# Vehicle Trajectory Prediction Using Residual Diffusion Model Based on Image Information
## 1 Introduction
Therefore, it is promising to introduce the diffusion model method into the field of trajectory prediction and improve it. In the existing trajectory prediction methods, there are still two major challenges to be solved. Firstly, drivers can autonomously change their travel routes according to the changes of the surrounding environment and nearby vehicles, which makes their choice of future roads highly uncertain. Therefore, the outcome of the prediction requires a method to balance the probability and certainty [18] of future trajectories, and most importantly to be able to accurately predict multiple potential future trajectory distributions [19]. Secondly, to achieve efficient trajectory prediction, the motion trajectories of multiple vehicles in the video sequence must be comprehensively considered. The prediction results of multiple vehicles are often interdependent, which means that predicting the trajectory of each vehicle separately often leads to unrealistic and contradictory results. In diffusion models, different levels of Gaussian noise are first added to the actual vehicle trajectory, and then it is processed by reverse denoising to obtain the future trajectory. The noise elimination process of the algorithm is to gradually remove the uncertainty in each region until the desired motion trajectory is generated.Since the method is based on Markov chain to guide the generation of the final trajectory, we can control the uncertainty of the future path by adjusting the step size of the link in the prediction process [20], so as to achieve the diversity and certainty of the prediction results. The traditional diffusion model needs a lot of denoising processing to obtain high-quality motion trajectories. Therefore, in complex background, in order to ensure the accuracy of the generated trajectory, it needs to spend a lot of time and computing power resources. The existing motion trajectory generation methods based on DPM generally follow the traditional generation and denoising process, that is, predict each vehicle separately, without considering the internal relationship and mutual influence between multiple vehicles. Therefore, the prediction results obtained by current methods are often contradictory when denoising. In view of the above problems, this paper designs a new prediction model based on residual denoising diffusion mode [21], called \"residual diffusion model\". The proposed method aims to solve the problem of joint distribution of future trajectories among vehicle platoons.The framework makes full use of the feature similarity between vehicles in the same scene, and reduces the information redundancy of the traditional denoising step by dividing the base noise and the residual noise. To complement the proposed joint prediction model for residual denoising,we improve the residual denoising model by combining the traditional motion trajectory modeling idea. On this basis, a series of constraint functions are proposed to improve the computational efficiency of the model [22]. Specifically, this project proposes a new method that imposes a certain physical distance limit on a group of vehicles at future time points and applies it to the trajectory generator, and partially executes the specified goal constraint behavior in the vehicle queue. In summary, the main contributions of this work are:  
* Drawing on previous research, our study introduces the residual diffusion model, a new trajectory prediction framework that innovatively combines the residual module with the diffusion model. The proposed method solves the problem of low accuracy of multi-vehicle joint inference and cumbersome denoising steps by connecting vehicle context and surrounding vehicles. * To increase the speed of reasoning and reasonable distribution of the diversity of the generated trajectory, we introduce the optimized traditional physical constraint tracking prediction method, and as the constraint of our tracking prediction.