Development of ADAS perception applications in ROS and "Software-In-the-Loop" validation with CARLA simulator

Stevic, Stevan; Krunić, Momčilo; Dragojević, Marko; Kaprocki, Nives

doi:10.5937/telfor2001040s

Cited by 8 publications

(4 citation statements)

References 7 publications

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“…[147] purpose is to evaluate the correctness and reliability of the functionalities executed by the testing objects in the virtual world. [48] Once the performed functionalities in SIL simulation are satisfactory, the software is embedded into relevant hardware for the execution of HIL. [45] Within the simulation platform, HIL provides an emulated plant to be controlled, which represents the physical subsystems such as sensors, actuators, and mechanical components with mathematical models.…”

Section: Controllabilitymentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Testbench‐Based Vehicle‐in‐the‐Loop Simulation Testing for Autonomous Vehicles: Architecture, Principle, and Equipment

Cheng,

Wang,

Zhao

et al. 2024

Advanced Intelligent Systems

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Autonomous vehicles (AVs) must be thoroughly tested to ensure safety and reliability before marketing. Simulation‐based testing has gained widespread recognition as the essential approach for AV testing by providing sufficient testing scenarios in the virtual environment. Vehicle‐in‐the‐loop (VIL) simulation has the ability to perform comprehensive tests and validations for the AVs’ overall behaviors while keeping significant testing accuracy and efficiency through the combination of the virtual scenarios and the physical AV. This article provides an overview of representative studies on testbench‐based VIL simulation testing for AVs, mainly focusing on utilizing testbenches to simulate realistic road conditions, and using physical signal stimulation methods and related equipment to generate sensors’ physical signals. This article first summarizes current AV testing studies, identifying existing issues and flaws of the state‐of‐the‐art methods and tools. Afterward, the testbench‐based VIL is addressed around architecture, principles, advantages, and characteristics. Then, the road condition simulation and the sensor physical signal generation in VIL are discussed in depth from structure, principle, and corresponding advanced equipment. Finally, research gaps between cutting‐edge technologies and AV testing applications in industrialization are identified to facilitate future research in this direction.

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Section: Controllabilitymentioning

confidence: 99%

“…SIL focuses on testing AVUT's algorithms, models, as well as software stacks, it is generally applied in the preliminary phases of simulation testing, [ 47 ] its purpose is to evaluate the correctness and reliability of the functionalities executed by the testing objects in the virtual world. [ 48 ]…”

Section: Overview Of Autonomous Vehicle Testingmentioning

confidence: 99%

A Survey on Testbench‐Based Vehicle‐in‐the‐Loop Simulation Testing for Autonomous Vehicles: Architecture, Principle, and Equipment

Cheng,

Wang,

Zhao

et al. 2024

Advanced Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…Another study by Stevic et al [21] highlighted the challenges associated with autonomous driving, particularly those arising from the unpredictability of the physical world. They emphasised that unpredictable circumstances could lead to accidents, making it crucial to develop robust testing mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Investigation of collision estimation with vehicle and pedestrian using CARLA simulation software

Beg,

Ismail

2024

J. Mech. Eng. Sci.

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The effectiveness of object detection systems in diverse driving environments is crucial in the growing field of automotive safety. The increasing frequency of traffic accidents, especially at busy intersections with heavy traffic and limited visibility, highlights the pressing requirement for advanced vehicle detection systems. Prior to implementing the real-time experiment, it is advisable first to conduct a simulation in order to gain a deeper understanding of the practical implementation in real-time scenarios. On the other hand, this approach has the potential to reduce both time and cost significantly. The system utilised a software-based solution by implementing the CARLA simulator. This study aims to analyse vehicle detection at T-junctions, cross-junctions, and roundabouts using image data obtained from the CARLA platform. Subsequent analysis differentiates between vehicles and non-vehicle objects in the dataset. The model concludes by proposing Python-based integrative solutions to enhance object detection systems for diverse roads and atmospheric situations. The significance of this study is evaluating the probability of accidents by tracking key factors like vehicle speed, distance, and density on various road types. In future research, it will be essential to investigate how different weather conditions, including rain, haze, and low-light scenarios, affect on sensor performance, specifically LiDAR sensors. Advanced machine learning techniques are proposed to evaluate the effectiveness of the vehicle detection system in collecting key parameters like vehicle count, speed, and distance in junction and roundabout scenarios. These findings have important implications for the advancement of more efficient, context-aware detection systems in the automotive sector.

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“…This technology gives abundant training datasets about abnormal situations and can potentially prevent traffic accidents more perfectly. Therefore, many studies have been conducted for modeling vehicles in simulation [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

Effects of Sim2Real Image Translation via DCLGAN on Lane Keeping Assist System in CARLA Simulator

et al. 2023

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Autonomous vehicle (AV) simulation using a virtual environment has the advantage of being able to test algorithms in various scenarios with reduced resources. However, there may exist a visual gap between the virtual environment and the real-world. In this paper, in order to mitigate this gap, we trained Dual Contrastive Learning Generative Adversarial Networks (DCLGAN) to realistically convert the image of the CARLA simulator and then evaluated the effect of the Sim2Real conversion focusing on the lane keeping assist system (LKAS). Moreover, in order to avoid the case where the lane is translated distortedly by DCLGAN, we found the optimal training hyperparameters using feature similarity (FSIM).After training, we built a system that connected the CARLA simulator with DCLGAN and AV in realtime. As for the result, we collected data and analyzed them using the following four methods. First, image reality was measured with Fréchet Inception Distance (FID), which we quantitatively verified to reflect the lane characteristics. The CARLA images that passed through DCLGAN had smaller FID values than the original images. Second, lane segmentation accuracy through ENet-SAD was improved by DCLGAN. Third, in the curved route, the case of using DCLGAN drove closer to the center of the lane and had a high success rate. Lastly, in the straight route, DCLGAN improved lane restoring ability after deviating from the center of the lane as much as in reality. Consequently, it convinced that the proposed method could be applicable to mitigate the gap of simulation toward real-world.

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Development of ADAS perception applications in ROS and "Software-In-the-Loop" validation with CARLA simulator

Cited by 8 publications

References 7 publications

A Survey on Testbench‐Based Vehicle‐in‐the‐Loop Simulation Testing for Autonomous Vehicles: Architecture, Principle, and Equipment

A Survey on Testbench‐Based Vehicle‐in‐the‐Loop Simulation Testing for Autonomous Vehicles: Architecture, Principle, and Equipment

Investigation of collision estimation with vehicle and pedestrian using CARLA simulation software

Effects of Sim2Real Image Translation via DCLGAN on Lane Keeping Assist System in CARLA Simulator

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