Spatiotemporal Semantic V2X Framework for Cooperative Collision Prediction

Aisha Syed; Burak Kantarci; Matthew Andrews; Murat Arda Onsu; Poonam Lohan; Sean Kennedy

REVIEW 4 major objections 4 minor 17 references

Spatiotemporal Semantic V2X Framework for Cooperative Collision Prediction

T0 review · 4 major / 4 minor · reviewed 2026-08-03 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The paper claims that V2X roadside units can transmit a single V-JEPA embedding vector per scene instead of raw video and still predict collisions with 92% accuracy and five orders of magnitude less data.

desk verdict A plausible semantic V2X demo whose headline numbers are undercut by no train/test split and an overstatement of V-JEPA's future-prediction role. read the letter →

arxiv 2601.17216 v3 pith:45OPWQQL submitted 2026-01-23 cs.CV cs.AIcs.LGeess.IV

Murat Arda Onsu , Poonam Lohan , Burak Kantarci , Aisha Syed , Matthew Andrews , Sean Kennedy This is my paper

classification cs.CVcs.AIcs.LGeess.IV

keywords semanticcommunicationV2XcollisionpredictionV-JEPAspatiotemporalembeddingsdigitaltwinattentiveprobevehiclesafety

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that a roadside camera can summarize an entire video clip into one compact semantic vector and send that vector over a V2X link, and that this vector alone lets a vehicle predict an imminent collision with 92% accuracy. The setup uses V-JEPA, a self-supervised video model that learns spatiotemporal representations by predicting masked future tokens, followed by a single-query attentive probe that pools the token embeddings into a 1,280-dimensional message. The authors build this and test it on 500 clips generated in a digital twin, with and without road-mask preprocessing. If the claim holds, collision warnings could travel with five orders of magnitude less data and sub-millisecond latency, which would remove a major bandwidth bottleneck for cooperative safety systems.

What carries the argument

The central object is V-JEPA (Video Joint Embedding Predictive Architecture), a self-supervised model pretrained to predict the embeddings of masked spatiotemporal video patches rather than reconstruct pixels. The paper keeps the ViT-H encoder frozen, passes a clip of up to 64 frames through it, and applies a single-query attentive probe that cross-attends over the token sequence to produce one 1,280-dimensional vector; a two-node linear classifier on the vehicle turns that vector into a collision/safe decision. That vector is the entire semantic message, and its fixed size is what drives both the compression ratio and the transmission latency.

What would settle it

Run the same classifier but feed it embeddings produced by V-JEPA's predictor for actual future frames at inference; if accuracy drops below the reported 92%, the predictive framing is not load-bearing and the gain comes from recognizing collision-prone patterns in observed frames.

Watch

Extended reading notes

Core claim

The central claim is that V-JEPA's spatiotemporal embeddings, pooled by a query-driven cross-attention probe into a single vector, preserve enough predictive information to classify traffic clips as collision or safe with 92% accuracy and an 8-point F1-score improvement over the raw-video baseline. The paper reports that binary road-mask preprocessing gives the best F1 (84%) and that removing 8 frames before impact still yields 84% F1, which it interprets as 400 ms of advance anticipation. Transmission of the 1×1280 embedding instead of 2048×2048 RGB frames cuts payload by five orders of magnitude, keeping V2X latency below 5 ms even for BPSK at 12 dB SNR.

Load-bearing premise

The paper assumes that embeddings produced by the frozen encoder on observed pre-collision frames are equivalent to V-JEPA's predicted future-frame embeddings, even though the predictor network is never used at inference; if that equivalence fails, the system is detecting collisions rather than forecasting them.

Editorial extensions

If this is right

With a fixed-size embedding per clip, a roadside unit can serve many vehicles with a single broadcast instead of streaming video to each one.
At 20 MHz bandwidth, the measured latencies (0.06–0.50 ms depending on modulation and byte format) all sit well below the 5 ms V2X safety threshold.
Binary road masking is the best preprocessing choice; highlighting vehicle positions with heatmaps actually lowers F1 by increasing false negatives.
Because the embedding is fixed-length while raw video grows with clip length, the compression advantage improves for longer continuous streams.
The 8-frame gap result implies that 400 ms of advance warning is achievable at 84% F1, a practical horizon for driver alert systems.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The paper's own pipeline never invokes V-JEPA's predictor network at inference; an editor's reading is that the 'future' claim should be tested by transmitting actual predicted embeddings and comparing accuracy against the frozen-encoder results.
Since all 500 clips come from a digital twin, transfer to real roadside footage is untested; a natural next step is fine-tuning on a small set of real collision clips.
The single query collapses all spatial structure into one vector; allowing a few spatial queries (e.g., one per road region) would retain geometry while adding only a small multiple to the payload.
The reported compression ratio assumes one embedding per clip; per-frame or sliding-window embeddings would reduce the gap but enable incremental risk updates.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The paper applies V-JEPA to V2X collision prediction: an RSU encodes a short clip into a 1x1280 vector, transmits that, and a vehicle-side probe/classifier predicts collision vs. safe. What's genuinely new is the QLabs digital-twin dataset with 500 labeled clips, which could be useful to the community, and the clean compression accounting—five orders of magnitude is roughly right for 64 frames of 2048x2048 RGB versus 1280 floats.

The problems are in the claims and the evaluation. The stress-test note is correct: V-JEPA's predictor network is described in Sec. III-B1 but never used in the inference pipeline of Sec. III-B2/C. The classifier sees observed frames with a gap before the collision; it is not fed predicted embeddings of the missing frames. So the 'future-frame embedding' language is more than a loose paraphrase—it misdescribes the actual system. Any frozen video encoder could produce a pooled representation from the observed clip, and the paper never isolates V-JEPA's predictive pretraining.

More damning, there is no train/test split anywhere in the evaluation. The 92% accuracy and 84% F1 appear to be in-sample numbers, since the training setup in Table I gives no split, cross-validation, or held-out set. The post-processing choice and frame gap are selected post hoc on the same data, so the '8% F1 improvement' is likely optimistic. There is also no raw-video classification baseline, so the central 'maintaining predictive accuracy vs. raw video' claim is unmeasured. The compression ratio is a back-of-envelope calculation, not a systems evaluation—no bit-rate accounting for the encoder cost or actual V2X frame structure.

This paper is for readers interested in semantic V2X and digital-twin datasets, but they should treat the numbers as illustrative only. The idea has merit—pushing heavy vision compute to RSUs and transmitting a small embedding is worth exploring—and the dataset is a real artifact. But in its current form, the manuscript does not support its claims. I would not cite it as evidence of predictive accuracy, though I might point people to the dataset.

A serious referee could ask for a proper held-out evaluation, a direct comparison with raw-video models, and removal of the 'future embedding' claim unless the predictor is actually used. I'd send it to peer review only on the expectation that those revisions are made; as is, it's a borderline reject.

Referee Report

4 major / 4 minor

Summary. The paper proposes a semantic V2X framework for collision prediction. A roadside unit (RSU) camera captures video in a QLabs digital twin, post-processes it with YOLOv11-based heatmaps/binary masks, and encodes clips with a frozen V-JEPA ViT-H encoder. An attentive probe aggregates the per-token embeddings into a single 1×D vector, which is transmitted to the vehicle over V2X; a linear classifier then outputs a collision/safe decision. The authors report 92% accuracy, 84% F1 (an 8% improvement over no post-processing), and transmission payload reductions of up to ~6.4×10^5 with INT8. The compression arithmetic and latency calculations are straightforward, but the paper's central claim that the transmitted embedding is a predictive spatiotemporal embedding of future frames is not supported by the implemented inference path, and the empirical evaluation lacks a held-out split and meaningful baselines.

Significance. If the claims were supported, the paper would make a useful contribution to task-oriented V2X communication: it demonstrates a concrete pipeline with a public pretrained video model (V-JEPA), a digital-twin dataset, transparent complexity analysis, and an explicit compression-latency trade-off. The compression ratio derivation is easy to verify, and the authors are careful to separate encoder cost from lightweight decoder cost. However, the significance is conditional on two unresolved issues: the transmitted representation is not actually a future-frame prediction, and the reported predictive accuracy is not robustly validated. Without addressing these, the paper's contribution reduces to a frozen-encoder-plus-probe baseline on a small simulated dataset.

major comments (4)

[§III-B, §III-C, Eq. (1)] The paper's central claim is that RSUs 'generate spatiotemporal semantic embeddings of future frames' (Abstract; §III-B). The implemented inference path, however, uses only a frozen V-JEPA encoder over an observed N-frame clip, followed by an attentive probe (Eq. (1)) and a linear classifier. The V-JEPA predictor described in §III-B1 is never invoked at inference, does not appear in the complexity analysis (Eqs. (4)–(7)), and the frame-gap experiments simply trim clips before the collision. The transmitted 1×D vector is thus a pooled descriptor of observed frames, not a predicted future-frame embedding. This is a load-bearing mismatch: the reported accuracy could in principle come from any frozen video encoder. Please either use the predictor at inference and compare with a non-predictive encoder, or revise the claims to describe descriptive semantic embeddings.
[§IV-A, Table I] No train/validation/test split is described. All 500 clips (385 safe, 115 collision) appear to be used for the attentive probe training, and the metrics in Fig. 5 and Table II seem to be computed on the same data. Given the class imbalance, 92% accuracy is not interpretable without held-out evaluation. Please report a fixed split or cross-validation, with per-class precision/recall/F1 and confidence intervals.
[§IV-B, Table II, Fig. 5] The evaluation configuration—binary masking and an 8-frame gap—is selected post hoc after comparing four post-processing methods and three frame gaps on the same data. This selection bias is not accounted for. Moreover, the claimed '8% F1-score improvement' is 84% vs. 76% for 'without post-processing', not a comparison to raw video transmission or a standard video classification baseline. Without such a baseline, the empirical advantage of the semantic pipeline is not established. Please report a validation-based configuration choice and add raw-video or non-semantic-embedding baselines.
[§IV-B, Eqs. (2)–(3)] The latency and compression results are computed on clean embeddings, but the paper also discusses modulation schemes and SNR (Table I and Fig. 4). No channel errors or bit errors are included in the collision prediction evaluation; the classifier sees the exact encoder output. The end-to-end performance over a noisy V2X link is therefore not demonstrated. Please either add a channel-aware evaluation or explicitly state that the accuracy/F1 results are pre-channel and that the latency/compression analysis assumes error-free transmission.

minor comments (4)

[Abstract] There are typos: 'd verse' should be 'diverse', and 'Afterwards, These embedding future frames are utilized' is ungrammatical. Also, the Abstract states '10% F1-score improvement' and 'four orders of magnitude', while the Introduction and Conclusion state '8% F1-score improvement' and 'five orders of magnitude'. Please reconcile these numbers.
[Fig. 5 text] The sentence 'Model achieves 76% F1 scores without post-processing...' is awkward. Also, the confusion matrices are reproduced in black and white; please use color or report numeric values in the caption for accessibility.
[Table II] The F1, precision, and recall values are reported without confidence intervals or significance tests. Given the small positive class (115 collisions across 500 clips), a few misclassifications change these metrics substantially.
[§III-A2] The YOLOv11 model is described as 'previously trained' and cited to [16], but the post-processing threshold or how vehicle coordinates are converted to heatmaps is not specified. Please provide implementation details or a pointer to the released data/code.

Circularity Check

0 steps flagged · score 2.0 of 10

No derivation-style circularity found: reported metrics are measured, not derived from their own inputs; only minor non-load-bearing self-citations exist. The 'future-frame embedding' wording is unsupported by the deployed frozen-encoder pipeline, but that is a soundness/claim-support gap rather than a circular reduction.

full rationale

The paper's headline quantities are empirical or arithmetic: 92% accuracy, 8% F1 improvement, and the up-to-10^5 compression factors come from measurements on the QLabs dataset and from Eqs. (2)-(3), respectively; they are not assumed into the conclusions. The encoder is an externally pretrained V-JEPA/ViT-H model [4], and the YOLOv11 detector is taken from [15]/[16], so the core pipeline is not derived from the authors' own prior claims. The only self-citations are [16] (YOLO weights for post-processing) and [17] (modulation/SNR selection), and neither is load-bearing for the collision-prediction result; there is no imported uniqueness theorem or ansatz-determined conclusion. The main concern is a claim-support gap: Sec. III-B1 describes V-JEPA's masked predictor generating future-token embeddings, but the deployed inference path in Sec. III-B2, Eq. (1), Table I, and the complexity analysis Eqs. (4)-(7) use only the frozen encoder on observed clips plus the attentive probe, so the transmitted 1xD vector is a pooled descriptor of the observed clip rather than a predictor-generated future-frame embedding. This weakens the 'predictive future-frame' framing and the stated novelty, but it is not an equation-level circularity because Eq. (1) does not reduce to the label being claimed. Similarly, selecting the best post-processing method and frame gap on the same evaluation set is test-set selection bias, a soundness issue, not a construction-level circularity. Because the principal measurements are externally grounded and the self-citations are minor, the appropriate score is low, 2 on the stated scale.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physical or architectural entities beyond a learned query token in the attentive probe, which is a standard parameter. The assumptions above are the load-bearing premises for the claimed experiment: transfer from pretrained models, the representativeness of the simulator, causal use of V-JEPA, and an idealized channel model.

free parameters (3)

Attentive probe + classifier weights = 1x1280 query, 2-class linear head
Trained on the QLabs clips; every reported accuracy/F1 depends on these fitted parameters.
Evaluation configuration (post-processing, frame gap) = binary road mask, 8 frames before collision
Chosen post hoc from Fig. 5 and Table II; directly determines the 84% F1 headline.
Fine-tuning hyperparameters = 40 epochs, AdamW, lr=0.001, batch 8
Hand-chosen; no sensitivity analysis is reported, and the probe results depend on them.

assumptions (5)

domain assumption Pretrained V-JEPA/ViT-H representations transfer to QLabs synthetic traffic.
Sec. III-B uses a frozen VideoMix2M-pretrained encoder; if features do not transfer, the probe learns from a mismatched embedding space.
domain assumption YOLOv11 detections on QLabs frames are accurate enough for masks and heatmaps.
Sec. III-A2 relies on pretrained YOLOv11 to identify vehicles; detection errors propagate into the embeddings.
domain assumption QLabs digital-twin clips are representative of real collision-prone intersections.
Sec. III-A1: the dataset is entirely synthetic; real-world generalization is assumed but not tested.
ad hoc to paper The V-JEPA predictor's future-embedding mechanism supports the transmitted representation.
Sec. III-B2 describes only an encoder plus probe; whether the predictor is used for future frames is unstated and unverified.
domain assumption V2X channel is modeled by fixed SNR values with no packet loss.
Sec. IV-B uses BPSK at 12 dB and QAM16 at 22 dB from prior work; no realistic channel errors or loss are considered.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Spatiotemporal Semantic V2X Framework for Cooperative Collision Prediction." pith.science (2026). https://pith.science/paper/45OPWQQL

@misc{pith2026260117216,
  author       = {Pith},
  title        = {Pith review of: Spatiotemporal Semantic V2X Framework for Cooperative Collision Prediction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/45OPWQQL}},
  note         = {Machine review of arXiv:2601.17216}
}

read the original abstract

Intelligent Transportation Systems (ITS) demand real-time collision prediction to ensure road safety and reduce accident severity. Conventional approaches rely on transmitting raw video or high-dimensional sensory data from roadside units (RSUs) to vehicles, which is impractical under vehicular communication bandwidth and latency constraints. In this work, we propose a semantic V2X framework in which RSU-mounted cameras generate spatiotemporal semantic embeddings of future frames using the Video Joint Embedding Predictive Architecture (V-JEPA). To evaluate the system, we construct a digital twin of an urban traffic environment enabling the generation of d verse traffic scenarios with both safe and collision events. These embeddings of the future frame, extracted from V-JEPA, capture task-relevant traffic dynamics and are transmitted via V2X links to vehicles, where a lightweight attentive probe and classifier decode them to predict imminent collisions. By transmitting only semantic embeddings instead of raw frames, the proposed system significantly reduces communication overhead while maintaining predictive accuracy. Experimental results demonstrate that the framework with an appropriate processing method achieves a 10% F1-score improvement for collision prediction while reducing transmission requirements by four orders of magnitude compared to raw video. This validates the potential of semantic V2X communication to enable cooperative, real-time collision prediction in ITS.

Figures

Figures reproduced from arXiv: 2601.17216 by the authors.

**Figure 2.** Different Post-processing Methods for Video Dataset [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** V-JEPA Pretraining and Fine-Tuning for Collision Prediction [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Average compression ratio for different video lengths [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 5.** Figure 5: Results for Different Post-Processing Techniques [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

17 extracted references · 4 linked inside Pith

[1]

Integrating LLMs with ITS: Re- cent Advances, Potentials, Challenges, and Future Directions,

D. Mahmud, H. Hajmohamed, S. Almentheri, S. Alqaydi, L. Aldha- heri, R. A. Khalil, and N. Saeed, “Integrating LLMs with ITS: Re- cent Advances, Potentials, Challenges, and Future Directions,”arXiv preprint:2501.04437, 2025

arXiv 2025
[2]

Video processing techniques for traffic flow monitoring: A survey,

B. Tian, Q. Yao, Y . Gu, K. Wang, and Y . Li, “Video processing techniques for traffic flow monitoring: A survey,” in2011 14th international IEEE conf. on intelligent transportation systems. IEEE, 2011, pp. 1103–1108

2011
[3]

Semantic communications with computer vision sensing for edge video transmission,

Y . Peng, L. Xiang, K. Yang, K. Wang, and M. Debbah, “Semantic communications with computer vision sensing for edge video transmission,” 2025. [Online]. Available: https://arxiv.org/abs/2503.07252

arXiv 2025
[4]

Revisiting Feature Prediction for Learning Visual Represen- tations from Video,

A. Bardes, Q. Garrido, J. Ponce, M. Rabbat, Y . LeCun, M. Assran, and N. Ballas, “Revisiting Feature Prediction for Learning Visual Represen- tations from Video,”arXiv:2404.08471, 2024

arXiv 2024
[5]

Traffic Congestion Detection from Surveillance Videos using Deep Learning,

G. B. Madhavi, A. D. Bhavani, Y . S. Reddy, A. Kiran, N. T. Chitra, and P. C. S. Reddy, “Traffic Congestion Detection from Surveillance Videos using Deep Learning,” inInternational Conference on Computer, Electronics & Electrical Engineering & their Applications, 2023, pp. 1–5

2023
[6]

A study on road accident prediction and contributing factors using explainable ML models: analysis and performance,

S. Ahmed, M. A. Hossain, S. K. Ray, M. M. I. Bhuiyan, and S. R. Sabuj, “A study on road accident prediction and contributing factors using explainable ML models: analysis and performance,”Transportation Research Interdisciplinary Perspectives, vol. 19, p. 100814, 2023

2023
[7]

Road car accident prediction using a machine-learning-enabled data analysis,

S. Pourroostaei Ardakani, X. Liang, K. T. Mengistu, R. S. So, X. Wei, B. He, and A. Cheshmehzangi, “Road car accident prediction using a machine-learning-enabled data analysis,”Sustainability, vol. 15/7, p. 5939, 2023

2023
[8]

Smart city transportation: Deep learning ensemble approach for traffic accident detection,

V . A. Adewopo and N. Elsayed, “Smart city transportation: Deep learning ensemble approach for traffic accident detection,”IEEE Access, vol. 12, pp. 59 134–59 147, 2024

2024

Show all 17 references

[9]

Video anomaly detection and explanation via large language models,

H. Lv and Q. Sun, “Video anomaly detection and explanation via large language models,”arXiv preprint arXiv:2401.05702, 2024

2024 arXiv
[10]

Hussain, Y

F. Hussain, Y . Ali, Y . Li, and M. M. Haque, “Real-time crash risk forecasting using Artificial-Intelligence based video analytics: A unified framework of generalised extreme value theory and autoregressive inte- grated moving average model,”Analytic Methods in Accident Resea...

2023
[11]

AI for Traffic Safety: Real-Time YOLO-Based System for Detecting Road Incidents,

J. Nowicki and M. M. Omwenga, “AI for Traffic Safety: Real-Time YOLO-Based System for Detecting Road Incidents,” in2025 IEEE International Conf. on Electro Information Technology, 2025, pp. 1–6

2025
[12]

Collision Prediction of Smart Cities Using Co- simulation,

A. Karre and A. Azim, “Collision Prediction of Smart Cities Using Co- simulation,”Procedia Computer Science, vol. 224, pp. 205–214, 2023

2023
[13]

An improved object detection and trajectory prediction method for traffic conflicts analysis,

L. Yang, A. S. A. Bin Mohamed, and M. K. B. M. Ali, “An improved object detection and trajectory prediction method for traffic conflicts analysis,”Promet-Traffic&Transportation, vol. 35/4, pp. 462–484, 2023

2023
[14]

[Online]

Quanser,Quanser Python API, 2024. [Online]. Available: https: //docs.quanser.com/quarc/documentation/python/getting started.html

2024
[15]

YOLOv11: An Overview of the Key Architectural Enhancements,

R. Khanam and M. Hussain, “YOLOv11: An Overview of the Key Architectural Enhancements,” 2024. [Online]. Available: https: //arxiv.org/abs/2410.17725

2024 arXiv
[16]

Leveraging multimodal-llms assisted by instance segmen- tation for intelligent traffic monitoring,

M. A. Onsu, P. Lohan, B. Kantarci, A. Syed, M. Andrews, and S. Kennedy, “Leveraging multimodal-llms assisted by instance segmen- tation for intelligent traffic monitoring,” in2025 IEEE Symposium on Computers and Communications (ISCC), 2025, pp. 1–6

2025
[17]

Semantic Edge–Cloud Communication for Real-Time Urban Traf- fic Surveillance with ViT and LLMs over Mobile Networks,

——, “Semantic Edge–Cloud Communication for Real-Time Urban Traf- fic Surveillance with ViT and LLMs over Mobile Networks,”IEEE Transactions on Network Science and Engineering, pp. 1–17, 2025

2025

Pith tools

Reviewed August 3, 2026 · model on record in the stance chip above.

[1] [1]

Integrating LLMs with ITS: Re- cent Advances, Potentials, Challenges, and Future Directions,

D. Mahmud, H. Hajmohamed, S. Almentheri, S. Alqaydi, L. Aldha- heri, R. A. Khalil, and N. Saeed, “Integrating LLMs with ITS: Re- cent Advances, Potentials, Challenges, and Future Directions,”arXiv preprint:2501.04437, 2025

arXiv 2025

[2] [2]

Video processing techniques for traffic flow monitoring: A survey,

B. Tian, Q. Yao, Y . Gu, K. Wang, and Y . Li, “Video processing techniques for traffic flow monitoring: A survey,” in2011 14th international IEEE conf. on intelligent transportation systems. IEEE, 2011, pp. 1103–1108

2011

[3] [3]

Semantic communications with computer vision sensing for edge video transmission,

Y . Peng, L. Xiang, K. Yang, K. Wang, and M. Debbah, “Semantic communications with computer vision sensing for edge video transmission,” 2025. [Online]. Available: https://arxiv.org/abs/2503.07252

arXiv 2025

[4] [4]

Revisiting Feature Prediction for Learning Visual Represen- tations from Video,

A. Bardes, Q. Garrido, J. Ponce, M. Rabbat, Y . LeCun, M. Assran, and N. Ballas, “Revisiting Feature Prediction for Learning Visual Represen- tations from Video,”arXiv:2404.08471, 2024

arXiv 2024

[5] [5]

Traffic Congestion Detection from Surveillance Videos using Deep Learning,

G. B. Madhavi, A. D. Bhavani, Y . S. Reddy, A. Kiran, N. T. Chitra, and P. C. S. Reddy, “Traffic Congestion Detection from Surveillance Videos using Deep Learning,” inInternational Conference on Computer, Electronics & Electrical Engineering & their Applications, 2023, pp. 1–5

2023

[6] [6]

A study on road accident prediction and contributing factors using explainable ML models: analysis and performance,

S. Ahmed, M. A. Hossain, S. K. Ray, M. M. I. Bhuiyan, and S. R. Sabuj, “A study on road accident prediction and contributing factors using explainable ML models: analysis and performance,”Transportation Research Interdisciplinary Perspectives, vol. 19, p. 100814, 2023

2023

[7] [7]

Road car accident prediction using a machine-learning-enabled data analysis,

S. Pourroostaei Ardakani, X. Liang, K. T. Mengistu, R. S. So, X. Wei, B. He, and A. Cheshmehzangi, “Road car accident prediction using a machine-learning-enabled data analysis,”Sustainability, vol. 15/7, p. 5939, 2023

2023

[8] [8]

Smart city transportation: Deep learning ensemble approach for traffic accident detection,

V . A. Adewopo and N. Elsayed, “Smart city transportation: Deep learning ensemble approach for traffic accident detection,”IEEE Access, vol. 12, pp. 59 134–59 147, 2024

2024