Correlation of Software-in-the-Loop Simulation with Physical Testing for Autonomous Driving

• URL: http://arxiv.org/abs/2406.03040v1
• Date: Wed, 5 Jun 2024 08:11:10 GMT
• Title: Correlation of Software-in-the-Loop Simulation with Physical Testing for Autonomous Driving
• Authors: Zhennan Fei, Mikael Andersson, Andreas Tingberg,
• Abstract summary: This paper presents a case study on the validation of an in-house developed SIL simulation toolchain. To align the test track runs with the SIL simulations, a synchronization approach is proposed. Preliminary results are presented to demonstrate the effectiveness of the proposed validation process.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Software-in-the-loop (SIL) simulation is a widely used method for the rapid development and testing of autonomous vehicles because of its flexibility and efficiency. This paper presents a case study on the validation of an in-house developed SIL simulation toolchain. The presented validation process involves the design and execution of a set of representative scenarios on the test track. To align the test track runs with the SIL simulations, a synchronization approach is proposed, which includes refining the scenarios by fine-tuning the parameters based on data obtained from vehicle testing. The paper also discusses two metrics used for evaluating the correlation between the SIL simulations and the vehicle testing logs. Preliminary results are presented to demonstrate the effectiveness of the proposed validation process

Related papers

• Active Test-Time Adaptation: Theoretical Analyses and An Algorithm [51.84691955495693]
  Test-time adaptation (TTA) addresses distribution shifts for streaming test data in unsupervised settings. We propose the novel problem setting of active test-time adaptation (ATTA) that integrates active learning within the fully TTA setting.
  arXiv  Detail & Related papers  (2024-04-07T22:31:34Z)
• Perception Imitation: Towards Synthesis-free Simulator for Autonomous Vehicles [45.27200446670184]
  We propose a perception imitation method to simulate results of a certain perception model, and discuss a new route of autonomous driving simulator without data synthesis. Experiments show that our method is effective to model the behavior of learning-based perception model, and can be further applied in the proposed simulation route smoothly.
  arXiv  Detail & Related papers  (2023-04-19T01:27:02Z)
• Synthetic Data-Based Simulators for Recommender Systems: A Survey [55.60116686945561]
  This survey aims at providing a comprehensive overview of the recent trends in the field of modeling and simulation. We start with the motivation behind the development of frameworks implementing the simulations -- simulators. We provide a new consistent classification of existing simulators based on their functionality, approbation, and industrial effectiveness.
  arXiv  Detail & Related papers  (2022-06-22T19:33:21Z)
• Efficient and Effective Generation of Test Cases for Pedestrian Detection -- Search-based Software Testing of Baidu Apollo in SVL [14.482670650074885]
  This paper presents a study on testing pedestrian detection and emergency braking system of the Baidu Apollo autonomous driving platform within the SVL simulator. We propose an evolutionary automated test generation technique that generates failure-revealing scenarios for Apollo in the SVL environment. In order to demonstrate the efficiency and effectiveness of our approach, we also report the results from a baseline random generation technique.
  arXiv  Detail & Related papers  (2021-09-16T13:11:53Z)
• Identification of Vehicle Dynamics Parameters Using Simulation-based Inference [1.933681537640272]
  This paper proposes a new method: Simulation-Based Inference ( SBI), a modern interpretation of Approximate Bayesian Computation methods (ABC) for parameter identification. We demonstrate in this paper that it can handle the identification of highly nonlinear vehicle dynamics parameters and gives accurate estimates of the parameters for the governing equations.
  arXiv  Detail & Related papers  (2021-08-27T04:28:14Z)
• 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)
• Digital Twins Are Not Monozygotic -- Cross-Replicating ADAS Testing in Two Industry-Grade Automotive Simulators [13.386879259549305]
  We show that SBST can be used to effectively and efficiently generate critical test scenarios in two simulators. We find that executing the same test scenarios in the two simulators leads to notable differences in the details of the test outputs.
  arXiv  Detail & Related papers  (2020-12-12T14:00:33Z)
• A User's Guide to Calibrating Robotics Simulators [54.85241102329546]
  This paper proposes a set of benchmarks and a framework for the study of various algorithms aimed to transfer models and policies learnt in simulation to the real world. We conduct experiments on a wide range of well known simulated environments to characterize and offer insights into the performance of different algorithms. Our analysis can be useful for practitioners working in this area and can help make informed choices about the behavior and main properties of sim-to-real algorithms.
  arXiv  Detail & Related papers  (2020-11-17T22:24:26Z)
• 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)
• Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real World [8.498542964344987]
  We present a new approach to automated scenario-based testing of the safety of autonomous vehicles. Our approach is based on formal methods, combining formal specification of scenarios and safety properties.
  arXiv  Detail & Related papers  (2020-03-17T14:17:52Z)

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.