Human trajectory prediction using LSTM with Attention mechanism

• URL: http://arxiv.org/abs/2309.00331v1
• Date: Fri, 1 Sep 2023 08:35:24 GMT
• Title: Human trajectory prediction using LSTM with Attention mechanism
• Authors: Amin Manafi Soltan Ahmadi, Samaneh Hoseini Semnani
• Abstract summary: We use attention scores to determine which parts of the input data the model should focus on when making predictions. We show that our modified algorithm performs better than the Social LSTM in predicting the future trajectory of pedestrians in crowded spaces.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose a human trajectory prediction model that combines a Long Short-Term Memory (LSTM) network with an attention mechanism. To do that, we use attention scores to determine which parts of the input data the model should focus on when making predictions. Attention scores are calculated for each input feature, with a higher score indicating the greater significance of that feature in predicting the output. Initially, these scores are determined for the target human position, velocity, and their neighboring individual's positions and velocities. By using attention scores, our model can prioritize the most relevant information in the input data and make more accurate predictions. We extract attention scores from our attention mechanism and integrate them into the trajectory prediction module to predict human future trajectories. To achieve this, we introduce a new neural layer that processes attention scores after extracting them and concatenates them with positional information. We evaluate our approach on the publicly available ETH and UCY datasets and measure its performance using the final displacement error (FDE) and average displacement error (ADE) metrics. We show that our modified algorithm performs better than the Social LSTM in predicting the future trajectory of pedestrians in crowded spaces. Specifically, our model achieves an improvement of 6.2% in ADE and 6.3% in FDE compared to the Social LSTM results in the literature.

Related papers

• JRDB-Traj: A Dataset and Benchmark for Trajectory Forecasting in Crowds [79.00975648564483]
  Trajectory forecasting models, employed in fields such as robotics, autonomous vehicles, and navigation, face challenges in real-world scenarios. This dataset provides comprehensive data, including the locations of all agents, scene images, and point clouds, all from the robot's perspective. The objective is to predict the future positions of agents relative to the robot using raw sensory input data.
  arXiv  Detail & Related papers  (2023-11-05T18:59:31Z)
• Prediction-Oriented Bayesian Active Learning [51.426960808684655]
  Expected predictive information gain (EPIG) is an acquisition function that measures information gain in the space of predictions rather than parameters. EPIG leads to stronger predictive performance compared with BALD across a range of datasets and models.
  arXiv  Detail & Related papers  (2023-04-17T10:59:57Z)
• Context-aware multi-head self-attentional neural network model for next location prediction [19.640761373993417]
  We utilize a multi-head self-attentional (A) neural network that learns location patterns from historical location visits. We demonstrate that proposed the model outperforms other state-of-the-art prediction models. We believe that the proposed model is vital for context-aware mobility prediction.
  arXiv  Detail & Related papers  (2022-12-04T23:40:14Z)
• Conditioned Human Trajectory Prediction using Iterative Attention Blocks [70.36888514074022]
  We present a simple yet effective pedestrian trajectory prediction model aimed at pedestrians positions prediction in urban-like environments. Our model is a neural-based architecture that can run several layers of attention blocks and transformers in an iterative sequential fashion. We show that without explicit introduction of social masks, dynamical models, social pooling layers, or complicated graph-like structures, it is possible to produce on par results with SoTA models.
  arXiv  Detail & Related papers  (2022-06-29T07:49:48Z)
• LSTM-RPA: A Simple but Effective Long Sequence Prediction Algorithm for Music Popularity Prediction [19.003312140124706]
  Researchers could predict the trend of popular songs accurately by analyzing this data. Traditional trend prediction models can better predict the short trend than the long trend. We propose the improved LSTM Rolling Prediction Algorithm (LSTM-RPA)
  arXiv  Detail & Related papers  (2021-10-27T08:59:09Z)
• Imputation-Free Learning from Incomplete Observations [73.15386629370111]
  We introduce the importance of guided gradient descent (IGSGD) method to train inference from inputs containing missing values without imputation. We employ reinforcement learning (RL) to adjust the gradients used to train the models via back-propagation. Our imputation-free predictions outperform the traditional two-step imputation-based predictions using state-of-the-art imputation methods.
  arXiv  Detail & Related papers  (2021-07-05T12:44:39Z)
• Test-time Collective Prediction [73.74982509510961]
  Multiple parties in machine learning want to jointly make predictions on future test points. Agents wish to benefit from the collective expertise of the full set of agents, but may not be willing to release their data or model parameters. We explore a decentralized mechanism to make collective predictions at test time, leveraging each agent's pre-trained model.
  arXiv  Detail & Related papers  (2021-06-22T18:29:58Z)
• Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning [7.194382512848327]
  We propose a new parametrization to supervised learning on state-action data to stably predict at longer horizons. Our results in simulated and experimental robotic tasks show that our trajectory-based models yield significantly more accurate long term predictions.
  arXiv  Detail & Related papers  (2020-12-16T18:47:37Z)
• Attentional-GCNN: Adaptive Pedestrian Trajectory Prediction towards Generic Autonomous Vehicle Use Cases [10.41902340952981]
  We propose a novel Graph Convolutional Neural Network (GCNN)-based approach, Attentional-GCNN, which aggregates information of implicit interaction between pedestrians in a crowd by assigning attention weight in edges of the graph. We show our proposed method achieves an improvement over the state of art by 10% Average Displacement Error (ADE) and 12% Final Displacement Error (FDE) with fast inference speeds.
  arXiv  Detail & Related papers  (2020-11-23T03:13:26Z)
• Data-Efficient Reinforcement Learning with Self-Predictive Representations [21.223069189953037]
  We train an agent to predict its own latent state representations multiple steps into the future. On its own, this future prediction objective outperforms prior methods for sample-efficient deep RL from pixels. Our full self-supervised objective, which combines future prediction and data augmentation, achieves a median human-normalized score of 0.415 on Atari.
  arXiv  Detail & Related papers  (2020-07-12T07:38:15Z)
• Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction [57.56466850377598]
  Reasoning over visual data is a desirable capability for robotics and vision-based applications. In this paper, we present a framework on graph to uncover relationships in different objects in the scene for reasoning about pedestrian intent. Pedestrian intent, defined as the future action of crossing or not-crossing the street, is a very crucial piece of information for autonomous vehicles.
  arXiv  Detail & Related papers  (2020-02-20T18:50:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.