Holistic Transformer: A Joint Neural Network for Trajectory Prediction and Decision-Making of Autonomous Vehicles

• URL: http://arxiv.org/abs/2206.08809v1
• Date: Fri, 17 Jun 2022 14:38:11 GMT
• Title: Holistic Transformer: A Joint Neural Network for Trajectory Prediction and Decision-Making of Autonomous Vehicles
• Authors: Hongyu Hu, Qi Wang, Zhengguang Zhang, Zhengyi Li, Zhenhai Gao
• Abstract summary: Trajectory prediction and behavioral decision-making are important tasks for autonomous vehicles. A joint neural network that combines multiple cues is proposed to predict trajectories and make behavioral decisions simultaneously.
• Score: 15.024503096898634
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Trajectory prediction and behavioral decision-making are two important tasks for autonomous vehicles that require good understanding of the environmental context; behavioral decisions are better made by referring to the outputs of trajectory predictions. However, most current solutions perform these two tasks separately. Therefore, a joint neural network that combines multiple cues is proposed and named as the holistic transformer to predict trajectories and make behavioral decisions simultaneously. To better explore the intrinsic relationships between cues, the network uses existing knowledge and adopts three kinds of attention mechanisms: the sparse multi-head type for reducing noise impact, feature selection sparse type for optimally using partial prior knowledge, and multi-head with sigmoid activation type for optimally using posteriori knowledge. Compared with other trajectory prediction models, the proposed model has better comprehensive performance and good interpretability. Perceptual noise robustness experiments demonstrate that the proposed model has good noise robustness. Thus, simultaneous trajectory prediction and behavioral decision-making combining multiple cues can reduce computational costs and enhance semantic relationships between scenes and agents.

Related papers

• Diffusion-Based Environment-Aware Trajectory Prediction [3.1406146587437904]
  The ability to predict the future trajectories of traffic participants is crucial for the safe and efficient operation of autonomous vehicles. In this paper, a diffusion-based generative model for multi-agent trajectory prediction is proposed. The model is capable of capturing the complex interactions between traffic participants and the environment, accurately learning the multimodal nature of the data.
  arXiv  Detail & Related papers  (2024-03-18T10:35:15Z)
• Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation [58.21683603243387]
  We propose three auxiliary tasks with relational-temporal reasoning and integrate them into the standard Deep Learning framework. These auxiliary tasks provide additional supervision signals to infer the behavior patterns other interactive agents. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics.
  arXiv  Detail & Related papers  (2023-11-27T18:57:42Z)
• Hybrid Predictive Coding: Inferring, Fast and Slow [62.997667081978825]
  We propose a hybrid predictive coding network that combines both iterative and amortized inference in a principled manner. We demonstrate that our model is inherently sensitive to its uncertainty and adaptively balances balances to obtain accurate beliefs using minimum computational expense.
  arXiv  Detail & Related papers  (2022-04-05T12:52:45Z)
• Adaptive Trajectory Prediction via Transferable GNN [74.09424229172781]
  We propose a novel Transferable Graph Neural Network (T-GNN) framework, which jointly conducts trajectory prediction as well as domain alignment in a unified framework. Specifically, a domain invariant GNN is proposed to explore the structural motion knowledge where the domain specific knowledge is reduced. An attention-based adaptive knowledge learning module is further proposed to explore fine-grained individual-level feature representation for knowledge transfer.
  arXiv  Detail & Related papers  (2022-03-09T21:08:47Z)
• TAE: A Semi-supervised Controllable Behavior-aware Trajectory Generator and Predictor [3.6955256596550137]
  Trajectory generation and prediction play important roles in planner evaluation and decision making for intelligent vehicles. We propose a behavior-aware Trajectory Autoencoder (TAE) that explicitly models drivers' behavior. Our model addresses trajectory generation and prediction in a unified architecture and benefits both tasks.
  arXiv  Detail & Related papers  (2022-03-02T17:37:44Z)
• Understanding Interlocking Dynamics of Cooperative Rationalization [90.6863969334526]
  Selective rationalization explains the prediction of complex neural networks by finding a small subset of the input that is sufficient to predict the neural model output. We reveal a major problem with such cooperative rationalization paradigm -- model interlocking. We propose a new rationalization framework, called A2R, which introduces a third component into the architecture, a predictor driven by soft attention as opposed to selection.
  arXiv  Detail & Related papers  (2021-10-26T17:39:18Z)
• Interpretable Social Anchors for Human Trajectory Forecasting in Crowds [84.20437268671733]
  We propose a neural network-based system to predict human trajectory in crowds. We learn interpretable rule-based intents, and then utilise the expressibility of neural networks to model scene-specific residual. Our architecture is tested on the interaction-centric benchmark TrajNet++.
  arXiv  Detail & Related papers  (2021-05-07T09:22:34Z)
• Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance [110.63037190641414]
  We propose to learn congestion patterns explicitly and devise a novel "Sense--Learn--Reason--Predict" framework. By decomposing the learning phases into two stages, a "student" can learn contextual cues from a "teacher" while generating collision-free trajectories. In experiments, we demonstrate that the proposed model is able to generate collision-free trajectory predictions in a synthetic dataset.
  arXiv  Detail & Related papers  (2021-03-26T02:42:33Z)
• Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Tracking [23.608125748229174]
  We propose a generic generative neural system for multi-agent trajectory prediction involving heterogeneous agents. The proposed system is evaluated on three public benchmark datasets for trajectory prediction.
  arXiv  Detail & Related papers  (2021-02-18T02:25:35Z)
• GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions [5.8010446129208155]
  An explainable neural network based on generalized additive models with structured interactions (GAMI-Net) is proposed to pursue a good balance between prediction accuracy and model interpretability. GAMI-Net is a disentangled feedforward network with multiple additiveworks. Numerical experiments on both synthetic functions and real-world datasets show that the proposed model enjoys superior interpretability.
  arXiv  Detail & Related papers  (2020-03-16T11:51:38Z)

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.