CERES: Critical-Event Reconstruction via Temporal Scene Graph Completion

• URL: http://arxiv.org/abs/2410.13514v1
• Date: Thu, 17 Oct 2024 13:02:06 GMT
• Title: CERES: Critical-Event Reconstruction via Temporal Scene Graph Completion
• Authors: Efimia Panagiotaki, Georgi Pramatarov, Lars Kunze, Daniele De Martini,
• Abstract summary: This paper proposes a method for on-demand scenario generation in simulation, grounded on real-world data. By integrating scenarios derived from real-world datasets into the simulation, we enhance the plausibility and validity of testing.
• Score: 7.542220697870245
• License:
• Abstract: This paper proposes a method for on-demand scenario generation in simulation, grounded on real-world data. Evaluating the behaviour of Autonomous Vehicles (AVs) in both safety-critical and regular scenarios is essential for assessing their robustness before real-world deployment. By integrating scenarios derived from real-world datasets into the simulation, we enhance the plausibility and validity of testing sets. This work introduces a novel approach that employs temporal scene graphs to capture evolving spatiotemporal relationships among scene entities from a real-world dataset, enabling the generation of dynamic scenarios in simulation through Graph Neural Networks (GNNs). User-defined action and criticality conditioning are used to ensure flexible, tailored scenario creation. Our model significantly outperforms the benchmarks in accurately predicting links corresponding to the requested scenarios. We further evaluate the validity and compatibility of our generated scenarios in an off-the-shelf simulator.

Related papers

• Testing Generalizability in Causal Inference [3.547529079746247]
  There is no formal procedure for statistically evaluating generalizability in machine learning algorithms. We propose a systematic and quantitative framework for evaluating model generalizability in causal inference settings. By basing simulations on real data, our method ensures more realistic evaluations, which is often missing in current work.
  arXiv  Detail & Related papers  (2024-11-05T11:44:00Z)
• Compositional simulation-based inference for time series [21.9975782468709]
  simulators frequently emulate real-world dynamics through thousands of single-state transitions over time. We propose an SBI framework that can exploit such Markovian simulators by locally identifying parameters consistent with individual state transitions. We then compose these local results to obtain a posterior over parameters that align with the entire time series observation.
  arXiv  Detail & Related papers  (2024-11-05T01:55:07Z)
• OmniPose6D: Towards Short-Term Object Pose Tracking in Dynamic Scenes from Monocular RGB [40.62577054196799]
  We introduce a large-scale synthetic dataset OmniPose6D, crafted to mirror the diversity of real-world conditions. We present a benchmarking framework for a comprehensive comparison of pose tracking algorithms.
  arXiv  Detail & Related papers  (2024-10-09T09:01:40Z)
• RealGen: Retrieval Augmented Generation for Controllable Traffic Scenarios [58.62407014256686]
  RealGen is a novel retrieval-based in-context learning framework for traffic scenario generation. RealGen synthesizes new scenarios by combining behaviors from multiple retrieved examples in a gradient-free way. This in-context learning framework endows versatile generative capabilities, including the ability to edit scenarios.
  arXiv  Detail & Related papers  (2023-12-19T23:11:06Z)
• 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)
• A Diffusion-Model of Joint Interactive Navigation [14.689298253430568]
  We present DJINN - a diffusion based method of generating traffic scenarios. Our approach jointly diffuses the trajectories of all agents, conditioned on a flexible set of state observations from the past, present, or future. We show how DJINN flexibly enables direct test-time sampling from a variety of valuable conditional distributions.
  arXiv  Detail & Related papers  (2023-09-21T22:10:20Z)
• One-Shot Domain Adaptive and Generalizable Semantic Segmentation with Class-Aware Cross-Domain Transformers [96.51828911883456]
  Unsupervised sim-to-real domain adaptation (UDA) for semantic segmentation aims to improve the real-world test performance of a model trained on simulated data. Traditional UDA often assumes that there are abundant unlabeled real-world data samples available during training for the adaptation. We explore the one-shot unsupervised sim-to-real domain adaptation (OSUDA) and generalization problem, where only one real-world data sample is available.
  arXiv  Detail & Related papers  (2022-12-14T15:54:15Z)
• Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior [135.78858513845233]
  STRIVE is a method to automatically generate challenging scenarios that cause a given planner to produce undesirable behavior, like collisions. To maintain scenario plausibility, the key idea is to leverage a learned model of traffic motion in the form of a graph-based conditional VAE. A subsequent optimization is used to find a "solution" to the scenario, ensuring it is useful to improve the given planner.
  arXiv  Detail & Related papers  (2021-12-09T18:03:27Z)
• Querying Labelled Data with Scenario Programs for Sim-to-Real Validation [5.8720142291102135]
  A fundamental question remains: are AV failure scenarios identified in simulation meaningful in reality, i.e., are they reproducible on the real system? An approach to validate simulated failure scenarios is to identify instances of the scenario in a corpus of real data, and check if the failure persists on the real data. We propose a formal definition of what it means for a labelled data item to match an abstract scenario, encoded as a scenario program using the SCENIC probabilistic programming language.
  arXiv  Detail & Related papers  (2021-12-01T01:04:13Z)
• Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles [86.9067793493874]
  We propose efficient mechanisms to characterize and generate testing scenarios using a state-of-the-art driving simulator. We use our method to characterize real driving data from the Next Generation Simulation (NGSIM) project. We rank the scenarios by defining metrics based on the complexity of avoiding accidents and provide insights into how the AV could have minimized the probability of incurring an accident.
  arXiv  Detail & Related papers  (2021-03-12T17:00:23Z)
• A Multi-Channel Neural Graphical Event Model with Negative Evidence [76.51278722190607]
  Event datasets are sequences of events of various types occurring irregularly over the time-line. We propose a non-parametric deep neural network approach in order to estimate the underlying intensity functions.
  arXiv  Detail & Related papers  (2020-02-21T23:10:50Z)

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.