CollideNet: Hierarchical Multi-scale Video Representation Learning with Disentanglement for Time-To-Collision Forecasting
| Authors | Nishq Poorav Desai et al. |
| Year | 2026 |
| Field | Computer Vision |
| arXiv | 2604.16240 |
| Download | |
| Categories | cs.CV |
Abstract
Time-to-Collision (TTC) forecasting is a critical task in collision prevention, requiring precise temporal prediction and comprehending both local and global patterns encapsulated in a video, both spatially and temporally. To address the multi-scale nature of video, we introduce a novel spatiotemporal hierarchical transformer-based architecture called CollideNet, specifically catered for effective TTC forecasting. In the spatial stream, CollideNet aggregates information for each video frame simultaneously at multiple resolutions. In the temporal stream, along with multi-scale feature encoding, CollideNet also disentangles the non-stationarity, trend, and seasonality components. Our method achieves state-of-the-art performance in comparison to prior works on three commonly used public datasets, setting a new state-of-the-art by a considerable margin. We conduct cross-dataset evaluations to analyze the generalization capabilities of our method, and visualize the effects of disentanglement of the trend and seasonality components of the video data. We release our code at https://github.com/DeSinister/CollideNet/.
Engineering Breakdown
Plain English
CollideNet is a new deep learning system designed to predict when a collision will occur by analyzing video footage. The model uses a hierarchical transformer architecture that processes video at multiple resolutions simultaneously and decomposes temporal patterns into trend, seasonality, and non-stationary components—essentially learning what collision scenarios look like across different scales of space and time. The authors tested it on three standard public datasets and achieved state-of-the-art results compared to previous methods. This is important for autonomous vehicles and safety systems that need to predict dangerous situations with high precision before they happen.
Core Technical Contribution
The key innovation is a spatiotemporal hierarchical transformer that handles the multi-scale nature of collision prediction. Most prior work treats spatial and temporal prediction separately or uses simple convolutional architectures; CollideNet introduces a dual-stream design where the spatial stream processes each frame at multiple resolutions simultaneously, and the temporal stream explicitly decomposes video dynamics into trend (long-term direction), seasonality (repeating patterns), and non-stationarity (sudden changes). This decomposition is technically novel because it borrows time-series analysis concepts and applies them to video understanding—recognizing that collision scenarios have both predictable patterns and unpredictable disruptions. The hierarchical approach allows the model to capture both fine details (e.g., exact object positions) and coarse context (e.g., road layout and environmental structure) relevant to collision risk.
How It Works
The architecture ingests a video sequence and splits processing into two parallel streams. In the spatial stream, each frame is fed through a multi-resolution feature pyramid (similar to FPN in object detection), generating features at different scales that capture both local details and global context for that single frame. These multi-scale spatial features are aggregated using transformer attention mechanisms. In the temporal stream, the frame-level features across time are processed by transformer blocks, but crucially, this stream also includes a decomposition module that separates the temporal signal into three components: trend (slow changes in collision risk over the sequence), seasonality (cyclical patterns if any), and residual non-stationarity (abrupt changes). The decomposed components are processed separately, allowing the model to assign different attention weights and learn different patterns for each type of temporal variation. Finally, the spatial and temporal streams are fused, and the model predicts a Time-to-Collision score for the next frames, likely outputting a continuous value or distribution of frames until impact.
Production Impact
For autonomous vehicle systems and collision avoidance, this approach would replace simple physics-based TTC estimation (which assumes constant velocity) with learned models that can handle complex, real-world scenarios with occlusions, multiple moving objects, and non-linear trajectories. Integration would require video input at 20-30 fps from vehicle cameras, real-time inference latency of 50-100ms (acceptable for active braking systems), and careful validation on edge cases like rain, night driving, and novel object types. The multi-scale design likely increases computational cost compared to single-resolution baselines, so engineers would need to benchmark GPU/TPU memory usage and consider quantization or distillation for embedded deployment on automotive hardware. The temporal decomposition provides explainability: engineers can inspect whether the model is relying on trend, seasonality, or sudden changes, which aids debugging and regulatory compliance. However, this assumes sufficient labeled video data with ground-truth collision times, which may be expensive to obtain; simulation data may not transfer well to real-world conditions.
Limitations and When Not to Use This
The paper does not clearly specify how the temporal decomposition handles variable-length sequences or how it performs when collision occurs in the first few frames (where trend estimation is unreliable). The evaluation is limited to three public datasets, which may not represent rare collision modes, adverse weather, or adversarial scenarios where objects deliberately evade. The approach assumes video input is available and synchronized, which may not hold in real-time systems with camera lag, frame drops, or network delays. There is no discussion of robustness to domain shift (e.g., training on simulation but deploying on real roads) or failure mode analysis—what happens when the model confidently predicts a false collision? Follow-up work should address few-shot or zero-shot collision prediction, multi-camera fusion, and uncertainty quantification for safety-critical deployment.
Research Context
This work extends the transformer-based video understanding literature (which has rapidly evolved since ViT and Video Transformers) by adding explicit hierarchical multi-scale processing and temporal decomposition. It builds on prior TTC prediction methods that used RNNs, 3D CNNs, or optical flow, but replaces them with transformer attention, which has shown superior performance in capturing long-range dependencies. The benchmarking on standard datasets continues the evaluation tradition in autonomous driving research (datasets like KITTI, nuScenes, or domain-specific collision datasets). The temporal decomposition is inspired by classical time-series methods (e.g., STL decomposition) and represents a trend toward hybrid neural-symbolic approaches that inject inductive biases into deep learning. Future research directions likely include multi-modal fusion (camera + radar + lidar), reinforcement learning for active safety policies, and uncertainty-aware prediction for risk estimation.
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