Improved Gaussian Mixture Probabilistic Model for Pedestrian Trajectory Prediction of Autonomous Vehicle

Abstract

Pedestrian trajectory prediction plays a crucial role in ensuring the safe and efficient operation of autonomous vehicles in urban environments. As autonomous driving technology continues to advance, accurate anticipation of pedestrians' motion trajectories has become increasingly important for informing subsequent decision-making processes. Pedestrians are dynamic and unpredictable agents, and their movements can vary greatly depending on factors, such as their intentions, interactions with other pedestrians or vehicles, and the surrounding environment. Therefore, developing effective methods to predict pedestrian trajectories is essential to enable autonomous vehicles to navigate and interact with pedestrians in a safe and socially acceptable manner. Various methods, both patented and non-patented, have been proposed, including physics-based and probability-based models, to capture the regularities in pedestrian motion and make accurate predictions. This paper proposes a pedestrian trajectory prediction method that combines a Gaussian mixture model and an artificial potential field. The study begins with an analysis of pedestrian motion patterns, allowing for the identification of distinct patterns and incorporating speed as an influential factor in pedestrian interactions. Next, a Gaussian mixture model is utilized to model and train the trajectories of pedestrians within each motion pattern cluster, effectively capturing their statistical characteristics. The trained model is then used with a regression algorithm to predict future pedestrian trajectories based on their past positions. To enhance the accuracy and safety of the predicted trajectories, an artificial potential field analysis is employed, considering factors such as collision avoidance and interactions with other entities. By combining the Gaussian mixture model and artificial potential field, this method provides an innovative and patentable approach to pedestrian trajectory prediction. Experimental results on the ETH and UCY datasets demonstrate that the proposed method combining the Gaussian mixture model and artificial potential field outperforms traditional Linear and social force models in terms of prediction accuracy. The method effectively improves accuracy while ensuring collision avoidance. The proposed method combining a Gaussian mixture model and an artificial potential field enhances pedestrian trajectory prediction. It successfully captures the differences between pedestrians and incorporates speed, improving prediction accuracy.