# Vehicle Trajectory Prediction Using Residual Diffusion Model Based on Image Information
## 6 Conclusions & Discussion  
This paper introduces a trajectory prediction model based on residual denoising model, which is an advanced trajectory prediction model, which introduces base noise and residual noise to exploit the correlation between adjacent vehicles, and effectively reduces the redundancy of trajectory data and environmental information in the independent prediction process. By using the trajectory constraint architecture of traditional physical paradigm, the algorithm can effectively reduce the unreasonable trajectory in complex traffic scenes and improve the accuracy of future vehicle motion prediction. The improvement of the original decoder enhances the ability of model generation to interact dynamic information and static perception, and improves the prediction diversity and training efficiency. Through comprehensive testing of the NGSIM dataset, our model outperforms existing methods, demonstrating its accuracy, reliability, and efficiency in vehicle motion prediction.  
Limitations: Although our model shows good performance on the current dataset of highway scenarios, in today's rapid development of automotive technology, emerging technologies such as electric vehicles and vehicles equipped with advanced driver assistance systems continue to emerge, these technological advances and the introduction of new driving behavior factors may have a profound impact on the predictive accuracy of our model [39]. Therefore, we believe that adapting and optimizing models for these emerging technologies and corresponding human factors will be the focus of future work. Through continuous research and development, we plan to build a more adaptable and effective forecasting system to cope with these complex and changeable influencing factors.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **minADE1** & **minADE5** & **minFDE1** & **minFDE5** \\ \hline MID & 1.58 & 0.65 & 3.51 & 1.70 \\ MID + Residual & 1.50 & 0.60 & 3.35 & 1.60 \\ MID + Constraint & 1.54 & 0.63 & 3.41 & 1.63 \\ MID + Decoder & 1.52 & 0.62 & 3.39 & 1.63 \\ \hline Ours & 1.46 & 0.57 & 3.27 & 1.54 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation of residual denoising generation model.  
Figure 5: Visual rendering of vehicle trajectory prediction results. The historical track and ground truth track of the target vehicle are shown in orange and red, respectively, and the prediction of the model is shown in green.  
At the same time, we also realized that in order to fully improve the utility and accuracy of the model, it needs to be validated and improved in more diverse driving scenarios. At present, due to the lack of open source datasets covering special scenarios such as rural roads and mountain roads, the robustness of our model in this area needs to be enhanced. Therefore, in future studies, we will aim to collect and integrate data from these special scenarios to enhance the adaptability and robustness of the model in different environments and conditions. This will not only improve the model's performance in off-highway scenarios, but also provide strong support for the safety application of autonomous driving technology in a wider range of road conditions.  
Conceptualization, W.H.; methodology, W.H.; software, H.L.; validation, N.W.; formal analysis, T.W.; investigation, T.W.; data curation, N.W.; writing--original draft preparation, H.L.; writing--review and editing, H.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.