End-to-End Pedestrian Trajectory Forecasting with Transformer Network

Chai

IET Cyber-Syst and Robotics

et al. 2022

Recurrent Neural Network, Long Short-Term Memory, and Transformer have made great progress in predicting the trajectories of moving objects. Although the trajectory element with the surrounding scene features has been merged to improve performance, there still exist some problems to be solved. One is that the time series processing models will increase the inference time with the increase of the number of prediction sequences. Another problem is that the features cannot be extracted from the scene's image and point cloud in some situations. Therefore, an Obstacle-Transformer is proposed to predict trajectory in a constant inference time. An 'obstacle' is designed by the surrounding trajectory rather than images or point clouds, making Obstacle-Transformer more applicable in a wider range of scenarios. Experiments are conducted on ETH and UCY datasets to verify the performance of our model.

Section: Related Workmentioning

confidence: 99%

Section: Model Without Scene Featuresmentioning

confidence: 99%

Obstacle‐transformer: A trajectory prediction network based on surrounding trajectories

Chai

IET Cyber-Syst and Robotics

et al. 2022

“…Achaji et al [47] introduced PReTR, which utilized a decomposed spatio-temporal attention module to extract features from multi-agent scenarios. Yao et al [48] proposed an end-to-end transformer network that has the self-correcting scheme to enhance the model robustness. The above methods make use of the powerful feature extraction ability of Transformer and perform good in trajectory prediction.…”

Section: Related Workmentioning

confidence: 99%

Multi-granularity scenarios understanding network for trajectory prediction

Yang

et al. 2022

Complex Intell. Syst.

Understanding agents’ motion behaviors under complex scenes is crucial for intelligent autonomous moving systems (like delivery robots and self-driving cars). It is challenging duo to the inherent uncertain of future trajectories and the large variation in the scene layout. However, most recent approaches ignored or underutilized the scenario information. In this work, a Multi-Granularity Scenarios Understanding framework, MGSU, is proposed to explore the scene layout from different granularity. MGSU can be divided into three modules: (1) A coarse-grained fusion module uses the cross-attention to fuse the observed trajectory with the semantic information of the scene. (2) The inverse reinforcement learning module generates optimal path strategy through grid-based policy sampling and outputs multiple scene paths. (3) The fine-grained fusion module integrates the observed trajectory with the scene paths to generate multiple future trajectories. To fully explore the scene information and improve the efficiency, we present a novel scene-fusion Transformer, whose encoder is used to extract scene features and the decoder is used to fuse scene and trajectory features to generate future trajectories. Compared with the current state-of-the-art methods, our method decreases the ADE errors by 4.3% and 3.3% by gradually integrating different granularity of scene information on SDD and NuScenes, respectively. The visualized trajectories demonstrate that our method can accurately predict future trajectories after fusing scene information.

“…The transformer model proposed by Vaswani et al [20] in 2017 outperformed the recurrent neural network (RNN) model in terms of capturing the long-term dependence. Yao et al [21] proposed an end-to-end transformer network with embedded random deviation queries for pedestrian trajectory prediction and achieved better performance.…”

Section: Introductionmentioning

confidence: 99%

Predicting Multidimensional Environmental Factor Trends in Greenhouse Microclimates Using a Hybrid Ensemble Approach

Ren

2023

Journal of Sensors

Trend prediction of greenhouse microclimate is crucial, as greenhouse crops are vulnerable to potential losses resulting from dramatic changes in greenhouse microclimate. Consequently, a precise greenhouse microclimate predictive model is required that can predict trends in greenhouse microclimates several weeks in advance to avoid financial losses. In the present study, we proposed a hybrid ensemble approach to predict greenhouse microclimate based on an Informer model that is optimized using improved empirical mode decomposition (IEMD). The dataset was decomposed using IEMD, and then all the decomposed datasets were predicted using the Informer model. Afterward, the predictions were combined. In the present study, five different environmental factor datasets of CO2 concentration, atmospheric pressure, light intensity, temperature, and humidity were predicted. The performance of the IEMD-Informer model was compared with other modeling approaches. The results demonstrate that the proposed method has outstanding performance and can predict the greenhouse microclimate environmental factors more accurately.