# Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey
## 4 CPS Application Domains
### Transportation-Based CPS
#### 4.1.4 Autonomous Vehicle Collision Prediction  
Autonomous vehicle collision prediction entails forecasting the likelihood of a collision between an autonomous vehicle and another object, such as a pedestrian or vehicle. Related to such an effort, [PERSON] et al. [70] proposed a spatial-temporal scene-graph embedding technique (SG2 VEC), which adopts GNNs and LSTM layers to predict future autonomous vehicle accidents with the assistance of visual scene perception. Likewise, [PERSON] et al. [71] adopted GNN and RNN to propose a global scheme called _GST-GAT_ for traffic prediction. The framework leveraged _\"global interaction + node query\"_ as a coherent way of information flow between nodes, which captures the interaction between traffic road networks that is spatial-temporal. These efforts can be mapped to the cube \(<\)\(X_{1},Y_{5}\), \(Z_{3}\)\(>\) area in Figure 3 and Table 1.  
Table 2 houses the identified research gaps and the contributions made by the reviewed efforts in the transportation domain.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref, Year** & **Framework** & **Gap** & **Contribution** \\ \hline
[23], 2021 & ConvLSTM & Traffic jam forecasting models were location specific & Created a model that covers the entire smart city \\ \hline
[37], 2020 & CNN-LSTM & Traffic forecasting models were vehicle-type based & Proposed a generalized model applicable to all smart vehicles \\ \hline
[55], 2020 & GATCN & Inaccurate prediction of spatial-temporal features from traffic data & An improved prediction of the hidden spatial-temporal features that lies in the data \\ \hline
[57], 2021 & ChebNet-LSTM & Inadequate accuracy, deficient adaptability and inferior real-time & Proposed an enhanced real-time, adaptable prediction scheme \\ \hline
[58], 2021 & DGNN & Previous models were built based on static adjacency matrix & Proposed a dynamic graph construction method \\ \hline
[59], 2021 & STAWnet & Previous models were built based on the static dependency within the predefined structure & Designed a self attentive model that requires no prior knowledge of the graph topology \\ \hline
[61], 2021 & GCGRNN & CNN models are inefficient in handling structure-varying data & Proposed a graph convolution approach that is independent of a predefined adjacency matrix \\ \hline
[62], 2021 & GCN & Previous models were single airport specific & Proposed a flight delay prediction of airport networks framework \\ \hline
[63], 2021 & GCN-LSTM & Previous models were static graphs based & Addressed data defects problems caused by the static graphs \\
[64], 2021 & GNNs & Current methods were not security driven & Designed a taxi driving fraud detection system \\ \hline
[71], 2021 & GST-GAT & Previous methods neglects the non-Euclidean nature of the road network & an improved dynamic spatial-temporal correlation method that captures the relevant characteristics of the traffic network \\ \hline
[65], 2022 & DSTA & Conventional methods neglects the spatial-temporal features that exist in the data & Proposed Dashcams video data to predict/detect accidents \\ \hline
[66], 2022 & CRFAST-GCN & Traditional forecasting methods neglects the semantic similarity between traffic nodes that degrades accuracy & Designed a model that extracts long and short-term dependencies, semantic similarity, and periodicity \\ \hline
[67], 2022 & CNN-DT & Current accidents prediction methods do not operate in real-time & Presented digital-twins AI-enabled autonomous cars prediction model \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the Reviewed Contributions in Transportation (\(X_{1}\)) CPS.