Driving scenario generation and evaluation using a structured layer representation and foundational models

• URL: http://arxiv.org/abs/2511.01541v1
• Date: Mon, 03 Nov 2025 13:04:55 GMT
• Title: Driving scenario generation and evaluation using a structured layer representation and foundational models
• Authors: Arthur Hubert, Gamal Elghazaly, Raphaël Frank,
• Abstract summary: Rare and challenging driving scenarios are critical for autonomous vehicle development.<n>We propose a structured five-layer model to improve the evaluation and generation of rare scenarios.<n>This paper showcases two metrics to evaluate the relevance of a synthetic dataset in the context of a structured representation.
• Score: 0.17205106391379021
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Rare and challenging driving scenarios are critical for autonomous vehicle development. Since they are difficult to encounter, simulating or generating them using generative models is a popular approach. Following previous efforts to structure driving scenario representations in a layer model, we propose a structured five-layer model to improve the evaluation and generation of rare scenarios. We use this model alongside large foundational models to generate new driving scenarios using a data augmentation strategy. Unlike previous representations, our structure introduces subclasses and characteristics for every agent of the scenario, allowing us to compare them using an embedding specific to our layer-model. We study and adapt two metrics to evaluate the relevance of a synthetic dataset in the context of a structured representation: the diversity score estimates how different the scenarios of a dataset are from one another, while the originality score calculates how similar a synthetic dataset is from a real reference set. This paper showcases both metrics in different generation setup, as well as a qualitative evaluation of synthetic videos generated from structured scenario descriptions. The code and extended results can be found at https://github.com/Valgiz/5LMSG.

Related papers

• Reinforcing Pre-trained Models Using Counterfactual Images [54.26310919385808]
  This paper proposes a novel framework to reinforce classification models using language-guided generated counterfactual images. We identify model weaknesses by testing the model using the counterfactual image dataset. We employ the counterfactual images as an augmented dataset to fine-tune and reinforce the classification model.
  arXiv  Detail & Related papers  (2024-06-19T08:07:14Z)
• Latent Semantic Consensus For Deterministic Geometric Model Fitting [109.44565542031384]
  We propose an effective method called Latent Semantic Consensus (LSC) LSC formulates the model fitting problem into two latent semantic spaces based on data points and model hypotheses. LSC is able to provide consistent and reliable solutions within only a few milliseconds for general multi-structural model fitting.
  arXiv  Detail & Related papers  (2024-03-11T05:35:38Z)
• Anchor Points: Benchmarking Models with Much Fewer Examples [88.02417913161356]
  In six popular language classification benchmarks, model confidence in the correct class on many pairs of points is strongly correlated across models. We propose Anchor Point Selection, a technique to select small subsets of datasets that capture model behavior across the entire dataset. Just several anchor points can be used to estimate model per-class predictions on all other points in a dataset with low mean absolute error.
  arXiv  Detail & Related papers  (2023-09-14T17:45:51Z)
• UMSE: Unified Multi-scenario Summarization Evaluation [52.60867881867428]
  Summarization quality evaluation is a non-trivial task in text summarization. We propose Unified Multi-scenario Summarization Evaluation Model (UMSE) Our UMSE is the first unified summarization evaluation framework engaged with the ability to be used in three evaluation scenarios.
  arXiv  Detail & Related papers  (2023-05-26T12:54:44Z)
• Revisiting the Evaluation of Image Synthesis with GANs [55.72247435112475]
  This study presents an empirical investigation into the evaluation of synthesis performance, with generative adversarial networks (GANs) as a representative of generative models. In particular, we make in-depth analyses of various factors, including how to represent a data point in the representation space, how to calculate a fair distance using selected samples, and how many instances to use from each set.
  arXiv  Detail & Related papers  (2023-04-04T17:54:32Z)
• Natural Language-Based Synthetic Data Generation for Cluster Analysis [4.13592995550836]
  Cluster analysis relies on effective benchmarks for evaluating and comparing different algorithms.<n>We propose synthetic data generation based on direct specification of high-level scenarios.<n>Our open-source Python package repliclust implements this workflow.
  arXiv  Detail & Related papers  (2023-03-24T23:45:27Z)
• Next-Year Bankruptcy Prediction from Textual Data: Benchmark and Baselines [10.944533132358439]
  Models for bankruptcy prediction are useful in several real-world scenarios. The lack of a common benchmark dataset and evaluation strategy impedes the objective comparison between models. This paper introduces such a benchmark for the unstructured data scenario, based on novel and established datasets.
  arXiv  Detail & Related papers  (2022-08-24T07:11:49Z)
• Label-Free Model Evaluation with Semi-Structured Dataset Representations [78.54590197704088]
  Label-free model evaluation, or AutoEval, estimates model accuracy on unlabeled test sets. In the absence of image labels, based on dataset representations, we estimate model performance for AutoEval with regression. We propose a new semi-structured dataset representation that is manageable for regression learning while containing rich information for AutoEval.
  arXiv  Detail & Related papers  (2021-12-01T18:15:58Z)
• A Topological-Framework to Improve Analysis of Machine Learning Model Performance [5.3893373617126565]
  We propose a framework for evaluating machine learning models in which a dataset is treated as a "space" on which a model operates. We describe a topological data structure, presheaves, which offer a convenient way to store and analyze model performance between different subpopulations.
  arXiv  Detail & Related papers  (2021-07-09T23:11:13Z)
• Learning deep autoregressive models for hierarchical data [0.6445605125467573]
  We propose a model for hierarchical structured data as an extension to the temporal convolutional network (STCN) We evaluate the proposed model on two different types of sequential data: speech and handwritten text.
  arXiv  Detail & Related papers  (2021-04-28T15:58:45Z)

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.