Pedestrian Trajectory Prediction Technology Based On Deep Learning

The trajectory prediction method based on crowd behavior has significant implications for the fields of autonomous driving and robotics.The accuracy of autonomous navigation largely depends on precise positioning of people and vehicles in the scene,massive data collection,and uninterrupted communication with other vehicles and surrounding intelligent infrastructure.Therefore,predicting the future trajectory data of pedestrians around the car can improve the safety of the autonomous driving system.Currently,Tesla has implemented autonomous driving technology on its electric vehicles,achieving a collision prediction accuracy of 76% and a collision prevention rate of over90%.Companies such as Google,Tesla,General Motors,Uber,and others are looking to the future of autonomous driving.In approximately 15-20 years,upgrading infrastructure such as automated highway systems,robot vehicle cruise management systems,real-time video processing,and nearly zero-latency 6G wireless communication systems will help achieve higher-level autonomous driving systems for cars.Predicting human trajectories is a complex task,as each pedestrian possesses multiple sensory systems such as vision,audition,and taste,and has independent logical thinking abilities.The diversity of human behavior and the complexity of internal and external influences pose challenges to accurately predicting human trajectories.Human behavior may be driven by multiple factors,such as self-intent,surrounding static environment,and social relationships among pedestrians,most of which cannot be directly observed and need to be inferred from complex clues.Therefore,to achieve real-time prediction in practical scenarios,this task faces various challenges.In general,pedestrian trajectory prediction can be viewed as a sequence generation problem based on observations of past trajectories.Early work modeled human trajectories in dynamic scenes using physics-based approaches.However,each person is a highly stochastic individual with a high degree of behavioral variability,and some key parameters are highly dependent on prior knowledge,making them unable to handle complex and crowded scenes.In recent years,Recurrent Neural Networks(RNNs)have shown strong capabilities in sequence problems,but architectures based solely on RNNs cannot handle the interaction between individuals.Recent work has mostly proposed new ideas for modeling the connectivity between people.To address the challenge of obtaining spatial features that represent the inter-person relationships for pedestrian trajectory prediction,we propose a graph convolutional network-based method for spatial feature construction.This method uses Euclidean distance to construct a graph that can represent the relationships among people,and learns and records the spatial features of individuals using graph convolutional networks.Experimental results on the public ETH and UCY datasets demonstrate that our method can effectively improve the FDE metric with a gain of 0.5%,providing more connected and interactive features for future pedestrian trajectory prediction tasks.This approach provides a more reliable foundation for future work on pedestrian trajectory prediction.In order to address the problem of crowd trajectory prediction,this paper proposes a spatial feature interaction model based on attention mechanism.The model utilizes Euclidean distance as an additional weighting factor,which more reasonably reflects the level of attention between pedestrians,and combines the crowd interaction features provided by graph convolutional networks with temporal features.Experimental results show that in most scenarios,the performance of the proposed model is superior to existing methods.Compared to the existing interaction model STAR,the proposed model achieves an average improvement of 3.8% in ADE evaluation metric and 7.5% in FDE evaluation metric.The experiments demonstrate that the proposed method can contribute to the fields of robotics and autonomous driving,by combining scene data provided by cameras and sensors to form safer and more reliable intelligent systems.