, K. J., & Murali, V. N. (2016). Deeplanes: End-to-end lane position estimation using deep neural networksa. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 38–45). · DOI · Paper page
  43. Gutman, 2004, Reach-based routing: a new approach to shortest path algorithms optimized for road networks, ALENEX/ANALC, 4, 100
  44. Harel, 1987, Statecharts: A visual formalism for complex systems, Science of Computer Programming, 8, 231, 10.1016/0167-6423(87)90035-9 · DOI · Paper page
  45. Hata, 2017, Monte carlo localization on gaussian process occupancy maps for urban environments, IEEE Transactions on Intelligent Transportation Systems, 19, 2893, 10.1109/TITS.2017.2761774 · doi:10.1109/tits.2017.2761774
  46. Hata, 2015, Feature detection for vehicle localization in urban environments using a multilayer lidar, IEEE Transactions on Intelligent Transportation Systems, 17, 420, 10.1109/TITS.2015.2477817 · doi:10.1109/tits.2015.2477817
  47. He, 2019, Emergency steering control of autonomous vehicle for collision avoidance and stabilisation, Vehicle System Dynamics, 57, 1163, 10.1080/00423114.2018.1537494 · DOI
  48. Hilger, 2009, Fast point-to-point shortest path computations with arc-flags, The Shortest Path Problem: Ninth DIMACS Implementation Challenge, 74, 41, 10.1090/dimacs/074/03 · DOI
  49. Hornung, 2013, Octomap: An efficient probabilistic 3d mapping framework based on octrees, Autonomous Robots, 34, 189, 10.1007/s10514-012-9321-0 · DOI · Paper page
  50. Howard, 2007, Optimal rough terrain trajectory generation for wheeled mobile robots, International Journal of Robotics Research, 26, 141, 10.1177/0278364906075328 · DOI · Paper page
  51. Hu, 2018, Dynamic path planning for autonomous driving on various roads with avoidance of static and moving obstacles, Mechanical Systems and Signal Processing, 100, 482, 10.1016/j.ymssp.2017.07.019 · doi:10.1016/j.ymssp.2017.07.019
  52. Jensen, 2016, Vision for looking at traffic lights: Issues, survey, and perspectives, IEEE Transactions on Intelligent Transportation Systems, 17, 1800, 10.1109/TITS.2015.2509509 · DOI
  53. Jo, 2015, Precise localization of an autonomous car based on probabilistic noise models of road surface marker features using multiple cameras, IEEE Transactions on Intelligent Transportation Systems, 16, 3377, 10.1109/TITS.2015.2450738 · DOI
  54. Jo, 2015, Development of autonomous car-part ii: a case study on the implementation of an autonomous driving system based on distributed architecture, IEEE Transactions on Industrial Electronics, 62, 5119, 10.1109/TIE.2015.2410258 · DOI · Paper page
  55. Jung, 2005, Lane following and lane departure using a linear-parabolic model, Image and Vision Computing, 23, 1192, 10.1016/j.imavis.2005.07.018 · doi:10.1016/j.imavis.2005.07.018
  56. Kala, 2013, Multi-level planning for semi-autonomous vehicles in traffic scenarios based on separation maximization, Journal of Intelligent and Robotic Systems, 72, 559, 10.1007/s10846-013-9817-7 · DOI
  57. Koukoumidis, 2011, Leveraging smartphone cameras for collaborative road advisories, IEEE Transactions on Mobile Computing, 11, 707, 10.1109/TMC.2011.275 · DOI
  58. Lafuente-Arroyo, 2010, A decision support system for the automatic management of keep-clear signs based on support vector machines and geographic information systems, Expert Systems with Applications, 37, 767, 10.1016/j.eswa.2009.05.102 · doi:10.1016/j.eswa.2009.05.102
  59. LaValle, 2001, Randomized kinodynamic planning, The International Journal of Robotics Research, 20, 378, 10.1177/02783640122067453 · DOI · Paper page
  60. Lee, 2018, Development of a self-driving car that can handle the adverse weather, International Journal of Automotive Technology, 19, 191, 10.1007/s12239-018-0018-z · DOI
  61. Li, 2015, Real-time trajectory planning for autonomous urban driving: Framework, algorithms, and verifications, IEEE/ASME Transactions on Mechatronics, 21, 740, 10.1109/TMECH.2015.2493980 · DOI · Paper page
  62. Li, 2017, Development of a new integrated local trajectory planning and tracking control framework for autonomous ground vehicles, Mechanical Systems and Signal Processing, 87, 118, 10.1016/j.ymssp.2015.10.021 · DOI · Paper page
  63. Mertz, 2013, Moving object detection with laser scanners, Journal of Field Robotics, 30, 17, 10.1002/rob.21430 · DOI
  64. Miller, 2008, Team cornell’s skynet: Robust perception and planning in an urban environment, Journal of Field Robotics, 25, 493, 10.1002/rob.20253 · DOI
  65. Mogelmose, 2012, Vision-based traffic sign detection and analysis for intelligent driver assistance systems: Perspectives and survey, IEEE Transactions on Intelligent Transportation Systems, 13, 1484, 10.1109/TITS.2012.2209421 · DOI · Paper page
  66. Montemerlo, 2008, Junior: The stanford entry in the urban challenge, Journal of Field Robotics, 25, 569, 10.1002/rob.20258 · DOI · Paper page
  67. Mouhagir, 2017, Trajectory planning for autonomous vehicle in uncertain environment using evidential grid, IFAC-PapersOnLine, 50, 12545, 10.1016/j.ifacol.2017.08.2193 · doi:10.1016/j.ifacol.2017.08.2193
  68. Mutz, 2016, Large-scale mapping in complex field scenarios using an autonomous car, Expert Systems with Applications, 46, 439, 10.1016/j.eswa.2015.10.045 · DOI
  69. Okumura, 2016, Challenges in perception and decision making for intelligent automotive vehicles: A case study, IEEE Transactions on Intelligent Vehicles, 1, 20, 10.1109/TIV.2016.2551545 · DOI
  70. Otsu, 1979, A threshold selection method from gray-level histograms, IEEE Transactions on Systems, Man, and Cybernetics, 9, 62, 10.1109/TSMC.1979.4310076 · DOI · Paper page
  71. Paden, 2016, A survey of motion planning and control techniques for self-driving urban vehicles, IEEE Transactions on Intelligent Vehicles, 1, 33, 10.1109/TIV.2016.2578706 · DOI · Paper page
  72. Petrovskaya, 2012, Awareness of road scene participants for autonomous driving, Handbook of Intelligent Vehicles, 1383, 10.1007/978-0-85729-085-4_54 · DOI
  73. Petrovskaya, 2009, Model based vehicle detection and tracking for autonomous urban driving, Autonomous Robots, 26, 123, 10.1007/s10514-009-9115-1 · DOI · Paper page
  74. Pivtoraiko, 2009, Differentially constrained mobile robot motion planning in state lattices, Journal of Field Robotics, 26, 308, 10.1002/rob.20285 · DOI · Paper page
  75. Ramos, 2016, Hilbert maps: scalable continuous occupancy mapping with stochastic gradient descent, International Journal of Robotics Research, 35, 1717, 10.1177/0278364916684382 · DOI
  76. Samson, 1995, Control of chained systems application to path following and time-varying point-stabilization of mobile robots, IEEE Transactions on Automatic Control, 40, 64, 10.1109/9.362899 · doi:10.1109/9.362899
  77. Sarcinelli, 2019, Handling pedestrians in self-driving cars using image tracking and alternative path generation with frenét frames, Computers & Graphics, 10.1016/j.cag.2019.08.004 · DOI
  78. Schaefer, 2018, Dct maps: Compact differentiable lidar maps based on the cosine transform, IEEE Robotics and Automation Letters, 3, 1002, 10.1109/LRA.2018.2794602 · doi:10.1109/lra.2018.2794602
  79. Segal, A., Haehnel, D., & Thrun, S. (2009). Generalized-ICP. In Robotics: Science and systems, Vol. 5 (168–176). Seattle, WA. · DOI · Paper page
  80. Stallkamp, 2012, Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition, Neural Networks, 32, 323, 10.1016/j.neunet.2012.02.016 · DOI · Paper page
  81. Suhr, 2016, Sensor fusion-based low-cost vehicle localization system for complex urban environments, IEEE Transactions on Intelligent Transportation Systems, 18, 1078, 10.1109/TITS.2016.2595618 · DOI
  82. Thorpe, 1991, Toward autonomous driving: the cmu navlab. ii. architecture and systems, IEEE Expert, 6, 44, 10.1109/64.85920 · DOI
  83. Thrun, 2010, Toward robotic cars, Communications of the ACM, 53, 99, 10.1145/1721654.1721679 · DOI · Paper page
  84. Thrun, 2006, The graph slam algorithm with applications to large-scale mapping of urban structures, International Journal of Robotics Research, 25, 403, 10.1177/0278364906065387 · DOI · Paper page
  85. Thrun, 2006, Stanley: The robot that won the darpa grand challenge, Journal of Field Robotics, 23, 661, 10.1002/rob.20147 · DOI · Paper page
  86. Vivacqua, 2017, A low cost sensors approach for accurate vehicle localization and autonomous driving application, Sensors, 17, 2359, 10.3390/s17102359 · DOI
  87. Wang, 2015, Model-free detection and tracking of dynamic objects with 2d lidar, International Journal of Robotics Research, 34, 1039, 10.1177/0278364914562237 · DOI · Paper page
  88. Wegner, 2015, Road networks as collections of minimum cost paths, ISPRS Journal of Photogrammetry and Remote Sensing, 108, 128, 10.1016/j.isprsjprs.2015.07.002 · DOI
  89. Wolcott, 2017, Robust lidar localization using multiresolution gaussian mixture maps for autonomous driving, International Journal of Robotics Research, 36, 292, 10.1177/0278364917696568 · DOI
  90. Xin, 2014, China future challenge: Beyond the intelligent vehicle, IEEE Intelligent Transportation Systems Society Newsletter, 16, 8
  91. Xue, 2017, A vision-centered multi-sensor fusing approach to self-localization and obstacle perception for robotic cars, Frontiers of Information Technology & Electronic Engineering, 18, 122, 10.1631/FITEE.1601873 · DOI
  92. Yenikaya, 2013, Keeping the vehicle on the road: A survey on on-road lane detection systems, ACM Computing Surveys, 46, 1, 10.1145/2522968.2522970 · DOI
  93. Yoon, 2015, Recursive path planning using reduced states for car-like vehicles on grid maps, IEEE Transactions on Intelligent Transportation Systems, 16, 2797, 10.1109/TITS.2015.2422991 · DOI
  94. Zhang, 2013, Multiple vehicle-like target tracking based on the velodyne lidar, IFAC Proceedings Volumes, 46, 126, 10.3182/20130626-3-AU-2035.00058 · DOI
  95. Zhao, 2012, Design of a control system for an autonomous vehicle based on adaptive-pid, International Journal of Advanced Robotic Systems, 9, 44, 10.5772/51314 · DOI · Paper page
  96. Zhao, 2017, Ontology-based driving decision making: A feasibility study at uncontrolled intersections, IEICE Transactions on Information and Systems, 100, 1425, 10.1587/transinf.2016EDP7337 · doi:10.1587/transinf.2016edp7337

Cited by

See all 1,209

Metadata

Authors
Badue, C., Guidolini, R., Carneiro, R. V., Azevedo, P., Cardoso, V. B., Forechi, A., Jesus, L., Berriel, R., Paixão, T. M., Mutz, F., de Paula Veronese, L., Oliveira-Santos, T., & De Souza, A. F.
Venue
Expert Systems with Applications
Publisher
Elsevier BV
Published
2021-03
Volume
165
Pages
113816
ISSN
0957-4174 (ISSN-L)
DOI
10.1016/j.eswa.2020.113816
OpenAlex
W2909676666
Cited by
1,209
References
12
FWCI
59.01
Type
journal article
Language
en
Open access
green
Licence
  • www.elsevier.com/tdm/userlicense/1.0/ (tdm)
  • www.elsevier.com/legal/tdmrep-license (tdm)
Funders
  • Conselho Nacional de Desenvolvimento Científico e Tecnológico (311654/2019-3, Grants311654/2019-3, 311504/2017-5)
  • Fundação Estadual de Amparo à Pesquisa do Estado do Espírito Santo (84412844/2018)
  • Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Self-driving cars: A survey. https://app.cochat.ai/papers/10.1016/j.eswa.2020.113816
The paper
Badue, C., Guidolini, R., Carneiro, R. V., Azevedo, P., Cardoso, V. B., Forechi, A., Jesus, L., Berriel, R., Paixão, T. M., Mutz, F., de Paula Veronese, L., Oliveira-Santos, T., & De Souza, A. F. (2021). Self-driving cars: A survey. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2020.113816

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.