Accident Anticipation via Temporal Occurrence Prediction
Authors: Tianhao Zhao
Yiyang Zou
Zihao Mao
Peilun Xiao
Yulin Huang
Hongda Yang
Yuxuan Li
Qun Li
Guobin Wu
Yutian Lin
Venue: NeurIPS 2025 poster ()
Keywords: Accident Anticipation
Primary area: deep_learning

Abstract: Accident anticipation aims to predict potential collisions in an online manner, enabling timely alerts to enhance road safety. Existing methods typically predict frame-level risk scores as indicators of hazard. However, these approaches rely on ambiguous binary supervision—labeling all frames in accident videos as positive—despite the fact that risk varies continuously over time, leading to unreliable learning and false alarms. To address this, we propose a novel paradigm that shifts the prediction target from current-frame risk scoring to directly estimating accident scores at multiple future time steps (e.g., 0.1s–2.0s ahead), leveraging precisely annotated accident timestamps as supervision. Our method employs a snippet-level encoder to jointly model spatial and temporal dynamics, and a Transformer-based temporal decoder that predicts accident scores for all future horizons simultaneously using dedicated temporal queries. Furthermore, we introduce a refined evaluation protocol that reports Time-to-Accident (TTA) and recall—evaluated at multiple pre-accident intervals (0.5s, 1.0s, and 1.5s)—only when the false alarm rate (FAR) remains within an acceptable range, ensuring practical relevance. Experiments show that our method achieves superior performance in both recall and TTA under realistic FAR constraints. Project page: https://happytianhao.github.io/TOP/

Accident Anticipation via Temporal Occurrence Prediction
Tianhao Zh
Yiyang Zou'
Zihao Mao
Peilun Xiao
Yulin Huang*
Hongda Yang*
Yuxuan Li
Qun Li
Guobin Wu
Yutian Lin
School of Computer Science, Wuhan University
*Zhongguancun Academy, Beijing, China
D Chuxing
(happytianhao, yutian. @inu edu..edu.com
Abstract
Accident anticipation aims to predict potential collisions in online manner, enabling timely alerts to enhance road safety. Existing methods typically predict frame-level risk scores as indicators of hazard. However, these approaches rely on ambiguous binary supervision- labiliin all frames accident videos as posi- tive-despite the fact that risk varies continuously over time, leading to unreliable learning and false alarms. To address this, we propose a novel paradigm that shifts the prediction target from current-frame risk scoring directly estimating accident scores at multiple future time steps (e.g., 0.1s-2.0s ahead), leveraging precisely annotated accident timestamps a snippet-level encoder to jointly model spatial and temporal dynamics, and a Transformer-basee temporal decoder that predicts accident scores for all future horizons simultaneously using dedicated temporal queries. Furthermore, we in- troduce refined evaluation protocol that reports Time-to-Accident (TTA) and evalutee at multiple pre-accident intervals (0.5s, 1.0s, and 1.5s) only when the false alarm rate (FAR) remains within an acceptable range, ensuring practical relevance. Experiments show that our method achieves superior per- formance both recall and TTA under realistic FAR constraints. Project page: https::/happytianiee .gihhb 1o/TOP/
supervision. Our method employs
Introduction
Driving accidents pose significant threat public safety, resulting in substantial human casualties and economic losses. This issue often arises when drivers, due to fatigue or distraction, fail to notice potential hazards their surroundings, ultimately resulting accidents. Recently, the task of accident anticipation 2 has been widely studied to analyze the risk in driving scenarios captured by dashcams in an online manner, assessing the likelihood of an impending accident with a risk score, shown in Figure T (a). If the risk score exceeds preset threshold, the system can promptly alert the driver to take evasive action, thereby reducing the chances an accident or mitigating its severity.
g train models to predict frame-wise risk scores using binary supervision: all frames from accident videos are labeled as 1, and all frames from safe videos as To reflect the intuition that risk increases near the crash, these methods typically assign exponentially decaying loss weights to frames based on their temporal distance to the accident- receive lower weights, while those near the crash receive higher ones. However, this approach treats
Previous works
5
6
frames
"Corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Fo
risk score Fo
RNN
RNN
Snippet Encoder
accident scores multiple time steps the future, indicating whether and when an accident will occur...
a, B2 Bg By
Temporal Decoder
(a) Previous works
(b) Ours
Figure I: Comparison of accident anticipation paradigms. (a) Previous works predict a single risk score for the current frame, which is ambiguous and hard supervise accurately. (b) Our method predicts a sequence of accident scores at multiple future time steps (e.g., 0.1s, 0.2s, 2.0s ahead), where each score indicates the likelihood of an accident occurring exactly at that future time.
risk static binary signal, ignoring its dynamic and continuously varying nature. In reality, risk levels differ significantly across frames even within the same accident sequence, and assigning a uniform label of fails to capture these temporal nuances. Such imprecise supervision misguides learning and often leads to unreliable predictions or false alarms.
We observe that, unlike ambiguous risk labels, the timestamp of actual accident occurrence can be precisely annotated in real-world driving videos. To leverage this reliable supervision, we shift the prediction target from per-frame risk scores to directly forecasting future accident timing. Specifically, as shown in Figure (b), our model outputs a sequence of accident scores at multiple future time steps fai, 02 . @r (e.g., 0.1s, 0.2s. ..... 2.0s ahead), where each score a, indicates the model's confidence that an accident occurs exactly at that time. During training, only the score at the ground- truth accident time is labeled as all others are O. At inference, an alert is triggered if any score exceeds a preset threshold, indicating an impending collision. This formulation offers two key advantages: (1) it uses precise temporal annotations for more stable and accurate training, and (2) by modeling when an accident may occur-rather than just whether the scene is "risky" yields more interpretable and actionable predictions.
To implement this paradigm online, we adopt an encoder-decoder architecture that processes current and past frames to predict accident scores for multiple future time steps (e.g., 0.1s, 0.2s. ...., 2.0s 6 7 91. which typically use method employs snippet-level
ahead). as shown in Figure [ (b). Unlike previous works [3] 4
[1]
frame-level encoders with RNNs to model temporal dynamics, encoder that jointly captures spatial and temporal information across short clips of consecutive frames, enabling comprehensive understanding of object positions, speeds, and motion trajectories. Furthermore, we design a Transformer-basee temporal decoder that simultaneously predicts accident scores for all future time steps using distinct learnable temporal queries-eade corresponding to a specific future horizon-to explicitly model accident likelihood at each time offset, supporting accurate and efficient frame-by-frame online prediction.
To evaluate the effectiveness of accident anticipation methods, previous works 3 primarily use AP (Average Precision), AUC (Area Under the ROC Curve), and TTA (Time-to- Accident). However, we observe that in real-world applications, an excessively high false alarm rate (FAR)-eg..- exceeding false alarm per causes disruptive alerts that are unacceptable; under such conditions, high recall may stem from indiscriminate alarming rather than genuine prediction, rendering recall and TTA misleading. To address this, propose a novel evaluation protocol that reports mean recall and TTA only when FAR is within an acceptable range. Furthermore, we evaluate recall at different pre-accident intervals (0.5s, 1.0s, and 1.5s before crashes) for a more granular assessment of anticipative capability. Finally, we identify limitations in existing TTA calculations that yield inflated values and propose a more reasonable approach, as detailed in Section 4
5
Our contributions can be summarized as follows:
We propose a novel accident anticipation paradigm that shifts from predicting ambiguous fer-frame risk scores to directly estimating accident scores at multiple future time steps (e.g., 0s 0.2s, 2.0s ahead), leveraging precise accident timestamps as supervision for more accurate and interpretable predictions.

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We design an effective encoder-decoder architecture featuring snippet-level encoder to jointly capture spatial and temporal features from driving scenarios, and Transformer-ban based temporal decoder that predicts accident scores for all future time steps simultaneously using dedicated temporal queries, enabling online frame-by-frame anticipation.
We introduce a refined evaluation protocol that computes recall and Time-to-Accident (TTA) only when the false alarm rate (FAR) within an acceptable range, evaluates recall at multiple pre-accident intervals (0.5s, 1.0s, 1.5s), and adopts an improved TTA calculation method that avoids inflated values, offering a more reliable and practical assessment.
2 Related Work
2.1 Temporal Modeling and Aftention-Baser Approaches
Early works on accident anticipation primarily rely on recurrent architectures to model temporal dynamics in dashcam videos. DSA [3] introduced the first large-scale dataset (DAD) and combined object-level and frame-level features with an RNN to predict per-frame risk scores, using an exponen- tially decaying loss that emphasizes frames closer to the accident. Subsequent methods enhanced temporal modeling through attention mechanisms. ACRA [2] proposed a soft-attention RNN to capture spatial and appearance interactions between the event-triggering agent and its surroundings. AdaLEA [0]] improved early anticipation via an adaptive loss weighting strategy, while DSTA (II] introduced dynamic spatial-temporal attention to focus on relevant regions over time.
More recently, transformer-basee architectures have emerged. AAT-DA [2] integrates driver attention
into transformer framework to jointly model spatial and temporal cues. LATTE [13] further advances temporal modeling by combining multiscale spatial features with memory- based attention and auxiliary self-attention for long-range dependency capture. Meanwhile, XAI
[14] employs
a GRU-based network to learn spatio-temporal relations, and RARE [15) achieves efficiency by
leveraging intermediate features from single pre-true traindridd detector. THAT-Net [16] enhances motion
understanding by fusing optical flow with spatial-temporal filtering to suppress distracting motions.
2.2 Graph-Based and Relational Reasoning Methods
To explicitly model interactions among traffic participants, several works adopt graph-baser represen- tations. GSC (17) formulates accident anticipation as graph learning problem with spatio-temporal continuity constraints. Graph(Graph) [18] proposes nested graph architecture to capture hierarchical agent relationships. DAA-GNN 19 introduces a dynamic attention- augmmetn-auggmeneed graph network that adaptively weights interactions among detected entities. CRASH (20) designs an object-aware module that prioritizes high-risk agents by computing their spatial-temporal relationships.
UString [6] combines relational learning with uncertainty quantification, using Bayesian neural networks model the stochasticity in agent interactions its newly collected CCD dataset. AM- Net (21) employs an attention- gundded multistream fusion strategy to localize hazardous agents by integrating appearance and motion cues.
2.3 Multimodal and Emerging Paradigms
Recent efforts explore richer input modalities and novel learning frameworks. AccNet 22 and CCAF-Net (33 incorporate monocular depth cues to enable 3D-aware scene understanding. fusing RGB and depth features for improved risk prediction. DADA (8] and DADA-2000 [24] frame anticipation as driver attention prediction task, linking gaze behavior to accident likelihood.
Other innovative directions include reinforcement learning and language grounding. DRIVE (7) models risk prediction as a Markov decision process and uses deep reinforcement learning with visual explanations. CAP 9 establishes a multimodal benchmark for cognitive accident anticipation, integrating behavioral and visual signals. DEDBM 25] fuses dashcam videos with textual accident reports in a dual-branch architecture, enabling cross-modal knowledge transfer. Most recently, WWW (26) leverages large language models (LLMs) to jointly reason about the what, when, and where potential accidents, marking shift toward interpretable, language-augmented anticipation systems.
3

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Safe scenario
C. Co BOS
Snippet Encoder
Temporal Decoder
Anomaly appeared
Bip
accident
Accident occurred
3
2
10
11
12 14 15
16
18
18
20
observed
unobserved
Figure 2: Overview of our encoder-decoder method. We feed the observed frames into the snippet encoder and use the temporal decoder to predict accident scores for unobserved future frames at multiple time steps, where the accident score label is for the accident frame and 0 otherwise.
Method
3.1 Problem Definition
Accident anticipation involves online analysis of dashcam footage to determine whether an accident likely to occur the near future. If there is a potential risk of an accident, the system promptly alerts the driver to take precautionary measures, helping to reduce both the likelihood and severity of collisions.
Specifically, given the current frame fu and past frames (f-1,f-2,-.. captured by the dashcam from the driving scenarios, previous works determine whether accident will occur the near future by predicting a risk score at the current time to. When the risk score To exceeds a preset threshold r, the system will automatically trigger an alert
the driver.
In contrast, our method assesses the likelihood future accidents by predicting a sequence of accident scores {a1, d2..... } of accident occurring multiple future time steps {t1, t2, ... When any of the accident scores a, (i 2 1) at a certain moment tę exceeds preset threshold т, it indicates that the model predicts accident will occur time 44- Consequently, the system will automatically trigger an alert at the current time to to warn the driver to take evasive action.
In this task, the model's optimization objective is to maximize the recall rate and earliness of accident anticipation while ensuring that the false alarm rate (FAR) does not exceed an acceptable limit.
3.2 Temporal Occurrence Prediction
To anticipate accidents more accurately, we propose a novel paradigm that focuses on predicting sequence of accident scores of accident occurring multiple future time steps, rather than simply outputting a risk score of the current frame, providing a more interpretable and reliable anticipation.
To implement this paradigm, designed encoder-decoder model, shown in Figure 2 The structure details are as follows:
Snippet encoder. To better understand the movement of objects in driving scenarios, we take as a snippet input to the
the current frame fo along with past frames f. model, where 5 is the length of the snippet. Then we employ 3D CNN instead of architectures of frame-level encoders with RNNs (widely adopted in previous works) as a snippet encoder to simultaneously capture the spatial and temporal information within the snippet. Next, we only apply spatial pooling to the features output by the snippet encoder, in order to preserve their temporal resolution, resulting in features { 2-1...., 2_ (S-1)) 13 }. where each features correspond to the time
steps 4-,,-1--- (_(5-1)) ). respectively. In this way, the model can not only understand the motion of objects but also establish one-to-one correspondence between different frames and their corresponding features.

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Temporal decoder. In order to predict the temporal sequence of accident scores {a1, ......, or } of an accident occurring at multiple time steps {t,, 12, tr), where the length of the sequence to predict, in the future based on features ( (20, 1............................... 2-(S-1) extracted by the snippet encoder from frames of time steps to.t-1,..... (s)-) we designed a temporal decoder with reference to the transformer decoder [27]. Specifically, to distinguish between different time steps of the temporal sequence in the future, we define T different temporal queries {41, ................................ 9r to represent T time steps f2......... }r} in the future following the current timestamp to as the reference. Next, we feed the features fzo 2....... (---1) output from the snippet encoder as the memory into the temporal decoder. Then, we feed temporal queries {q1, ......, qr into the temporal decoder to predict the temporal sequence of accident scores {a1, 0g ..,,, JT) an accident occurring time steps {t1, .............................. }.
Sampling strategy. During training, we randomly sample continuous segment of S frames from the accident video as the input snippet for the model. Let f-(s-1) 13 be the starting frame of the snippet, then the last frame is fo, which denotes the current frame. To ensure the relevance of training data, snippets are sampled only from frames occurring at or before the accident, while frames after the accident are excluded.
During testing, we adopt a sliding window approach to sample snippets from all available frames in the video, including those before, during, and after the accident. Specifically, we slide a window of consecutive frames across the entire video sequence with a fixed stride, ensuring comprehensive evaluation of the model's performance over time. This allows the model to make predictions at every time step, reflecting real-world deployment scenarios where the exact timing of an accident is unknown.
Labeling strategy. During training, given a snippet across time steps (lo, 4-1,,11,1--- (,.,,,,-)s)) as input, the model outputs the sequence of accident scores {aj, .2,.... Ør } at multiple time steps {t1, . strr J. If the accident occurrence time step tA falls within this range, i.e., 1 1 A < T. we assign its label 444 as I, while setting the labels of all other time steps ye (1 S t S T. + A) 0, as shown in F The model is then trained using the Binary Cross-Entropy (BCE) loss function to optimize its predictions:
1 T
LBC
w_ a.
log(1-ai) at)
(1)
where w4 is the weight of the positive one.
4 Evaluation Metrics
Previous works 9 28 29 30 31 primarily used AP (Average Precision), AUC (Area Under the ROC Curve), and TTA (Time-To-Accideee Ac-Accident) to evaluate accident anticipation methods. However, unlike traditional binary classification tasks, we observe that in real-world applications, excessively high false alarm rates (FAR) can cause unacceptable disturbances to drivers. Therefore, when FAR exceeds reasonable threshold, comparing recall rates and TTA becomes meaningless. Existing metrics allow FAR to range from to which could lead to suboptimal model selection for practical deployment. To address this, we introduce a threshold X for FAR and only evaluate cumulative recall and TTA when FAR remains below A. Furthermore, while current metrics measure overall anticipation capability, they fail to assess performance at specific pre-accident time intervals. Following the approach in (3), we analyze anticipation recall rates at different time intervals before accidents. Finally, we identify limitations in conventional TTA calculation methods and propose improved alternative. The detailed metrics are described below:
Area under the ROC curve (AUC). We employ AUC (Area Under the ROC Curve) to calculate the average recall rate of accident anticipation models under varying false alarm rates (FAR). Notably, when the false alarm rate exceeds a certain level, comparing recall rates across different models becomes meaningless. Therefore, we specifically compute the average recall rate only when the false alarm rate remains below predefined threshold X, denoted A illustrated in Equation 2 and
5

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Figure
n
(TPR, + -1R;-1) (FPR; - FPR,-1), S A),
AUC
(2)
2
where FPR equivalent the false alarm rate (FAR) and TPR is equivalent the recall rate, is set to 0.1 by default.
Additionally, to evaluate the capability of the accident anticipation models different horizons before an accident occurs, we extracted video clips from 0.5s-1.0s, 1.0s-1.5s, and 1.5s-2.0s before the accident as positive samples, while capturing an equal number of 0.5s-long video segments from accident-free driving scenarios as negative samples. We then calculated the AUC* for different time intervals, denoted AUC). AUC1.0e Oe+ and AUC1. (e.g., AUC1. represents the model's capability in anticipating accidents 1.5 seconds before they occur). Finally, we computed the model's mean AUC* using Equation
mAUCA AUC, 0.5a + AUC + AUC)
3
Tome-io-Accident (TTA). We adopt Time- To-Accident (TTA) to evaluate the earliness the accident anticipation. Specifically, for each frame in accident video, if the model's predicted anomaly score exceeds a preset threshold 7, an alarm will be triggered, and the time gap (in seconds) between this alarm moment and the actual accident occurrence is recorded TTA. Generally, as the threshold r decreases, both TTA and the false alarm rate (FAR) increase simultaneously. Therefore, we only compute the mean TTA when the false alarm rate remains below X, as illustrated in Equation
MTTA>
TTA, (FPR, S A).
(4)
However, as illustrated in Figure 3 the TTA calculation method used in pre- vious works can lead to misleadingly high TTA values. Specifically, when a model raises a false alarm in safe driving scenario-i.e.. it predicts an accident that is not causally linked to any actual future crash the method still computes TTA based on the time until subsequent accident (even unrelated), This results in artificially inflated TTA estimates, sometimes ex- ceeding 3 seconds, despite the predic- tion being a false alarm.
"false alarm"
TTA (Previous works)
predicted risk score
threshold
TTA (Ours)
anomaly appear
accident occur
Figure 3: Comparison of the TTA calculation approaches between previous works and our method. We only compute TTA after the anomaly appears, because the moment the anomaly appears is the earliest time annotated by humans as being predictive of the accident. If a model issues an alert earlier than this moment, the perceived risk is typically unrelated to the actual accident that eventually occurs. We consider such alerts as false alarms rather than early warn- ings. However, previous works did not account for this when calculating TTA, leading to inflated TTA values sometimes even exceeding 3 seconds.
To address this issue, we propose a revised TTA calculation method: only compute TTA for alarms trig- gered after the anomaly appears. This is because the anomaly appearance time is the earliest moment annotated by humans reliably indicating an impending accident. Alarms issued before this point are considered false alarms, not valid early warnings, as the perceived risk lacks a causal connection to the eventual accident. Under our method, the maximum achievable average TTA is bounded by the average interval between anomaly appearance and accident occurrence in the dataset-1.55 seconds on CAP [9] and 1.66 seconds on DADA [ Moreover, if the model fails to predict accident at all, its TTA is set to 0, ensuring fair and meaningful evaluation.

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5 Experiments
5.1 Experimental Setup
33 a large-scale ego-view traffic accident benchmark collected from public sources including existing datasets (CCD [6. A3D A) DOTA [ DADA-2000 (24) and video platforms (YouTube, Bilibili, Tencent), encompassing diverse weather (sunny, rainy, snowy, foggy). lighting (day, night), scenes (highway, tunnel, mountain, urban, rural), and road types (arterial roads, curves, intersections, T-junctions, ramps) -which enables robust evaluation of model generalizability, in contrast to prior works that primarily train on limited datasets like DAD and CCD. MM-AU consists of two subsets: CAP [9] with 11,727 videos (2, 195,613 frames) and DADA 8 with 2,000 videos (658,476 frames), both providing annotations for 58 accident categories and temporal labels for key events (" ("anomaly appear". "accident occur", and "accident end"). We refined and validated the frame rates and annotations for all ego-involved accidents, and selected approximately 20% of the data as the test set; for evaluation, we extract clips from the first frame to the "anomaly appear" frame as negative samples compute the false alarm rate (FAR), and clips from "anomaly appear" to "accident occur" as positive samples to assess anticipation recall and Time-to-Acciden (TTA).
Datasets. We conduct experiments on the MM-AU dataset
Implementation details. We preprocess input videos by resizing each frame to 224 x 224 and resampling at 10 FPS, so that each frame corresponds to 0.1s time interval. During training, sample snippets only from the period before the accident occurs; during testing, we apply a sliding window over the entire video. Each input snippet consists of 5 5 consecutive frames, which are fed into a snippet-level encoder (SlowOnly [34], initialized with ImageNet pre-trained weights) to extract spatiotemporal features. The model then predicts sequence of accident scores of length T 20, corresponding to future time steps from 0.1s to 2.0s ahead. To decode these scores, we employ a Transformer-based temporal decoder with 2 layers and cosine positional encodings as queries for each future horizon. We optimize the model using SGD with batch size of 64 on 8 NVIDIA 4090 GPUs. The binary cross-entropy loss is weighted with w++ 10 for positive samples, and the initial learning rate is set to 0.01, decayed to 10% of its value every 20 epochs over 50 total epochs.
5.2 Quantitative Results
Quantitative comparison. We evaluate our method against prior approaches and baselines on the CAP [9] and DADA [8] datasets under unified experi- mental protocol. For fair comparison, all meth- ods-including CAP 19) DRIVE 77. DSTA (αI) and GSC 17) trained and tested using the same data splits and evaluation metrics (Section 4.. As baselines, we include (1) ResNet+LSTM architec- ture that predicts a single per-frame risk score (iden- tical to Experiment I in our ablation study), and (2)
Anticipation ROC (Ours)
1.0
0.9
True Positive Rate (Recal))
0.8
0.7-
0.6
0.5
0.4
a variant of our full model without the temporal de- coder.
0.2
AUC
0.55 before accidents
0.0s before accidents
0.1
0.1
1.0s before accidents
As shown in Table our method achieves signifi- cant gains on CAP. At the 0.0s whei the task reduces to precise accident detection-our AUC reaches 0,8381, far surpassing all prior works and the baseline (0.4357). This demonstrates our model's strong capability in recognizing the exact moment of collision. At short-term horizons (0.5s), we obtain an AUC of 0.6747, nearly doubling the best prior result (GSC: 0.4177). The improvement gradually dimin- ishes at longer horizons (1.0s and 1.5s), where risk signals are inherently weaker, yet our method still maintains the highest performance (AUC: 0.3982 and 0.2 respectively), yielding a mean AUC (mAUC) of 0.4290 and the best mean Time-to- Ao-cident (mTTA~ = 0.8644 s). These trends are further visualized in the ROC curves of Figure g which show consistently higher recall across all false alarm rates, especially near the accident onset.
1.5s before accidents
0.0
0.1 0,2 0.3
0.4 0.5 0.6 0.7 0.8 0.9 1.0
False Positive Rate (False Alarm Rate)
Figure 4: ROC curves of our method on CAP 9 at different accident anticipation
horizons: 0.0s, 0.5s, 1.0s, and 1.5s before the accident.
7

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Table Quantitative results comparison of different methods on the CAP 9 dataset.
Method
AUC
AUC t
AUC t
AUC
mAUC +
mTTA 0.1 (s)
CAP
0.0421
0.0400
0.0296
0,0373
0.0357
0.6372
DRIVE
0.1288
0.1167
0.1079
0.1231
0.1159
0.3954
DSTA
0.5593
0.3862
0.2817
0.1913
0.2864
0.8039
GSC
0.6093
0.4177
0.2969
0.1994
0.3046
0.8165
Baseline
0,4357
0.3938
0.2770
0.1777
0.2829
0.5389
Ours
0.8381
0.6747
0.3982
0.2141
0.4290
0.8644
Table 2: Quantitative results comparison of different methods on the DADA 8 dataset.
Method
AUC
A t
AUC t
AUC
mAUC
mTTA D.1 (s)
CAP 9
0.0317
0.0365
0.0670
0,0643
0.0560
0.4964
DRIVE
0.1005
0.0628
0.0770
0.0885
0.0761
0.2257
DSTA
0.4728
0.3276
0.2207
0,1345
0.2276
0.6952
GSC 17
0.5142
0.3495
0.2382
0.1392
0.2423
0.7034
Baseline
0.3411
0.3046
0.2099
0.1251
0.2132
0.4138
Ours 0.7903
0.5669
0.2877
0.1399
0.3315
0.8848
Similar patterns are observed DADA (Table Our method achieves 0.7903 AUC 0.0s and 0.5669 0.5s, substantially outperforming previous methods. Although the gains at 1.0s-1.5s are more modest, our mAUC (0.3315) and mTTA (0.8848 s) remain the best, confirming the robustness of our approach across datasets. Overall, the results validate that shifting supervision from ambigu- risk labels to precise future accident timing enables more accurate and reliable anticipation, particularly in the critical moments just before a crash.
Threshold variation. To evaluate the robustness of our accident anticipation capability under different false alarm constraints, we vary the FAR tolerance threshold X defined as the maximum allowable false alarm rate for computing metrics. When À = 1, no constraint is applied, and the evaluation aligns with conventional protocols used in prior works. As decreases (e.g., to 0.1 or 0.01), metrics are computed only over predictions that satisfy the stricter FAR requirement.
As shown in Table under the practical setting of 0.1 (i.e., FAR 10%), our method achieves mAUC of 0.4290 on CAP and 0.3315 on DADA, with strong short-term anticipation performance (AUCI-I 0.6747 and 0.5669, respectively). Even under stringent constraint of 0.01 (FAR < 1%), our model retains non-trivial performance, particularly at the 0.5s horizon (AUC = 0.3371 on CAP, 0. 1183 on DADA).
Notably, as decreases, AUC drops more sharply at longer horizons (1.0s-1.5s) than at shorter ones 00.0s-0.5s) indicating that early false alarms effectively suppressed under tight FAR constraints. This confirms that our model's early predictions are often spurious, while its near-crash anticipation remains reliable-a behavior aligned with real-world safety requirements. The corresponding reduction in mTTA reflects the inherent trade-off between false alarm suppression and anticipation lead time.
5.3 Ablation Study
Temporal occurrence prediction. Our temporal occurrence prediction (TOP) module replaces the conventional single risk score with sequence of accident scores over future time steps (0.1s-2.0s) enabling explicit modeling of when an accident may occur. As shown in Table adding TOP (Experiment III vs. I) improves performance the 0.0s horizon (AUC from 0.4357 0.5700), but yields limited gains at longer horizons, suggesting that TOP alone-wiln without strong spatiotemporal modeling-strugglee to capture early precursors of accidents. However, when combined with snippet encoder (Exxperiment IV), TOP contributes significantly to overall anticipation accuracy, confirming that forecasting future accident timing provides more informative supervision than frame- level risk scoring.

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Table 3: Quantitative results comparison across different À of our method on the CAP 9 and DADA 8 datasets.
Datasel
AUC
AUCb.
AUC1.0. 1
AUC1,5 f
mAUC*
m A (s) 1
0,9760
0.9389
0.8377
0.7164
0.8310
1.5908
CAP 9
0.1
0.8381
0.6747
0.3982
0.2141
0.4290
0.8644
0.01
0.7329
0.3371
0.0882
0.0227
0.1494
0.4394
0.9666
0,8946
0.7399
0.6400
0.7582
1.4328
DADA g
0.1
0.7903
0,5669
0.2877
0.1399
0.3315
0.8848
0.01
0.3177
0.1183
0.0203
0.0068
0.0484
0.5153
Table Ablation study on the CAP 9 encoder.
dataset. TOP: temporal occurrence prediction; SE: snippet
Experiment
TOP
SE
AUC
AUC
AUCT.
AUC f
mAU 1
mTTA t.1
(Bascline)
0,4357
0.3938
0.2770
0.1777
0.2829
0.5389
II
V
0.6027
0.5550
0.3607
0.1931
0.3696
0.7330
III
0.5700
0.3432
0.2284
0.1721
0.2479
0.4595
(Ours)
0.8381
0.6747
0.3982
0.2141
0.4290
0.8644
Snippet encoder. The snippet encoder (SE) processes short clips consecutive frames to jointly model spatial and temporal dynamics, which is crucial for understanding motion patterns and scene evolution. Comparing Experiment (SE only) with the baseline (I), SE alone boosts AUCg.5s from 0.3938 to 0.5550 and mTTAA from 0.5389s to 0. 0.7330s. More importantly, when SE is combined with TOP (Experiment IV), the model achieves the best results across all metrics: AUC 1 = 0.8381, mAuC 0.4290, and mTTA 0.8644s. This demonstrates that SE and TOP are highly complementary provides rich spatiotemporal context, while TOP leverages precise temporal supervision to produce well-calibrated anticipation outputs.
Conclusion
In this work, we propose a novel accident anticipation paradigm that shifts the prediction target from ambiguous per--rertee per-frame risk scores to directly estimating accident scores multiple future time steps (c.g., 0.1s-2.0s ahead), leveraging precisely annotated accident occurrences as supervision. Our method employs snippet encoder and Transformer-based temporal decoder to jointly model spatiotemporal dynamics and enable online anticipation. Furthermore, we introduce a practical evaluation protocol that reports recall and l'ime-to-Accident (TTA) only under acceptable false alarm rates, aligning metrics with real-world deployment needs. Experiments show that our approach achieves state-of-the-at performance, particularly in the critical moments just before crash.
Limitations. While our method significantly improves anticipation accuracy near the accident onset, its performance at longer horizons (e.g.. >1.0s) remains limited, indicating challenges in capturing subtle early precursors. Additionally, even under constrained false alarm rates, spurious alerts can still occur in complex scenes, which may affect user trust. These issues point to key directions for future work.
Potential societal impacts. Our system has the potential to enhance road safety by providing timely warnings. However, over-reliance on automated alerts might reduce driver vigilance. Careful human- in-the-loop design and user education are essential to maximize safety benefits while mitigating behavioral risks.
Acknowledgments and Disclosure of Funding
This work was supported by the National Natural Science Foundation of China (Grant No. 62471344), the Zhongguancun Academy (Project No. 20240304), and the CCF-DiDi GATA Collaborative Research Funds for Young Scholars.

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A
Qualitative Results
Annotations:
Sale acenario
Aromayy appeared
Accident cocumed
0.90
threchold-00.5
Time-To Ime-To-kocident
(8)
-2.0
-1.9
-1.8
-1.7
-15
-15
14
1.3
-2
-11
-10
-089
-08
-07
0.6
0.5
0.4
003
-22
-011
0.0
0.1
02
0.3
0.4
(a) case where the ego vehicle collides with motorcyclist who suddenly emerges from blind spot.
Annokations:
Sala scenario
Anomayy appeared
Accidant oocureed
#trechold-0.5
Time- To-Acadeen
(8)
-2.0
-199
-1.8
-1.7
-188
-55
-44
-13
-12
-.1
-10
-0.9
-08
-0.7
0.6
00.5
-04
-0.3
-022
-0.1
0.0
0.1
0.2
03
0.4
(b) A case where the ego vehicle collides with a cyclist who suddenly cuts across road.
Annotation::
Safe sconario
Anomaly appeared
Accident oocureed
D.51
threchold-0.8
(8)
-2.0
-19
-1.8
-1.
-1.8
-15
-14
-.3
-12
-11
-10
-0.9
488
007
466
005
0.4
-0.
-02
-0.1
0.0
0.1
0.2
0.3
0.4
(c) A case where the ego vehicle collides with suddenly lane- channchnggng car.
Armotation::
Sale scenario
Anomaly appeared
Accident cocuree
threchold-00.
Amm-To-Acciden
(8)
-.0
-19
-18
-17
-18
-55
-14
-1.9
-12
-111
-10
0.9
.88
0..
-06
0.55
-04
-0.3
-0.2
-0.1
0.0
0.2
0.8
0.4
(d) case where ego vehicle rear-ends the lead car.
Each case shows the trend of the maximum accident score predicted over future time steps; an alarm is triggered if this maximum exceeds the threshold.
Figure 5: Qualitative results on CAP 9
present the qualitative results of our method on the CAP dataset 9 in Figure 5 where different colors denote the temporal annotations in the dataset. Four distinct cases are demonstrated: (a) and (b) involve ego-vehicle collisions with vulnerable road users, while (c) and (d) involve collisions with other vehicles.
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We trigger an alarm if any accident score within the 2.0s prediction horizon exceeds a predefined threshold (e.g., 0.5). cases (a) and (b), the model issues an alert immediately when a motoreyclist emerges from a blind spot cyclist. begins to cut across the road. In cases (c) and (d), the model accurately responds sudden lane changes and abrupt braking of the lead vehicle, demonstrating reliable anticipation under diverse hazardous scenarios.
The average Time-to-Accident (TTA) across these four cases is 1.0s, consistent with the typical duration between anomaly onset and collision in real-world accidents. Notably, previously reported stem from flawed calculation methods that count false alarms far before the anomaly as valid early predictions -rather than genuine long-term anticipation capability. This underscores the necessity of our revised TTA metric.
TTAs exceeding seconds in prior works 7 [
Furthermore, we observed inconsistencies in the dataset's "anomaly appear" annotations. For instance, cases (b) and (d) were labeled early, while case (c) was labeled too late. Such subjectivity introduces noise when using anomaly onset as training or evaluation boundary.
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Justification: We propose a novel accident anticipation paradigm that shifts from traditional per-frame risk score prediction to directly estimating accident scores multiple future time steps (c.g., 0. 11s-2.0s ahead), leveraging precisely annotated accident occurrences as supervision. This provides more accurate training signals and yields more interpretable and temporally grounded predictions.
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Justification: Our method development does not involve LLMs any important, original, or non-standard components.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non standardd components.
Please refer to our LLM policy http::://eerrippsscc/ooeeeeeeree for what should or should not be described.
20


Reviews:
[{"review_id": "L6B62U3Ou1", "reviewer": "Reviewer_frVz", "summary": "This paper proposes a novel paradigm for driving accident anticipation that predicts the probability of accident occurrence at multiple future timestamps (ranging from 0.1 to 2.0 seconds), rather than estimating frame-level anomaly scores. The technical framework consists of two main components: 1) A snippet encoder to capture spatial-temporal dynamics from past driving frames, and 2) A Transformer-based temporal decoder that estimates accident occurrence probabilities across multiple future steps simultaneously. Additionally, the paper introduces a refined evaluation protocol that measures recall rate and Time-to-Accident (TTA) only under acceptable false alarm rates. Extensive experiments demonstrate the superiority of the proposed method over prior state-of-the-art approaches.", "questions": "1. The authors should elaborate on how their approach would perform across diverse environmental conditions and with alternative sensor configurations, potentially providing preliminary results on additional datasets or theoretical justification for expected performance in these scenarios.\n2. The baseline comparison is limited to only DRIVE (2021) and CAP (2024), which are not representative of the current state-of-the-art in accident anticipation. The authors should justify this selection and consider expanding comparisons to include more recent and competitive methods to strengthen the evaluation of their approach's relative performance.\n3. The authors should provide deeper insights into how far in advance predictions should be made to maximize benefits, and explain the theoretical underpinnings of how predictions at different time steps influence each other, which would strengthen the methodological contribution.\n4. The fixed probability threshold approach for warning triggering appears simplistic for real-world deployment. The authors should address how this threshold was determined and discuss the implementation of adaptive thresholding mechanisms that could account for varying driving contexts (urban vs. highway, different times of day, weather conditions).", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strength**：\n\n1. First, the research direction of this paper - Accident Anticipation - is highly significant for autonomous driving with substantial practical application value and social impact.\n2. The paper is well-written with clear illustrations and rigorous mathematical formulations to describe the proposed method, making it easy for readers to understand the methodology. Additionally, shifting from anomaly scores to explicit temporal probability predictions for accidents is well-motivated, providing more interpretable and stable supervision.\n3. The proposed encoder-decoder framework effectively captures spatio-temporal dynamics, and the proposed evaluation protocol aligns better with real-world deployment needs where excessive false alarms are unacceptable. Furthermore, extensive experiments were conducted on representative datasets, achieving significant performance improvements.\n\n**Weakness**：\n\n1. The experimental section only uses the MM-AU datasets, an ego-view traffic accident dataset. The paper does not discuss how the method might generalize to different environments (e.g., different geographies, lighting/weather conditions, or sensor types).\n2. Regarding baseline selection, only DRIVE (2021) and CAP (2024) were chosen, which is too limited, and these baselines are not state-of-the-art anymore.\n3. In the method design section, there is insufficient theoretical derivation and analysis of issues such as how far in advance predictions can yield maximum benefits and the dependency relationships between predictions at different time steps. The paper lacks understanding of the mechanisms behind the method.\n4. For warning triggering, a fixed probability threshold is used, but there is no explanation for this choice, lacking an adaptive mechanism from a system perspective. Fixed thresholds may not be applicable in different scenarios (e.g., urban/highway, day/night), potentially leading to a surge in false alarms or missed reports in some real-world applications.", "was_revised": true, "final_justification": "The authors have addressed most of my concerns in the rebuttal, and their clarifications have improved my understanding of the work. Therefore, I would like to raise my score to 4.", "rebuttal": "Dear Reviewer frVz,\n\nThank you for your thorough review and constructive feedback on our submission \"Accident Anticipation via Temporal Occurrence Prediction\". We highly appreciate your recognition of the significance of our research direction, the clarity of our paper, and the novelty of our proposed paradigm and evaluation protocol. Your insights are invaluable in helping us improve the quality of our work.\n\nWe have carefully considered each of your points and would like to address them as follows:\n\n## **Response to Weaknesses and Questions:**\n**1. Generalization to Diverse Environments and Sensor Configurations:**\n\nWe agree with your valid point that discussing the generalization capabilities of our method is crucial. Here are our points:\n- **MM-AU Includes Diverse Environments:** As stated in [1], MM-AU is a large-scale ego-view traffic accident dataset collected from public accident datasets (CCD, A3D, DoTA, DADA-2000) and various video stream sites (YouTube, Bilibili, Tencent), including diverse weather conditions (sunny, rainy, snowy, foggy), lighting conditions (day, night), scenes (highway, tunnel, mountain, urban, rural), and roadways (arterial, curve, intersection, T-junction, ramp). That's why we chose to train our method on it (unlike previous works primarily use CCD and A3D). Besides, experimental results on the validation subset of MM-AU achieved state-of-the-art, further demonstrating its generalizability across diverse environments.\n- **Nexar Dataset:** We further test our method on the Nexar dataset released by Nexar Inc.. The dataset includes 1,500 annotated video clips of real-world traffic scenarios, encompassing different environmental conditions (lighting, weather) and scene types (urban, rural, highway, etc.). We compare our method with solutions of participants in the Kaggle competition \"Nexar Dashcam Collision Prediction Challenge\" hosted by Nexar Inc. As shown in Table 2 below, our method outperforms the Champion solution on both public and private leaderboards.\n- **Sensor Types:** Although our current experiments use dashcam data, the snippet encoder could be replaced with other encoders to accommodate input from different sensor modalities (e.g., LiDAR, radar). However, the lack of annotated traffic accident datasets of other sensor modalities prevents us from validating our method's performance with other sensor modalities. \n\n[1] J. Fang et al., “Abductive ego-view accident video understanding for safe driving perception”, in CVPR, 2024.\n\n**2. Baseline Selection and State-of-the-Art Comparison:**\n- **More Baselines:** We further compared our method with two recent SOTA open-source baselines. We trained their model with the same implementation details on MM-AU (CAP-DATA) and demonstrate the experimental results in the table below. The quantitative results in the table demonstrate that our method outperforms recent SOTA methods.\n\nTable 1: Quantitative results comparison of different methods on the CAP-DATA dataset.\n| Method | [C]/[J] | Year | AUC$^{0.1}_{0.0s}$ ↑ | AUC$^{0.1}_{0.5s}$ ↑ | AUC$^{0.1}_{1.0s}$ ↑ | AUC$^{0.1}_{1.5s}$ ↑ | mAUC$^{0.1}$ ↑ | mTTA$^{0.1}$ (s) ↑|\n| :- | :- | :- | :-: | :-: | :-: | :-: | :-: | :-: | \n| DRIVE [2] |ICCV|2021| 0.1288 | 0.1167 | 0.1079 | 0.1231 | 0.1159 | 0.3954 | \n| CAP [3]  |ITSM|2024| 0.0421 | 0.0400 | 0.0296 | 0.0373 | 0.0357 | 0.6372 | \n| DSTA [4]  |TITS|2022| 0.5593 | 0.3862 | 0.2817 | 0.1913 | 0.2864 | 0.8039 | \n| GSC [5]  |TIV|2023| 0.6093 | 0.4177 | 0.2969 | 0.1994 | 0.3046 | 0.8165 | \n| Baseline |-|-| 0.4357 | 0.3938 | 0.2770 | 0.1777 | 0.2829 | 0.5389 | \n| **Ours**  |-|-| **0.8739** | **0.6979** | **0.3892** | **0.2014** | **0.4295** | **0.9327** | \n\n[2] W. Bao et al., “Drive: Deep reinforced accident anticipation with visual explanation”, in ICCV, 2021.\n\n[3] J. Fang et al., “Cognitive accident prediction in driving scenes: A multimodality benchmark”, in ITSM, 2024.\n\n[4] M. M. Karim et al., “A dynamic spatial-temporal attention network for early anticipation of traffic accidents”, in TITS, 2022.\n\n[5] T. Wang et al., “GSC: A graph and spatio-temporal continuity based framework for accident anticipation”, in TIV, 2023.\n\n- **Kaggle Competition (CVPR DriveX Workshop, 2025):** As demonstrated in section B of our supplemental material, we compared our method with other participants' solutions in the **Nexar Dashcam Crash Prediction Challenge [6]**, which is a Kaggle competition of accident anticipation on the Nexar dataset hosted by Nexar Inc., and the Top-3 solutions are announced on the CVPR DriveX Workshop, 2025. As shown in the table below, our method outperforms all the solutions from 282 participants and 237 teams by a large margin, further confirming that our method is state-of-the-art.\n\nTable 2: Public and private scores of our method on the leaderboard of the Nexar Dashcam Crash Prediction Challenge [6].\n| Team | Public Score | Public Rank | Private Score | Private Rank |\n| :- | :-: | :-: | :-: | :-: |\n| MBTDF1 | 0.815 | 4 | 0.788 | 18 |\n| biascia | 0.813 | 5 | 0.826 | 9 |\n| Benchmark | 0.804 | 6 | 0.842 | 5 |\n| Hi F | 0.779 | 13 | 0.853 | 4 |\n| mzwager | 0.853 | 3 | 0.855 | 3 |\n| siuuuuuuuu | 0.886 | 2 | 0.864 | 2 |\n| Paul Endresen | 0.898 | 1 | 0.872 | 1 |\n| **Ours** | **0.924** | - | **0.893** | - |\n\n[6] D. C. Moura et al., “Nexar dashcam collision prediction dataset and challenge”, 2025.\n\n**3. Theoretical Derivation and Analysis:**\n\nThank you for your valuable suggestions. We will conduct an in-depth analysis of these two points.\n- **How far in advance can predictions yield maximum benefits:**\n\nIn our paper, we set the prediction horizon to 2.0 seconds according to our experience. We acknowledge that this selection lacks theoretical analysis. To this end, we conduct comparison experiments of the \nprediction horizon (ranging from 1.0s to 3.0s). As shown in the following table, when the prediction horizon decreases to 1.0s, although the late anticipation performance improves a little, the ability of early anticipation degrades a lot. When the prediction horizon increases to 3.0s, the overall anticipation performance drops. Therefore, we come to the conclusion that setting the prediction horizon to 2.0 seconds yields maximum benefits.\n\n| Prediction Horizon | AUC$^{0.1}_{0.0s}$ ↑ | AUC$^{0.1}_{0.5s}$ ↑ | AUC$^{0.1}_{1.0s}$ ↑ | AUC$^{0.1}_{1.5s}$ ↑ | mAUC$^{0.1}$ ↑ | mTTA$^{0.1}$ (s) ↑|\n| :- | :-: | :-: | :-: | :-: | :-: | :-: | \n|1.0s|**0.8634**|**0.6962**|0.3306|0.0742|0.3670|0.6206|\n|1.5s|0.8496|0.6843|0.3845|0.1564|0.4084|0.7453|\n|2.0s|0.8381|0.6747|**0.3982**|0.2141|**0.4290**|**0.8644**|\n|2.5s|0.8124|0.6685|0.3957|0.2207|0.4283|0.8604|\n|3.0s|0.7953|0.6456|0.3842|**0.2263**|0.4187|0.8532|\n\n- **Dependency relationships between predictions at different time steps:**\n\nOur Transformer-based temporal decoder inherently models the dependencies between predictions at different time steps. The self-attention mechanism in the Transformer allows the model to capture complex, non-linear relationships across the entire predicted sequence. Each output probability is informed by the context of all other positions in the sequence, allowing for a holistic and interdependent prediction. We will revise the Method section of our paper to further elaborate on this dependency.\n\n**4. Fixed Probability Threshold for Warning Triggering:**\n\nYou raised a critical point regarding the fixed probability threshold, and we agree that an adaptive mechanism is more aligned with real-world deployment.\n- **Determination of Fixed Threshold:** Following the way of all previous works, the current fixed threshold was empirically determined based on achieving a balance between recall and false alarm rates on the validation set of the MM-AU dataset, aiming for an \"acceptable\" false alarm rate as per our refined evaluation protocol. This was a pragmatic initial choice for validating our method and a fair comparison with other methods.\n- **Adaptive Thresholding Mechanisms:** We fully concur that a fixed threshold is a simplification for real-world scenarios. We propose to explore adaptive thresholding mechanisms in future work, potentially leveraging:\n    * **Context-aware Thresholds:** Implementing different thresholds based on driving contexts (e.g., urban vs. highway, day vs. night, speed limits). This would require richer contextual information to be fed into the system or a context-aware classification module.\n    * **Uncertainty Estimation:** Integrating uncertainty quantification into our probability predictions. Warnings could then be triggered not just by a high probability, but also by a high probability with low uncertainty, or by dynamically adjusting the threshold based on the predicted uncertainty for a given scenario."}, {"review_id": "SyteAbfBd5", "reviewer": "Reviewer_TYgU", "summary": "This study proposes a driving accident anticipation framework that leverages a snippet encoder to capture spatiotemporal dynamics and a Transformer-based decoder to estimate accident probabilities across multiple future time steps. The main contributions are two-fold: (1) the introduction of a refined evaluation protocol that computes recall and Time-to-Accident (TTA) only under acceptable false alarm rates, thereby ensuring practical relevance for real-world deployment; and (2) the development of a predictive framework capable of anticipating the probability of accident occurrence at various future timestamps, offering a more temporally comprehensive risk assessment.", "questions": "Please provide a more comprehensive and structured discussion of:\n    Works that use TTA or similar temporal safety metrics (e.g., SurvivalNet, DRAG, DAD).\n    Studies that model risk as a sequence or through latent continuous variables rather than per-frame labels (e.g., LSTM-based risk predictors, probabilistic risk mapping).\n\nWhile the paper emphasizes that driving risk \"evolves gradually,\" it’s unclear how this is formalized or measured in your method. What specific representation or signal is used to capture \"gradual\" risk (e.g., a risk score? probability distribution over time?).", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\nClear and coherent writing: The manuscript is well-organized and accessible to both experts and readers less familiar with the topic.\nRelevant and impactful topic: Addressing gradual risk evolution is an important advancement beyond current per-frame models.\nWell-structured experiments and results: The use of standard metrics like recall and TTC enables benchmarking, even if their context is underexplored in the related work.\n\nWeaknesses\nIncomplete Related Work Section: The manuscript fails to cite or discuss relevant existing works that:\nUse recall and Time-To-Accident (TTA) as evaluation metrics in accident anticipation.\nMove beyond frame-level analysis by modeling driving risk as a temporally evolving or probabilistic process (e.g., through sequence modeling, latent risk scoring, or uncertainty-aware methods).\n\nThis omission makes it difficult to assess the novelty and contribution of the proposed approach. Readers are left without a solid foundation to understand how the presented method compares with or advances the state of the art.", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer TYgU,\n\nThank you for your very positive and constructive review of our submission \"Accident Anticipation via Temporal Occurrence Prediction\". We greatly appreciate your recognition of our paper's clarity, the relevance and impact of our topic, and the well-structured experiments. Your confidence in our work is truly encouraging.\n\nWe acknowledge your valuable feedback regarding the **Related Work section** and the need for a more explicit discussion on how our method formalizes \"gradual risk evolution.\" We agree that addressing these points will significantly strengthen our manuscript, clarify our contributions, and provide a more comprehensive context for readers.\n\nWe have carefully considered your questions and would like to address them as follows:\n\n## **Response to Weaknesses and Questions:**\n\n**1. Comprehensive Discussion of Related Works:**\nThank you for your valuable comments. In response, we have carefully surveyed and reviewed relevant prior works. We will make sure to include all of them explicitly in the updated manuscript to provide a more comprehensive contextualization of our contribution.\n\n- **Works using recall and Time-To-Accident (TTA) as evaluation metrics:** To the best of our knowledge, all accident anticipation works use recall (AUC or AP) and Time-To-Accident (TTA) (or Time-To-Collision (TTC)) as evaluation metrics. For \ninstance, DSA [1] first proposed a dataset and baseline for accident anticipation using AP and TTA. AdaLEA [2] proposed an adaptive loss for early anticipation using AP and TTC. DSTA [3] proposed to utilize dynamic temporal attention and GRU using AP and TTA. DADA [4] proposed to utilize driver attention for anticipation using AUC. CAP [5] proposed a multimodality benchmark for anticipation using AUC, AP, and TTA. Graph(Graph) [6] proposed a nested graph-based framework for early accident anticipation using AP and TTA.\n\n[1] F.-H. Chan et al., “Anticipating accidents in dashcam videos”, in ACCV, 2017.\n\n[2] T. Suzuki et al., “Anticipating traffic accidents with adaptive loss and large-scale incident DB”, in CVPR, 2018.\n\n[3] M. M. Karim et al., “A dynamic spatial-temporal attention network for early anticipation of traffic accidents”, in TITS, 2022.\n\n[4] J. Fang et al., “Dada: Driver attention prediction in driving accident scenarios”, in TITS, 2021.\n\n[5] J. Fang et al., “Cognitive accident prediction in driving scenes: A multimodality benchmark”, in ITSM, 2024.\n\n[6] N. Thakur et al., “Graph(Graph): A nested graph-based framework for early accident anticipation”, in WACV, 2024.\n- **Works modeling driving risk as a temporally evolving or probabilistic process:** UString [7] proposed an uncertainty-based accident anticipation model that employs spatio-temporal relational learning. It sequentially predicts traffic accident probabilities by leveraging Bayesian neural networks to address the intrinsic variability of latent relational representations. DRIVE [8] regarded risk prediction as a Markov decision process and proposed a deep reinforcement learning approach with visual explanation. AccNet [9] formulated the risk by monocular depth-enhanced 3D modeling. CRASH [10] formulated the risk with object detection and context modeling. A branch of work [1, 2, 3, 5, 6, 7, 8] regarded risk evolving as a sequence over time and used RNNs to predict risk. However, the risks before the accident lack accurate labels for training.\n\n[7] W. Bao et al., “Uncertainty-based traffic accident anticipation with spatio-temporal relational learning”, in ACMMM, 2020.\n\n[8] W. Bao et al., “Drive: Deep reinforced accident anticipation with visual explanation”, in ICCV, 2021.\n\n[9] H. Liao et al., “Real-time accident anticipation for autonomous driving through monocular depth-enhanced 3D modeling,” in AA&P, 2024.\n\n[10] H. Liao et al., “CRASH: Crash recognition and anticipation system harnessing with context-aware and temporal focus attentions,” in ACMMM, 2024.\n\n**2. Formalizing and Measuring \"Gradual Risk Evolution\":** Thank you for the question. We formalize the \"gradual evolution\" of driving risk as a sequence of future accident probabilities over time. Specifically, for each timestamp, our method predicts a probability sequence at each future timestamp, rather than a single anomaly score. This allows us to model how the likelihood of an accident changes and increases as the vehicle approaches the accident time. We will revise the Method section to clearly explain this issue.\n\nAs illustrated in Table 1, the label of the probability sequence evolves from 2.0s before the accident to 0.1s before the accident, like a one-hot vector iteratively shifting left. That's how we formalize the \"gradual evolution\" of driving risk, while a single anomaly score cannot formalize this evolution.\n\n|Current Timestamp|0.1s|0.2s|0.3s|0.4s|0.5s|0.6s|0.7s|0.8s|0.9s|1.0s|1.1s|1.2s|1.3s|1.4s|1.5s|1.6s|1.7s|1.8s|1.9s|2.0s|\n|:-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n|-3.2s|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|\n|-2.0s|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|**1**|\n|-1.4s|0|0|0|0|0|0|0|0|0|0|0|0|0|**1**|0|0|0|0|0|0|\n|-0.7s|0|0|0|0|0|0|**1**|0|0|0|0|0|0|0|0|0|0|0|0|0|\n|-0.1s|**1**|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|\n\nTable 1: Labels of the probability sequence at every timestamp within a future period (0.1s-2.0s), where the accident timestamp is 0.0s."}, {"review_id": "uBmxSuBZSB", "reviewer": "Reviewer_nKX6", "summary": "The paper proposes a novel accident anticipation paradigm that shifts the prediction target from frame-level anomaly scores to the probability of future accident occurrences at multiple time steps, enabling more precise supervision and more interpretable predictions. Specifically, it employs a snippet encoder and a Transformer-based temporal decoder, and introduces a new evaluation protocol that considers false alarm rates.", "questions": "1.\tOne of the main contributions of the paper is the shift “from frame-level anomaly scores to the probability of future accident occurrences at multiple timestamps.” **However, is there a clear distinction between ‘scores’ and ‘probability’?** The authors appear to use “score” and “probability” interchangeably throughout the paper (e.g., lines 136–138 on page 4: “we propose…**predicting the probabilities** …rather than simply outputting the **anomaly scores** of…”; lines 287–288 on page 7: “For our baseline, we adopt a CNN+RNN architecture to predict an **anomaly score** and …”), yet on lines 332–334 of page 8 they explicitly state: “This validates that explicit **probability prediction** at future time steps provides more reliable signals than **anomaly score-based** methods.”\n\n2.\tThe authors repeatedly state that “when FAR exceeds a reasonable threshold, comparing recall rates and TTA becomes meaningless.” **However, they should report the actual false alarm rates (FAR) of existing methods and compare them with their own approach**, especially since this is a key motivation of the paper. Simply stating this claim without quantitative evidence weakens the argument.\n\n3.\tIn paper [1], it is pointed out that “Intuitively, the penalty of failing to anticipate an accident at a frame very close to the accident should be higher than the penalty at a frame far away from the accident.” That is, as a probability prediction task, the predicted probability should increase as it approaches the accident frame. However, in this paper, the same loss function (Equation 1) is applied to both positive and negative samples. Given the temporal nature of the task, is there any mechanism in the loss function to ensure that frames closer to the accident are predicted with higher probabilities? Additional explanation on this point would strengthen the paper.\n\n4. Do the authors plan to release the code and pretrained models upon acceptance to support reproducibility of the results?\n\n[1] Anticipating Accidents in Dashcam Videos.\n\nNote：If the authors can address the above issues, I would consider raising my score.", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**: \nThis paper proposes a novel accident anticipation paradigm and introduces a new evaluation protocol that accounts for false alarm rates, thereby improving the accuracy and timeliness of accident prediction.\n\n**Weaknesses**: \nThe writing in the paper lacks clarity in some parts; please refer to the “Questions” section for details", "was_revised": true, "final_justification": "The author’s explanation is convincing and has resolved all my questions. The author has also promised to provide open-source code to ensure the reproducibility of the results.", "rebuttal": "Dear Reviewer nKX6,\n\nThank you for your valuable time and insightful feedback on our submission \"Accident Anticipation via Temporal Occurrence Prediction\". We appreciate your recognition of our novel accident anticipation paradigm and the introduction of a new evaluation protocol that accounts for false alarm rates. Your comments are crucial for improving the clarity and rigor of our work.\n\nWe have carefully considered each of your points and would like to address them as follows:\n\n## **Response to Questions:**\n**1. Distinction Between \"Score\" and \"Probability\":**\nThank you for pointing out this important issue. We would like to clarify that in our paper, “anomaly score” and “probability” refer to the same concept—a quantitative value indicating the likelihood of an accident. We will revise the manuscript to standardize the terminology for clarity.\n\n- **Clarification of the main contribution:**\nThe key distinction we aim to highlight is not the difference between “score” and “probability,” but rather the shift from predicting **a single score/probability** to predicting **a temporal sequence of such scores/ probabilities**. Specifically, previous methods predict one anomaly score/risk probability per frame, indicating the general likelihood of an accident. In contrast, for each frame, our method predicts a sequence of future probabilities, such as the likelihood of an accident occurring at +0.1s, +0.2s, ..., up to +2.0s. This allows our model to explicitly capture the temporal evolution of accident risk over a future horizon.\n\nWe appreciate the reviewer’s observation and will ensure that this distinction is clearly articulated in the revised manuscript.\n\n**2. Reporting False Alarm Rates (FAR) of Existing Methods:**\nThank you for the valuable comment. We agree that quantitative evidence is essential to support our claim regarding the limitations of existing methods under high false alarm rates (FAR). In practice, the false alarm rate is threshold-dependent, making direct comparisons between methods at a fixed threshold inappropriate. Consequently, to ensure a fair comparison, we follow a consistent evaluation protocol: we report the FAR of each method when it achieves 80% early warning recall rate. The results of this evaluation are summarized in Table 1.\n\nTable 1: False Alarm Rate (FAR) of each method when achieving 80% early warning recall.\n| Method | Drive [1] | CAP [2] | DSTA [3] | GSC [4] | Baseline | Ours |\n|:-|:-:|:-:|:-:|:-:|:-:|:-:|\n| FAR $\\downarrow$ | 0.59 | 0.47 | 0.23 | 0.19 | 0.25 | **0.01** |\n\n[1] W. Bao et al., “Drive: Deep reinforced accident anticipation with visual explanation”, in ICCV, 2021.\n\n[2] J. Fang et al., “Cognitive accident prediction in driving scenes: A multimodality benchmark”, in ITSM, 2024.\n\n[3] M. M. Karim et al., “A dynamic spatial-temporal attention network for early anticipation of traffic accidents”, in TITS, 2022.\n\n[4] T. Wang et al., “GSC: A graph and spatio-temporal continuity based framework for accident anticipation”, in TIV, 2023.\n\nOur method (\"Ours\") achieves a remarkably low False Alarm Rate of 0.01. This is significantly lower than all other compared methods: Drive [1] (0.59), CAP [2] (0.47), DSTA [3] (0.23), GSC [4] (0.19), and the Baseline (0.25). This result strongly demonstrates our method's superior capability in controlling false alarms. In real-world applications, a low False Alarm Rate is crucial because it indicates that the system can more accurately identify genuine accident risks without frequently issuing erroneous alerts. An excessively high false alarm rate (like 0.59 and 0.47) can lead to user fatigue and distrust, thereby diminishing the system's practical utility. That's why we proposed a new evaluation protocol that explicitly measures recall rate and Time-to-Accident (TTA) *only under acceptable false alarm rates*. We will include this evaluation and the corresponding results in the revised manuscript to substantiate our motivation and improve clarity.\n\n**3. Temporal Exponential Decay for Penalty Weights of Loss Function:**\nThank you for your thoughtful question regarding the temporal weights of our loss function, especially considering the idea that earlier anticipations might warrant lower penalties. Such mechanisms (e.g., [1]'s implementation) give a lower penalty weight to the loss for earlier anticipations of accident videos in a temporal exponential decay manner ($e^{-\\max(0,y-t)}$), while the labels remain 1 for all frames in the accident video, as shown in Equation (12).\n\n$$L_p(\\{a_t\\}) = \\sum_t -e^{-\\max(0,y-t)} \\log(a_t^0), \\quad (12)$$\n\nSimilarly, we can apply this mechanism (Exponential Weights) to our method. We conducted ablation studies of this mechanism on the Baseline method (in the way of predicting an anomaly score) and our method (in the way of predicting a sequence of probabilities at multiple timestamps in the future), as shown in the following table. This mechanism could bring a large improvement to the Baseline method of late anticipations (e.g., AUC$^{0.1}_{0.5s}$). However, it only brings a subtle improvement to our method. This is because we model the supervision signal of different timestamps before the accident independently (see Rebuttal 2 for Reviewer TYgU), while previous works model them as the same \"anomaly score\" (the uncorrected label of early anticipations may degrade the ability of late anticipations). Therefore, this experimental result proves that our method is more robust to the penalty weights of the loss function.\n\nTable 2. Impact of Exponential Temporal Weights on the baseline and our method.\n| Method | Exponential Weights | AUC$^{0.1}_{0.0s}$ ↑ | AUC$^{0.1}_{0.5s}$ ↑ | AUC$^{0.1}_{1.0s}$ ↑ | AUC$^{0.1}_{1.5s}$ ↑ | mAUC$^{0.1}$ ↑ | mTTA$^{0.1}$ (s) ↑|\n| :- | :- | :-: | :-: | :-: | :-: | :-: | :-: | \n| Baseline |✕| 0.3897 | 0.3042 | 0.2359 | 0.1842 | 0.2414 | 0.5452 | \n| Baseline |✓| 0.5142 | 0.3967 | 0.2463 | 0.1652 | 0.2694 | 0.5427 | \n| Ours  |✕| 0.8381 | 0.6747 | 0.3982 | 0.2141 | 0.4290 | 0.8644 | \n| Ours  |✓| 0.8538 | 0.6853 | 0.3931 | 0.2057 | 0.4280 | 0.9036 | \n\n**4. Code and Pretrained Model Release:**\nYes, we **plan to release our code, pretrained models, and detailed documentation upon acceptance** to support full reproducibility. Our method achieves state-of-the-art (SOTA) performance on both the MM-AU and Nexar datasets. More importantly, we propose a more reasonable and standardized evaluation protocol for accident anticipation. By releasing our implementation along with strong SOTA baselines, we aim to facilitate fair comparison and accelerate progress in future research on this task."}, {"review_id": "DL2sKSFt85", "reviewer": "Reviewer_o9V3", "summary": "This paper introduces a method for traffic accident anticipation in terms of spatiotemporal snippet encoder and multiple time-stamp anticipation. Additionally, for the purpose of practical traffic application usage, this paper also considers to assign improved evaluation protocols with recall rate and TTA. This paper shows the effectiveness of performance and anticipation earliness in the experimental section.", "questions": "- In Tables 1 & 2, the scores show improved performance, but it is unclear whether this improvement is due to the proposed method itself or other factors. If possible, could you clarify, by means of a correspondence table, what detection methods, architectures, or other factors were used for the proposed vs. the conventional methods [7, 8, 9]? The reviewer also believes that these details should be clearly documented in the paper. If the reviewer has overlooked this, he sincerely apologizies. Please point out where the paper described the points?", "limitations": "Yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "- S1. The proposed method is well-deserved and well-motivated in the topic of traffic accident anticipation. At the beginning, this approach follows the conventional manners such as AUC, TTA, and FPS. On top of that, the proposed method additionally proposes snippet encoder and multiple time-span (from 0.1 to 2.0 seconds) anticipation. \n\n- S2. The proposed method has been compared to the conventional approaches including recent works including the reference [7,8,9]. Totally, the propsed method serves better results in terms of performance and earliness in the real-time traffic accident anticipation. \n\n- S3. Basically the paper is written properly. For example, the figures are put on the right locations and included good presentation for describing the methodology and results. \n\n- W1. The paper describes as \"Existing methods typically predict frame-level anomaly scores as risk indicators,\" in l.2-3, and \"For example, a branch of works [3, 4, 5, 6, 7] regards the risks as 1 for frames within a fixed interval (e.g. 2.0s) before the annotated accident timestamp and 0 for frames sampled from safe driving scenarios,\" in l.26-28. However, for example, it seems the paper [Kumamoto+, WACV25] assigns Transformer model and anticipate traffic accidents from multi-frame input. On the other hand, the paper [Suzuki+, CVPR18] and following papers have been considered the traffic accident anticipation with flexible frame lengths in the adaptive loss function. Therefore, the descriptions in l.2-3 & l.26-28 are slightly overclaiming according to what they do in the conventional works.\n\n[Kumamoto+, WACV25] Y. Kumamoto et al. \"AAT-DA: Accident Anticipation Transformer with Driver Attention,\" WACV 2025.\n\n[Suzuki+, CVPR18] T. Suzuki et al. \"Anticipating Traffic Accidents with Adaptive Loss and Large-scale Incident DB,\" CVPR 2018.\n\n\n- W2. Lacking some important citations. Other than the above mentioned [Kumamoto+, WACV25] and [Suzuki+, CVPR18], this paper has no citation of e.g., [Karim+, TITS22]. This relates to the paper quality. \n\n[Karim+, TITS22] M. Karim et al. \"A Dynamic Spatial-Temporal Attention Network for Early Anticipation of Traffic Accidents,\" IEEE Transactions on Intelligent Transportation Systems, 2022.\n\n- W3. Certainly, some proposed approaches are somewhat new such as the snippet encoder and multiple timestamps. However, these are not conceptually very novel in the topic of traffic accident anticipation. Although the performance has been improved by comparing to the conventional approaches, I am concerning its coming from the novel detection methods and architectural changes.", "was_revised": true, "final_justification": "The reviewer thanks the authors for a helpful information and great efforts. After the rebuttal phase, the reviewer has a better understanding on the proposed method. This has a contribution to the topic of traffic accident anticipation. Hopefully the proposed method can be widely shared in the research community and employed diverse traffic applications.", "rebuttal": "Dear Reviewer o9V3,\n\nThank you for your detailed review and valuable feedback on our submission \"Accident Anticipation via Temporal Occurrence Prediction\". We appreciate your recognition of our method's strong motivation, its effective comparison with conventional approaches, and the overall clarity of our paper's presentation.\n\nWe have carefully considered your comments, especially regarding the originality claims, missing citations, and the need for clearer ablation studies, and we're committed to addressing them to improve our manuscript.\n\n## **Response to Weaknesses and Questions:**\n**W1. Clarity on Our Claims:**\nWe sincerely apologize for any ambiguity or confusion caused by our claims. We have provided clarifications for each of our claims and guarantee that these will be revised in the accepted version of the paper to prevent any future misunderstanding for readers.\n- **\"Existing methods typically predict frame-level anomaly scores as risk indicators,\" in l.2-3:** \nWe would like to clarify that our contribution **does not lie in the use of multi-frame input, but in predicting multiple timestamp risk sequence**. We fully acknowledge that many existing methods, including [Kumamoto+, WACV’25], utilize multi-frame inputs and powerful temporal encoders such as Transformers to extract temporal features. However, our claim means that prior works predict **a single anomaly score** for each frame, while our method predicts **a future sequence of anomaly scores** for each frame, such as the likelihood of an accident occurring at +0.1s, +0.2s, ..., up to +2.0s. This allows our model to explicitly capture the temporal evolution of accident risk over a future horizon.\n\n- **\"For example, a branch of works [3, 4, 5, 6, 7] regards the risks as 1 for frames within a fixed interval (e.g. 2.0s) before the annotated accident timestamp and 0 for frames sampled from safe driving scenarios,\" in l.26-28:** \nWe would like to clarify that while the paper [Suzuki+, CVPR18] and following papers do introduce adaptive loss functions—typically by applying penalty weights that decay with temporal distance from the accident frame—they still assign a binary label of 1 to all frames within a fixed interval (e.g., 2.0s before the accident). In other words, although the adaptive loss functions reduce the penalty for frames farther from the accident, the model is still optimized toward **predicting a value close to 1 for all positive frames**, which may not reflect the actual uncertainty or gradual buildup of risk over time. In contrast, we argue that only the accident timestamp itself can be precisely labeled, while risk at earlier frames is uncertain and should be predicted in a temporally resolved and probabilistic manner, rather than assigned a flat label. Our method addresses this by **predicting a distribution of future accident probabilities**, which better captures the evolving nature of risk.\n\n**W2. Lacking Important Citations:**\nThank you for bringing these important missing citations to our attention. We agree that including relevant prior work is crucial for paper quality and proper contextualization. We will revise our manuscript to include comprehensive discussions of:\n    * **[Kumamoto+, WACV25]**: We will discuss their use of Transformer models and driver attention for accident anticipation,\n    * **[Suzuki+, CVPR18]**: We will include their work on adaptive loss functions and large-scale incident databases.\n    * **[Karim+, TITS22]**: We will discuss their Dynamic Spatial-Temporal Attention Network, acknowledging its contribution to early anticipation and its approach to spatio-temporal modeling.\n    * **Other works**: We will investigate the latest work and incorporate it into our related work for discussion.\n\n**W3. Novelty and the Causes of Performance Improvement:**\n- **Novelty:**  The key novelty of our work lies in the novel paradigm of predicting the temporal occurrence of an accident. Specifically, we move from predicting a single risk score for each frame to predicting a temporal sequence of future accident probabilities for each frame. This formulation allows the model to better capture the evolving nature of risk in dynamic driving scenes.\n- **Causes of Performance Improvement:** \nOur method does not contain object detection modules, nor does it depend on complex architectural modifications. Since two of our contributions are the novel paradigm of predicting the temporal occurrence of an accident and the snippet encoder, we have highlighted their effectiveness in our ablation studies (see Sec. 5.3). \n\nAs shown in Table 4, our main contribution, \"predicting the temporal occurrence\" (TOP), brings performance improvement to mAUC$^{0.1}$ (0.0201) and mTTA$^{0.1}$ (0.0510), especially to AUC$_{0.0s}^{0.1}$ (0.1715). \n\nOur second contribution, \"snippet encoder\" (SE), also brings performance improvement to mAUC$^{0.1}$ (0.1378) and mTTA$^{0.1}$ (0.3006), especially to AUC$_{0.0s}^{0.1}$ (0.2090), while experiments I, II, V, VI utilized RNNs to aggregate temporal features. \n\nBased on the above, these ablation experiments demonstrate that our proposed novel contributions are effective and can lead to performance improvements.\n\nTable 4: Ablation study on the CAP-DATA [8] dataset, where TOP denotes the temporal occurrence prediction, SE denotes the snippet encoder, and AAA denotes the anomaly appearance annotations.\n\n| Experiment     | TOP | SE | AAA | AUC$_{0.0s}^{0.1} \\uparrow$ | AUC$_{0.5s}^{0.1} \\uparrow$ | AUC$_{1.0s}^{0.1} \\uparrow$ | AUC$_{1.5s}^{0.1} \\uparrow$ | mAUC$^{0.1} \\uparrow$ | mTTA$^{0.1}$ (s) $\\uparrow$ |\n|-|-|-|-|-|-|-|-|-|-|\n| I (Baseline)   | ✕   | ✕  | ✕   | 0.4357                    | 0.3951                    | 0.2770                    | 0.1777                    | 0.2829                | 0.5389                      |\n| II             | ✕   | ✕  | ✓   | 0.5151                    | 0.3951                    | 0.2353                    | 0.1491                    | 0.2598                | 0.5551                      |\n| III            | ✕   | ✓  | ✕   | 0.6027                    | 0.5550                    | 0.3607                    | 0.1931                    | 0.3696                | 0.7330                      |\n| IV             | ✕   | ✓  | ✓   | 0.7251                    | 0.6280                    | 0.3671                    | 0.1949                    | 0.3967                | 0.8046                      |\n| V              | ✓   | ✕  | ✕   | 0.5700                    | 0.3432                    | 0.2284                    | 0.1721                    | 0.2479                | 0.4595                      |\n| VI             | ✓   | ✕  | ✓   | 0.6827                    | 0.4395                    | 0.2510                    | 0.1584                    | 0.2830                | 0.5790                      |\n| VII            | ✓   | ✓  | ✕   | 0.8381                    | 0.6747                    | **0.3982** | **0.2141** | 0.4290                | 0.8644                      |\n| **VIII (Ours)**    | ✓   | ✓  | ✓   | **0.8739** | **0.6979** | 0.3892                    | 0.2014                    | **0.4295** | **0.9327** |\n\n**Questions: Clarifying Performance Improvement Causes:**\nSince we have clarified that our contributions could cause performance improvement in the above \"W3\" with ablation studies in Table 4, we will clarify the difference in implementation details between the conventional methods and ours in this response.\n\nWe list all the components used in previous works (including text input, driver attention, object detector, spatial encoder, GCNs, RNNs) in the following Table. Compared with previous works using a spatial encoder and RNNs to understand spatial and temporal features, we capture spatial and temporal features simultaneously by a snippet encoder, SlowOnly (a very vanilla backbone in video recognition). Additionally, we did not use any other structures (like object detectors and GCNs) or auxiliary information (like text input and driver attention) to ensure simplicity of implementation. Despite this simplicity, we also achieve SOTA performance (see Rebuttal 2. for Reviewer frVz) within fair comparison and Kaggle competition. This demonstrates that the improvement is due to the proposed method itself rather than novel detection methods and architectural changes. \n\n|Method|Text Input|Driver Attention|Object Detector|Spatial Encoder|GCNs|RNNs|TOP|mAUC$^{0.1}$ ↑|mTTA$^{0.1}$ (s) ↑|\n|:-|:-|:-|:-|:-|:-|:-|:-|:-:|:-:|\n|DRIVE [Bao+, ICCV21]|✕|✓|✕|VGG-16-based MLNet|✕|✕|✕|0.1159|0.3954|\n|CAP [Fang+, ITSM24]|✓|✓|✕|CNN|✓|GRU|✕|0.0357|0.6372|\n|DSTA [Karim+, TITS22]|✕|✕|Cascade R-CNN|ResNet-101|DSN|GRU|✕|0.2864|0.8039|\n|GSC [Wang+, TIV23]|✕|✕|Faster R-CNN|ResNet-101|✓|LSTM|✕|0.3046|0.8165|\n|Baseline|✕|✕|✕|Resnet-50|✕|LSTM|✕|0.2829|0.5389|\t\n|Ours|✕|✕|✕|Snippet Encoder (SlowOnly)|✕|✕|✓|0.4295|0.9327|\n\n[Bao+, ICCV21] W. Bao et al., “Drive: Deep reinforced accident anticipation with visual explanation”, in ICCV, 2021.\n\n[Fang+, ITSM24] J. Fang et al., “Cognitive accident prediction in driving scenes: A multimodality benchmark”, in ITSM, 2024.\n\n[Karim+, TITS22] M. M. Karim et al., “A dynamic spatial-temporal attention network for early anticipation of traffic accidents”, in TITS, 2022.\n\n[Wang+, TIV23] T. Wang et al., “GSC: A graph and spatio-temporal continuity based framework for accident anticipation”, in TIV, 2023."}]

Author rebuttal:

Meta-review: This work presents an accident anticipation framework that predicts the probability of future accidents at multiple timestamps, instead of assigning frame-level anomaly scores. It integrates a snippet encoder and a Transformer-based temporal decoder to model spatio-temporal dynamics for online prediction. A practical evaluation protocol emphasizes low false alarms while measuring recall and time-to-accident (TTA). Experiments demonstrate strong performance in both recall and TTA, validating the framework’s real-world effectiveness.

The authors’ rebuttal adequately addressed all major concerns raised in the original reviews. Additional experiments were also conducted in response to reviewers’ follow-up questions. These efforts have further strengthened the quality of the paper. Consequently, multiple reviewers raised their scores, leading to a consensus decision to accept the paper. The authors are encouraged to incorporate the new experimental results and discussions into the final version of the paper.
Decision: Accept (poster)