Vectorized Scenario Description and Motion Prediction for Scenario-Based Testing

• URL: http://arxiv.org/abs/2302.01161v2
• Date: Fri, 25 Aug 2023 15:21:50 GMT
• Title: Vectorized Scenario Description and Motion Prediction for Scenario-Based Testing
• Authors: Max Winkelmann, Constantin Vasconi, Steffen M\"uller
• Abstract summary: This paper proposes a vectorized scenario description defined by the road geometry and vehicles' trajectories. Data of this form are generated for three scenarios, merged, and used to train the motion prediction model VectorNet.
• Score: 2.07180164747172
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated vehicles (AVs) are tested in diverse scenarios, typically specified by parameters such as velocities, distances, or curve radii. To describe scenarios uniformly independent of such parameters, this paper proposes a vectorized scenario description defined by the road geometry and vehicles' trajectories. Data of this form are generated for three scenarios, merged, and used to train the motion prediction model VectorNet, allowing to predict an AV's trajectory for unseen scenarios. Predicting scenario evaluation metrics, VectorNet partially achieves lower errors than regression models that separately process the three scenarios' data. However, for comprehensive generalization, sufficient variance in the training data must be ensured. Thus, contrary to existing methods, our proposed method can merge diverse scenarios' data and exploit spatial and temporal nuances in the vectorized scenario description. As a result, data from specified test scenarios and real-world scenarios can be compared and combined for (predictive) analyses and scenario selection.

Related papers

• 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)
• 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)
• DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving [76.29141888408265]
  We propose a large-scale dataset containing diverse accident scenarios that frequently occur in real-world driving. The proposed DeepAccident dataset includes 57K annotated frames and 285K annotated samples, approximately 7 times more than the large-scale nuScenes dataset.
  arXiv  Detail & Related papers  (2023-04-03T17:37:00Z)
• Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation [35.4495536683099]
  We propose a scenario-Adaptive and Self-Supervised (SASS) model to solve the three challenges mentioned above. The model is created symmetrically both in user side and item side, so that we can get distinguishing representations of items in different scenarios. This model also achieves more than 8.0% improvement on Average Watching Time Per User in online A/B tests.
  arXiv  Detail & Related papers  (2022-08-24T11:44:00Z)
• Normalizing Flow-based Day-Ahead Wind Power Scenario Generation for Profitable and Reliable Delivery Commitments by Wind Farm Operators [62.997667081978825]
  We present a specialized scenario generation that utilizes forecast information to generate scenarios for the particular usage in day-ahead scheduling problems. In particular, we use normalizing flows to generate wind power generation scenarios by sampling from a conditional distribution that uses day-ahead wind speed forecasts to tailor the scenarios to the specific day. We apply the generated scenarios in a simple day-ahead bidding problem of a wind electricity producer and run a statistical analysis focusing on whether the scenarios yield profitable and reliable decisions.
  arXiv  Detail & Related papers  (2022-04-05T14:27:25Z)
• 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)
• 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 Scenario-Based Development Framework for Autonomous Driving [0.0]
  This article summarizes the research progress of scenario-based testing and development technology for autonomous vehicles. We propose the definition of scenario, the elements of the scenario, the data source of the scenario, the processing method of the scenario data, and scenario-based V-Model.
  arXiv  Detail & Related papers  (2020-11-03T03:06:48Z)
• SMART: Simultaneous Multi-Agent Recurrent Trajectory Prediction [72.37440317774556]
  We propose advances that address two key challenges in future trajectory prediction. multimodality in both training data and predictions and constant time inference regardless of number of agents.
  arXiv  Detail & Related papers  (2020-07-26T08:17:10Z)
• Real-World Scenario Mining for the Assessment of Automated Vehicles [12.962830182937035]
  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.
  arXiv  Detail & Related papers  (2020-05-31T10:10:39Z)

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.