Enhanced Multimodal Trajectory Prediction for Autonomous Vehicles Using Advanced Diffusion Model Techniques

Vehicle trajectory prediction is crucial for ensuring the safety and reliability of autonomous driving systems. Due to the highly stochastic nature of road participants’ behaviors, it is vital that prediction models accommodate a wide range of possible scenarios to mitigate safety risks. To address this challenge, we propose a novel trajectory prediction model called DiffusionTrajPred, an innovative trajectory prediction model based on the diffusion model. This model uniquely combines forward and reverse processes, manipulating noise levels in trajectory data to forecast future paths. Through the application of a mask-based reverse process, the model can make full use of historical trajectory information and predict trajectories that combine accuracy and multiple possibilities. The model utilizes a Transformer architecture for learning the noise, which enables the model to extract richer temporal information from trajectory data, resulting in improved semantic comprehension. Furthermore, we have effectively encoded high-definition (HD) semantic map information and vehicle interaction dynamics as crucial input features, improving the model ’s predictive power. Extensive experiments on the widely recognized open-source dataset ’Argoverse’ reveal that our method outperformed the most existing state-of-the-art methods in terms of accuracy and multimodality, demonstrating the diffusion model’s unique advantage in addressing the stochastic nature of road scenarios in autonomous driving.