Real-World Scenario Mining for the Assessment of Automated Vehicles

• URL: http://arxiv.org/abs/2006.00483v2
• Date: Mon, 12 Jul 2021 09:33:28 GMT
• Title: Real-World Scenario Mining for the Assessment of Automated Vehicles
• Authors: Erwin de Gelder, Jeroen Manders, Corrado Grappiolo, Jan-Pieter Paardekooper, Olaf Op den Camp, Bart De Schutter
• Abstract summary: We propose a new method to capture scenarios from real-world data using a two-step approach. The method is not specific for one type of scenario and, therefore, it can be applied to a large variety of scenarios.
• Score: 12.962830182937035
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scenario-based methods for the assessment of Automated Vehicles (AVs) are widely supported by many players in the automotive field. Scenarios captured from real-world data can be used to define the scenarios for the assessment and to estimate their relevance. Therefore, different techniques are proposed for capturing scenarios from real-world data. In this paper, we propose a new method to capture scenarios from real-world data using a two-step approach. The first step consists in automatically labeling the data with tags. Second, we mine the scenarios, represented by a combination of tags, based on the labeled tags. One of the benefits of our approach is that the tags can be used to identify characteristics of a scenario that are shared among different type of scenarios. In this way, these characteristics need to be identified only once. Furthermore, the method is not specific for one type of scenario and, therefore, it can be applied to a large variety of scenarios. We provide two examples to illustrate the method. This paper is concluded with some promising future possibilities for our approach, such as automatic generation of scenarios for the assessment of automated vehicles.

Related papers

• Adaptive Utilization of Cross-scenario Information for Multi-scenario Recommendation [11.489766641148151]
  Multi-scenario Recommendation (MSR) is an important topic that improves ranking performance by leveraging information from different scenarios. Recent methods for MSR mostly construct scenario shared or specific modules to model commonalities and differences among scenarios. We propose a unified model named Cross-Scenario Information Interaction (CSII) to serve all scenarios by a mixture of scenario-dominated experts.
  arXiv  Detail & Related papers  (2024-07-29T06:17:33Z)
• GM-DF: Generalized Multi-Scenario Deepfake Detection [49.072106087564144]
  Existing face forgery detection usually follows the paradigm of training models in a single domain. In this paper, we elaborately investigate the generalization capacity of deepfake detection models when jointly trained on multiple face forgery detection datasets.
  arXiv  Detail & Related papers  (2024-06-28T17:42:08Z)
• scenario.center: Methods from Real-world Data to a Scenario Database [0.0]
  This paper presents the scenario database scenario.center to process and manage scenario data. A common input format with defined quality requirements is defined. For evaluation, the methodology is compared to state-of-the-art scenario databases.
  arXiv  Detail & Related papers  (2024-04-03T08:29:44Z)
• Causality-based Transfer of Driving Scenarios to Unseen Intersections [0.0]
  In scenario-based testing automated functions are evaluated in a set of pre-defined scenarios. To create realistic scenarios, parameters and parameter dependencies have to be fitted utilizing real-world data. This paper proposes a methodology to systematically analyze relations between parameters of scenarios.
  arXiv  Detail & Related papers  (2024-04-02T15:38:18Z)
• Graph Convolutional Networks for Complex Traffic Scenario Classification [0.7919810878571297]
  A scenario-based testing approach can reduce the time required to obtain statistically significant evidence of the safety of Automated Driving Systems. Most methods on scenario classification do not work for complex scenarios with diverse environments. We propose a method for complex traffic scenario classification that is able to model the interaction of a vehicle with the environment.
  arXiv  Detail & Related papers  (2023-10-26T20:51:24Z)
• Tree-Based Scenario Classification: A Formal Framework for Coverage Analysis on Test Drives of Autonomous Vehicles [0.0]
  In scenario-based testing, relevant (driving) scenarios are the basis of tests. We address the open challenges of classifying sets of scenarios and measuring coverage of theses scenarios in recorded test drives.
  arXiv  Detail & Related papers  (2023-07-11T08:30:57Z)
• 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)
• 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)
• Benchmarking Multimodal AutoML for Tabular Data with Text Fields [83.43249184357053]
  We assemble 18 multimodal data tables that each contain some text fields. Our benchmark enables researchers to evaluate their own methods for supervised learning with numeric, categorical, and text features.
  arXiv  Detail & Related papers  (2021-11-04T09:29:16Z)
• 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)
• Text Recognition in Real Scenarios with a Few Labeled Samples [55.07859517380136]
  Scene text recognition (STR) is still a hot research topic in computer vision field. This paper proposes a few-shot adversarial sequence domain adaptation (FASDA) approach to build sequence adaptation. Our approach can maximize the character-level confusion between the source domain and the target domain.
  arXiv  Detail & Related papers  (2020-06-22T13:03:01Z)

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.