A Joint Approach Towards Data-Driven Virtual Testing for Automated Driving: The AVEAS Project

• URL: http://arxiv.org/abs/2405.06286v1
• Date: Fri, 10 May 2024 07:36:03 GMT
• Title: A Joint Approach Towards Data-Driven Virtual Testing for Automated Driving: The AVEAS Project
• Authors: Leon Eisemann, Mirjam Fehling-Kaschek, Silke Forkert, Andreas Forster, Henrik Gommel, Susanne Guenther, Stephan Hammer, David Hermann, Marvin Klemp, Benjamin Lickert, Florian Luettner, Robin Moss, Nicole Neis, Maria Pohle, Dominik Schreiber, Cathrina Sowa, Daniel Stadler, Janina Stompe, Michael Strobelt, David Unger, Jens Ziehn,
• Abstract summary: There is a significant shortage of real-world data to parametrize and/or validate simulations. This paper presents the results of the German AVEAS research project.
• Score: 2.4163276807189282
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: With growing complexity and responsibility of automated driving functions in road traffic and growing scope of their operational design domains, there is increasing demand for covering significant parts of development, validation, and verification via virtual environments and simulation models. If, however, simulations are meant not only to augment real-world experiments, but to replace them, quantitative approaches are required that measure to what degree and under which preconditions simulation models adequately represent reality, and thus allow their usage for virtual testing of driving functions. Especially in research and development areas related to the safety impacts of the "open world", there is a significant shortage of real-world data to parametrize and/or validate simulations - especially with respect to the behavior of human traffic participants, whom automated vehicles will meet in mixed traffic. This paper presents the intermediate results of the German AVEAS research project (www.aveas.org) which aims at developing methods and metrics for the harmonized, systematic, and scalable acquisition of real-world data for virtual verification and validation of advanced driver assistance systems and automated driving, and establishing an online database following the FAIR principles.

Related papers

• DrivingSphere: Building a High-fidelity 4D World for Closed-loop Simulation [54.02069690134526]
  We propose DrivingSphere, a realistic and closed-loop simulation framework. Its core idea is to build 4D world representation and generate real-life and controllable driving scenarios. By providing a dynamic and realistic simulation environment, DrivingSphere enables comprehensive testing and validation of autonomous driving algorithms.
  arXiv  Detail & Related papers  (2024-11-18T03:00:33Z)
• An Approach to Systematic Data Acquisition and Data-Driven Simulation for the Safety Testing of Automated Driving Functions [32.37902846268263]
  In R&D areas related to the safety impact of the "open world", there is a significant shortage of real-world data to parameterize and/or validate simulations. We present an approach to systematically acquire data in public traffic by heterogeneous means, transform it into a unified representation, and use it to automatically parameterize traffic behavior models for use in data-driven virtual validation of automated driving functions.
  arXiv  Detail & Related papers  (2024-05-02T23:24:27Z)
• Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research [76.93956925360638]
  Waymax is a new data-driven simulator for autonomous driving in multi-agent scenes. It runs entirely on hardware accelerators such as TPUs/GPUs and supports in-graph simulation for training. We benchmark a suite of popular imitation and reinforcement learning algorithms with ablation studies on different design decisions.
  arXiv  Detail & Related papers  (2023-10-12T20:49:15Z)
• Reinforcement Learning with Human Feedback for Realistic Traffic Simulation [53.85002640149283]
  Key element of effective simulation is the incorporation of realistic traffic models that align with human knowledge. This study identifies two main challenges: capturing the nuances of human preferences on realism and the unification of diverse traffic simulation models.
  arXiv  Detail & Related papers  (2023-09-01T19:29:53Z)
• From Model-Based to Data-Driven Simulation: Challenges and Trends in Autonomous Driving [26.397030011439163]
  We provide an overview of challenges with regard to different aspects and types of simulation. We cover aspects around perception-, behavior- and content-realism as well as general hurdles in the domain of simulation. Among others, we observe a trend of data-driven, generative approaches and high-fidelity data synthesis to increasingly replace model-based simulation.
  arXiv  Detail & Related papers  (2023-05-23T11:39:23Z)
• TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction [149.5716746789134]
  We show data-driven traffic simulation can be formulated as a world model. We present TrafficBots, a multi-agent policy built upon motion prediction and end-to-end driving. Experiments on the open motion dataset show TrafficBots can simulate realistic multi-agent behaviors.
  arXiv  Detail & Related papers  (2023-03-07T18:28:41Z)
• Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses [130.15554653948897]
  In vehicular mixed reality (MR) Metaverse, distance between physical and virtual entities can be overcome. Large-scale traffic and driving simulation via realistic data collection and fusion from the physical world is difficult and costly. We propose an autonomous driving architecture, where generative AI is leveraged to synthesize unlimited conditioned traffic and driving data in simulations.
  arXiv  Detail & Related papers  (2023-02-16T16:54:10Z)
• BITS: Bi-level Imitation for Traffic Simulation [38.28736985320897]
  We take a data-driven approach and propose a method that can learn to generate traffic behaviors from real-world driving logs. We empirically validate our method, named Bi-level Imitation for Traffic Simulation (BITS), with scenarios from two large-scale driving datasets. As part of our core contributions, we develop and open source a software tool that unifies data formats across different driving datasets.
  arXiv  Detail & Related papers  (2022-08-26T02:17:54Z)
• Augmented Driver Behavior Models for High-Fidelity Simulation Study of Crash Detection Algorithms [2.064612766965483]
  We present a simulation platform for a hybrid transportation system that includes both human-driven and automated vehicles. We decompose the human driving task and offer a modular approach to simulating a large-scale traffic scenario. We analyze a large driving dataset to extract expressive parameters that would best describe different driving characteristics.
  arXiv  Detail & Related papers  (2022-08-10T19:59:16Z)
• Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction [88.0416857308144]
  We propose an alternative to sensor simulation, as sensor simulation is expensive and has large domain gaps. We directly simulate the outputs of the self-driving vehicle's perception and prediction system, enabling realistic motion planning testing.
  arXiv  Detail & Related papers  (2020-08-13T17:20:02Z)

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.