Resonance: Learning to Predict Social-Aware Pedestrian Trajectories as Co-Vibrations

• URL: http://arxiv.org/abs/2412.02447v2
• Date: Mon, 10 Mar 2025 01:37:08 GMT
• Title: Resonance: Learning to Predict Social-Aware Pedestrian Trajectories as Co-Vibrations
• Authors: Conghao Wong, Ziqian Zou, Beihao Xia, Xinge You,
• Abstract summary: We propose the Resonance model to encode and forecast pedestrian trajectories in the form of co-vibrations''<n>It decomposes trajectory modifications and randomnesses into multiple vibration portions to simulate agents' reactions to each single cause.<n>It forecasts trajectories as the superposition of these independent vibrations separately.
• Score: 10.240007698680097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning to forecast trajectories of intelligent agents has caught much more attention recently. However, it remains a challenge to accurately account for agents' intentions and social behaviors when forecasting, and in particular, to simulate the unique randomness within each of those components in an explainable and decoupled way. Inspired by vibration systems and their resonance properties, we propose the Resonance (short for Re) model to encode and forecast pedestrian trajectories in the form of ``co-vibrations''. It decomposes trajectory modifications and randomnesses into multiple vibration portions to simulate agents' reactions to each single cause, and forecasts trajectories as the superposition of these independent vibrations separately. Also, benefiting from such vibrations and their spectral properties, representations of social interactions can be learned by emulating the resonance phenomena, further enhancing its explainability. Experiments on multiple datasets have verified its usefulness both quantitatively and qualitatively.

Related papers

• Notes on Quantum Soundscapes and Music [0.0]
  "Listening" to data via their sonifications facilitates the discovery of signals. "Listening" to data via their resulting sonifications provides a complementary way to discern when the data violates macroscopic realism.
  arXiv  Detail & Related papers  (2025-04-06T21:13:59Z)
• Spatial Reasoning with Denoising Models [49.83744014336816]
  We introduce a framework to perform reasoning over sets of continuous variables via denoising generative models. We demonstrate for the first time, that order of generation can successfully be predicted by the denoising network itself.
  arXiv  Detail & Related papers  (2025-02-28T14:08:30Z)
• Self-Distilled Disentangled Learning for Counterfactual Prediction [49.84163147971955]
  We propose the Self-Distilled Disentanglement framework, known as $SD2$. Grounded in information theory, it ensures theoretically sound independent disentangled representations without intricate mutual information estimator designs. Our experiments, conducted on both synthetic and real-world datasets, confirm the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-06-09T16:58:19Z)
• Can Transformers Predict Vibrations? [0.5076419064097734]
  Electric vehicles (EVs) experience vibrations when driving on rough terrains, known as torsional resonance. Current damping technologies only detect resonance after the vibration amplitude of the drive shaft torque reaches a certain threshold. We introduce Resoformer, a transformer-based model for predicting torsional resonance.
  arXiv  Detail & Related papers  (2024-02-16T08:56:22Z)
• Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation [50.01551945190676]
  Social robot navigation can be helpful in various contexts of daily life but requires safe human-robot interactions and efficient trajectory planning. We propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures. We demonstrate its effectiveness for multi-agent trajectory prediction and social robot navigation.
  arXiv  Detail & Related papers  (2024-01-22T18:58:22Z)
• Quantum ballet by gravitational waves: Generating entanglement's dance of revival-collapse and memory within the quantum system [0.3958317527488534]
  Using LIGO's arms as oscillators interacting with gravitational waves (GWs), our study demonstrates the potential for generating quantum entanglement. Our findings reveal unique entanglement dynamics, including periodic "collapse and revival" influenced by GW oscillations, alongside a distinct "quantum memory effect" We believe that these forecasts may hold significance towards both theoretically probing and experimentally verifying various properties of classical gravitational waves.
  arXiv  Detail & Related papers  (2024-01-05T12:32:47Z)
• SocialCircle: Learning the Angle-based Social Interaction Representation for Pedestrian Trajectory Prediction [19.075215661355486]
  We build a new anglebased trainable social interaction representation, named SocialCircle, for continuously reflecting the context of social interactions. We validate the effect of the proposed SocialCircle by training it along with several newly released trajectory prediction models. Experiments show that the SocialCircle not only quantitatively improves the prediction performance, but also qualitatively helps better simulate social interactions.
  arXiv  Detail & Related papers  (2023-10-09T02:59:21Z)
• Emergence of a stochastic resonance in machine learning [0.0]
  We find that injecting noise to the training data can induce a resonance with significant benefits to both short-term prediction of the state variables and long-term prediction of the attractor of the system.
  arXiv  Detail & Related papers  (2022-11-15T18:15:43Z)
• Gaussian theory for estimating fluctuating perturbations with back action evasive oscillator variables [0.0]
  We apply a Gaussian state formalism to track fluctuating perturbations that act on the position and momentum quadrature variables of a harmonic oscillator. Our real-time analysis of the continuous monitoring of the system employs a hybrid quantum-classical description of the quantum probe and the unknown classical perturbations.
  arXiv  Detail & Related papers  (2022-02-27T08:57:18Z)
• Social-DualCVAE: Multimodal Trajectory Forecasting Based on Social Interactions Pattern Aware and Dual Conditional Variational Auto-Encoder [14.05141917351931]
  We present a conditional variational auto-encoder (Social-DualCVAE) for multi-modal trajectory forecasting. It is based on a generative model conditioned not only on the past trajectories but also the unsupervised classification of interaction patterns. The proposed model is evaluated on widely used trajectory benchmarks and outperforms the prior state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-02-08T16:04:47Z)
• SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian Trajectory Prediction [59.064925464991056]
  We propose one new prediction model named Social Soft Attention Graph Convolution Network (SSAGCN) SSAGCN aims to simultaneously handle social interactions among pedestrians and scene interactions between pedestrians and environments. Experiments on public available datasets prove the effectiveness of SSAGCN and have achieved state-of-the-art results.
  arXiv  Detail & Related papers  (2021-12-05T01:49:18Z)
• A Frequency Perspective of Adversarial Robustness [72.48178241090149]
  We present a frequency-based understanding of adversarial examples, supported by theoretical and empirical findings. Our analysis shows that adversarial examples are neither in high-frequency nor in low-frequency components, but are simply dataset dependent. We propose a frequency-based explanation for the commonly observed accuracy vs. robustness trade-off.
  arXiv  Detail & Related papers  (2021-10-26T19:12:34Z)
• You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction [52.442129609979794]
  Recent deep learning approaches for trajectory prediction show promising performance. It remains unclear which features such black-box models actually learn to use for making predictions. This paper proposes a procedure that quantifies the contributions of different cues to model performance.
  arXiv  Detail & Related papers  (2021-10-11T14:24:15Z)
• Human Trajectory Prediction via Counterfactual Analysis [87.67252000158601]
  Forecasting human trajectories in complex dynamic environments plays a critical role in autonomous vehicles and intelligent robots. Most existing methods learn to predict future trajectories by behavior clues from history trajectories and interaction clues from environments. We propose a counterfactual analysis method for human trajectory prediction to investigate the causality between the predicted trajectories and input clues.
  arXiv  Detail & Related papers  (2021-07-29T17:41:34Z)
• Multiple Future Prediction Leveraging Synthetic Trajectories [25.721634055111643]
  Trajectory prediction is an important task, especially in autonomous driving. We propose a data driven approach based on Markov Chains to generate synthetic trajectories. We show that combining synthetic and real data leads to prediction improvements.
  arXiv  Detail & Related papers  (2020-10-18T09:33:23Z)
• What-If Motion Prediction for Autonomous Driving [58.338520347197765]
  Viable solutions must account for both the static geometric context, such as road lanes, and dynamic social interactions arising from multiple actors. We propose a recurrent graph-based attentional approach with interpretable geometric (actor-lane) and social (actor-actor) relationships. Our model can produce diverse predictions conditioned on hypothetical or "what-if" road lanes and multi-actor interactions.
  arXiv  Detail & Related papers  (2020-08-24T17:49:30Z)
• Noisy Agents: Self-supervised Exploration by Predicting Auditory Events [127.82594819117753]
  We propose a novel type of intrinsic motivation for Reinforcement Learning (RL) that encourages the agent to understand the causal effect of its actions. We train a neural network to predict the auditory events and use the prediction errors as intrinsic rewards to guide RL exploration. Experimental results on Atari games show that our new intrinsic motivation significantly outperforms several state-of-the-art baselines.
  arXiv  Detail & Related papers  (2020-07-27T17:59:08Z)
• Vector-Quantized Timbre Representation [53.828476137089325]
  This paper targets a more flexible synthesis of an individual timbre by learning an approximate decomposition of its spectral properties with a set of generative features. We introduce an auto-encoder with a discrete latent space that is disentangled from loudness in order to learn a quantized representation of a given timbre distribution. We detail results for translating audio between orchestral instruments and singing voice, as well as transfers from vocal imitations to instruments.
  arXiv  Detail & Related papers  (2020-07-13T12:35:45Z)
• Optimal Learning with Excitatory and Inhibitory synapses [91.3755431537592]
  I study the problem of storing associations between analog signals in the presence of correlations. I characterize the typical learning performance in terms of the power spectrum of random input and output processes.
  arXiv  Detail & Related papers  (2020-05-25T18:25:54Z)
• Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network [29.289670231364788]
  In this paper, we propose a generic generative neural system for multi-agent trajectory prediction. We also employ an efficient kinematic constraint layer applied to vehicle trajectory prediction. The proposed system is evaluated on three public benchmark datasets for trajectory prediction.
  arXiv  Detail & Related papers  (2020-02-14T20:11:13Z)

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.