Artificial intelligence techniques for driving safety and vehicle crash prediction
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
AI, ML, and DL can be used to improve road safety by analyzing traffic patterns and identifying areas prone to accidents.
“Road Safety: AI, ML, and DL can be used to improve road safety by analyzing traffic patterns and identifying areas prone to accidents.”
From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [86]
Algorithms can be used to predict and prevent accidents by alerting drivers of potential hazards and suggesting safer routes.
“Algorithms can be used to predict and prevent accidents by alerting drivers of potential hazards and suggesting safer routes [86] .”
From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [86]
Cites
Temporal difference models and reward-related learning in the human brain
O'Doherty, J. P., Dayan, P., Friston, K., Critchley, H., & Dolan, R. J. (2003) · Neuron · doi:10.1016/s0896-6273(03)00169-7 · Cited by 1,522 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Artificial intelligence, machine learning and deep learning in advanced robotics, a review
Soori, M., Arezoo, B., & Dastres, R. (2023) · Cognitive Robotics · doi:10.1016/j.cogr.2023.04.001 · Cited by 1,131 · see citing works
- Authors
- Halim, Z., Kalsoom, R., Bashir, S., & Abbas, G.
- Affiliations
- Ghulam Ishaq Khan Institute of Engineering Sciences and Technology (PK)
- Imam Mohammad ibn Saud Islamic University (SA)
- Corresponding author
- Halim, Zahid
- Venue
- Artificial Intelligence Review
- Published
- 2016-02-23
- Volume
- 46
- Issue
- 3
- Pages
- 351-387
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W2336384609
- Cited by
- 110
- References
- 1
- Citations by year
- 2026: 9 · 2025: 14 · 2024: 11 · 2023: 15 · 2022: 9 · 2021: 15 · 2020: 9 · 2019: 17 · 2018: 7 · 2017: 4
- FWCI
- 15.71
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Artificial Intelligence Review (publishedVersion)
- Topics
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
- Physical Sciences › Engineering › Mechanical Engineering › IoT and GPS-based Vehicle Safety Systems
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Keywords
- accident prediction, road safety, driving data, driving simulator
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
