NEST: A Neuromodulated Small-world Hypergraph Trajectory Prediction Model for Autonomous Driving

• URL: http://arxiv.org/abs/2412.11682v1
• Date: Mon, 16 Dec 2024 11:49:12 GMT
• Title: NEST: A Neuromodulated Small-world Hypergraph Trajectory Prediction Model for Autonomous Driving
• Authors: Chengyue Wang, Haicheng Liao, Bonan Wang, Yanchen Guan, Bin Rao, Ziyuan Pu, Zhiyong Cui, Chengzhong Xu, Zhenning Li,
• Abstract summary: NEST (Neuromodulated Small-world Hypergraph Trajectory Prediction) is a novel framework that integrates Small-world Networks and hypergraphs for superior interaction modeling and prediction accuracy. We validate the NEST model on several real-world datasets, including nuScenes, MoCAD, and HighD.
• Score: 15.17856086804651
• License:
• Abstract: Accurate trajectory prediction is essential for the safety and efficiency of autonomous driving. Traditional models often struggle with real-time processing, capturing non-linearity and uncertainty in traffic environments, efficiency in dense traffic, and modeling temporal dynamics of interactions. We introduce NEST (Neuromodulated Small-world Hypergraph Trajectory Prediction), a novel framework that integrates Small-world Networks and hypergraphs for superior interaction modeling and prediction accuracy. This integration enables the capture of both local and extended vehicle interactions, while the Neuromodulator component adapts dynamically to changing traffic conditions. We validate the NEST model on several real-world datasets, including nuScenes, MoCAD, and HighD. The results consistently demonstrate that NEST outperforms existing methods in various traffic scenarios, showcasing its exceptional generalization capability, efficiency, and temporal foresight. Our comprehensive evaluation illustrates that NEST significantly improves the reliability and operational efficiency of autonomous driving systems, making it a robust solution for trajectory prediction in complex traffic environments.

Related papers

• Characterized Diffusion Networks for Enhanced Autonomous Driving Trajectory Prediction [0.6202955567445396]
  We present a novel trajectory prediction model for autonomous driving. Our model enhances the accuracy and reliability of trajectory predictions by incorporating uncertainty estimation and complex agent interactions. The proposed model showcases strong potential for application in real-world autonomous driving systems.
  arXiv  Detail & Related papers  (2024-11-25T15:03:44Z)
• FollowGen: A Scaled Noise Conditional Diffusion Model for Car-Following Trajectory Prediction [9.2729178775419]
  This study introduces a scaled noise conditional diffusion model for car-following trajectory prediction. It integrates detailed inter-vehicular interactions and car-following dynamics into a generative framework, improving the accuracy and plausibility of predicted trajectories. Experimental results on diverse real-world driving scenarios demonstrate the state-of-the-art performance and robustness of the proposed method.
  arXiv  Detail & Related papers  (2024-11-23T23:13:45Z)
• Hypergraph-based Motion Generation with Multi-modal Interaction Relational Reasoning [13.294396870431399]
  Real-world driving environments are characterized by dynamic and diverse interactions among vehicles. This research introduces an integrated framework for autonomous vehicles (AVs) motion prediction.
  arXiv  Detail & Related papers  (2024-09-18T03:30:38Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• RACER: Rational Artificial Intelligence Car-following-model Enhanced by Reality [51.244807332133696]
  This paper introduces RACER, a cutting-edge deep learning car-following model to predict Adaptive Cruise Control (ACC) driving behavior. Unlike conventional models, RACER effectively integrates Rational Driving Constraints (RDCs), crucial tenets of actual driving. RACER excels across key metrics, such as acceleration, velocity, and spacing, registering zero violations.
  arXiv  Detail & Related papers  (2023-12-12T06:21:30Z)
• Attention-based Spatial-Temporal Graph Neural ODE for Traffic Prediction [3.4806267677524896]
  We propose attention-based graph neural ODE (AST) that explicitly learns the dynamics of the traffic system. Our model aggregates traffic patterns of different periods and has satisfactory performance on two real-world traffic data sets.
  arXiv  Detail & Related papers  (2023-05-01T00:58:48Z)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)
• Gradient-Based Trajectory Optimization With Learned Dynamics [80.41791191022139]
  We use machine learning techniques to learn a differentiable dynamics model of the system from data. We show that a neural network can model highly nonlinear behaviors accurately for large time horizons. In our hardware experiments, we demonstrate that our learned model can represent complex dynamics for both the Spot and Radio-controlled (RC) car.
  arXiv  Detail & Related papers  (2022-04-09T22:07:34Z)
• Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction [75.1213178617367]
  We propose a hybrid approach combining deep learning and physical motion models. We achieve interpretability by restricting the output range of the deep neural network as part of the hybrid model. The results show that our hybrid model can improve model interpretability with no decrease in accuracy compared to existing deep learning approaches.
  arXiv  Detail & Related papers  (2021-03-11T15:21:08Z)
• Spatio-Temporal Look-Ahead Trajectory Prediction using Memory Neural Network [6.065344547161387]
  This paper attempts to solve the problem of Spatio-temporal look-ahead trajectory prediction using a novel recurrent neural network called the Memory Neuron Network. The proposed model is computationally less intensive and has a simple architecture as compared to other deep learning models that utilize LSTMs and GRUs.
  arXiv  Detail & Related papers  (2021-02-24T05:02:19Z)
• Spatial-Temporal Transformer Networks for Traffic Flow Forecasting [74.76852538940746]
  We propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) to improve the accuracy of long-term traffic forecasting. Specifically, we present a new variant of graph neural networks, named spatial transformer, by dynamically modeling directed spatial dependencies. The proposed model enables fast and scalable training over a long range spatial-temporal dependencies.
  arXiv  Detail & Related papers  (2020-01-09T10:21:04Z)

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.