Grounded Relational Inference: Domain Knowledge Driven Explainable Autonomous Driving

• URL: http://arxiv.org/abs/2102.11905v3
• Date: Sat, 6 Jul 2024 19:40:13 GMT
• Title: Grounded Relational Inference: Domain Knowledge Driven Explainable Autonomous Driving
• Authors: Chen Tang, Nishan Srishankar, Sujitha Martin, Masayoshi Tomizuka,
• Abstract summary: We aim to develop an explainable model that generates explanations consistent with both human domain knowledge and the model's inherent causal relation. In particular, we focus on an essential building block of autonomous driving, multi-agent interaction modeling. We demonstrate that it can model interactive traffic scenarios under both simulation and real-world settings.
• Score: 47.22329993674051
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Explainability is essential for autonomous vehicles and other robotics systems interacting with humans and other objects during operation. Humans need to understand and anticipate the actions taken by the machines for trustful and safe cooperation. In this work, we aim to develop an explainable model that generates explanations consistent with both human domain knowledge and the model's inherent causal relation. In particular, we focus on an essential building block of autonomous driving, multi-agent interaction modeling. We propose Grounded Relational Inference (GRI). It models an interactive system's underlying dynamics by inferring an interaction graph representing the agents' relations. We ensure a semantically meaningful interaction graph by grounding the relational latent space into semantic interactive behaviors defined with expert domain knowledge. We demonstrate that it can model interactive traffic scenarios under both simulation and real-world settings, and generate semantic graphs explaining the vehicle's behavior by their interactions.

Related papers

• 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)
• 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)
• PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Driving [57.89801036693292]
  PPAD (Iterative Interaction of Prediction and Planning Autonomous Driving) considers the timestep-wise interaction to better integrate prediction and planning. We design ego-to-agent, ego-to-map, and ego-to-BEV interaction mechanisms with hierarchical dynamic key objects attention to better model the interactions.
  arXiv  Detail & Related papers  (2023-11-14T11:53:24Z)
• Learning Heterogeneous Interaction Strengths by Trajectory Prediction with Graph Neural Network [0.0]
  We propose the attentive relational inference network (RAIN) to infer continuously weighted interaction graphs without any ground-truth interaction strengths. We show that our RAIN model with the PA mechanism accurately infers continuous interaction strengths for simulated physical systems in an unsupervised manner.
  arXiv  Detail & Related papers  (2022-08-28T09:13:33Z)
• 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)
• Controlling the Sense of Agency in Dyadic Robot Interaction: An Active Inference Approach [6.421670116083633]
  We examine dyadic imitative interactions of robots using a variational recurrent neural network model. We examined how regulating the complexity term to minimize free energy during training determines the dynamic characteristics of networks.
  arXiv  Detail & Related papers  (2021-03-03T02:38:09Z)
• Interaction-Based Trajectory Prediction Over a Hybrid Traffic Graph [4.574413934477815]
  We propose to use a hybrid graph whose nodes represent both the traffic actors as well as the static and dynamic traffic elements present in the scene. The different modes of temporal interaction (e.g., stopping and going) among actors and traffic elements are explicitly modeled by graph edges. We show that our proposed model, TrafficGraphNet, achieves state-of-the-art trajectory prediction accuracy while maintaining a high level of interpretability.
  arXiv  Detail & Related papers  (2020-09-27T18:20:03Z)
• Implicit Latent Variable Model for Scene-Consistent Motion Forecasting [78.74510891099395]
  In this paper, we aim to learn scene-consistent motion forecasts of complex urban traffic directly from sensor data. We model the scene as an interaction graph and employ powerful graph neural networks to learn a distributed latent representation of the scene.
  arXiv  Detail & Related papers  (2020-07-23T14:31:25Z)
• Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios [11.893098067272463]
  Interpretation of common-yet-challenging interaction scenarios can benefit well-founded decisions for autonomous vehicles. This paper describes a Bayesian nonparametric approach that leverages continuous (i.e., Gaussian processes) and discrete (i.e., Dirichlet processes) processes. Our proposed approach sheds light on efficiently analyzing other kinds of multi-agent interactions, such as vehiclepedestrian interactions.
  arXiv  Detail & Related papers  (2020-03-02T10:59:35Z)

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.