Validation Method of a Self-Driving Architecture for Unexpected Pedestrian Scenario in CARLA Simulator

Gutiérrez, Raquel Lázaro; Arango, J. Felipe; Gómez-Huélamo, Carlos; Bergasa, Luis M.; Barea, Rafael; Araluce, Javier

doi:10.1109/iv48863.2021.9575884

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…[17] Webots [76], [136] CARLA (Unreal Engine) [3], [21], [52], [60], [62], [196] CARLA (Unreal Engine) + Autoware…”

Section: Simulator Typementioning

confidence: 99%

“…This dominance can be attributed to its open-source nature, continuous community support, and modularity, as evidenced by its frequent pairing with RViz. Although Gazebo currently dominates, the data shows a noticeable increase in the use of game engines such as Unity [31], [68], [89], [94], [103], [124], [206] and Unreal Engine [3], [21], [52], [60], [62], [63], [79], [196]. The reason for the popularity of these engines is that they are capable of producing high-quality, real-time graphics rendering and contain easy-to-use machine learning tools.…”

Section: Rq4: What Ros-compatible Simulation Platforms Are Available ...mentioning

confidence: 99%

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Emerging Trends in Realistic Robotic Simulations: A Comprehensive Systematic Literature Review

Kargar,

Yordanov,

Harvey

et al. 2024

IEEE Access

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Simulation plays a pivotal role in providing safely reproducible scenarios to evaluate the ever-advancing domain of computer science and robotics. It was an essential part of the pandemic when no access to physical spaces was available. The advent of AI-powered platforms in conjunction with enhanced graphics, physics and other sensory engines attracts a new breed of interdisciplinary researchers to enter the robotic field, most notably from computer science, engineering and social sciences. Integration of ROS as a uniform middleware to deploy achieved outcomes in real practice provides an opportunity to move one step closer to the sim-to-real experiences that enables researchers to test ideas beyond the close laboratory spaces. There is a lack of comprehensive evaluation of ROS-enabled simulators, and the integration of advanced AI techniques for realistic scenario replication. This paper addresses this challenge by evaluating ROSenabled simulators in the design and implementation of AI techniques through an in-depth systematic literature review (SLR). This SLR is guided by the research and commercial market demands, employing Population, Intervention, Comparison, Outcome, and Context (PICOC) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) frameworks with a major focus on Wheeled Mobile Robots (WMRs). We also highlight the increasing importance of game engines like Unity and Unreal in future of robotic simulations, especially under modelling close to real experiences. By comparing simulation platform features and capabilities, this paper offers guidance to developers and researchers, enabling them to select the most suitable platform for their projects efficiently that contradicts the commonly in use "one size fits all" approach. Finally, based on the thorough insights from the review, we identify and suggest some key future research directions in AI-enhanced realistic robotic simulations.

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“…[17] Webots [76], [136] CARLA (Unreal Engine) [3], [21], [52], [60], [62], [196] CARLA (Unreal Engine) + Autoware…”

Section: Simulator Typementioning

confidence: 99%

Section: Rq4: What Ros-compatible Simulation Platforms Are Available ...mentioning

confidence: 99%

Emerging Trends in Realistic Robotic Simulations: A Comprehensive Systematic Literature Review

Kargar,

Yordanov,

Harvey

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…A scenario was set according to the distance between a vehicle and a pedestrian. In this scenario, the speed of the vehicle was fixed and the time required to stop (or collide) according to changes in lighting and weather (day, night, rain, and fog) was derived [ 15 ]. Kordani, A.…”

Section: Introductionmentioning

confidence: 99%

RSS Model Improvement Considering Road Conditions for the Application of a Variable Focus Function Camera

Kim

2023

Sensors

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The automobile industry has developed dramatically in recent years, the supply of vehicles has also increased, and thus it has become deeply established in everyday life. Recently, as the supply of vehicles with autonomous driving functions increases, the safety of vehicles is also an emerging issue. Various car-following models for the safe driving of vehicles have long been studied by various people, and recently, a Responsibility-Sensitive Safety (RSS) model has been proposed by Mobileye. However, in existing car-following models or the RSS model, the safe distance between vehicles is presented using only vehicle speed and acceleration information, so there is a limitation in that it cannot respond to changes in road conditions due to the weather. In this paper, in order to ensure safety when the RSS model is applied to a variable focus function camera, an improved RSS model is presented in consideration of the changes in road conditions due to changes in weather, and a safety distance is derived based on the proposed model.

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“…Since autonomous driving technology has been required to have diverse validation processes, autonomous driving simulation is drawing attention as a new testbed. Unlike reality, the simulation can implement complex situations, such as traffic accidents and specific weather, that are hard to reproduce in the real-world [3]- [5]. This technology gives abundant training datasets about abnormal situations and can potentially prevent traffic accidents more perfectly.…”

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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Validation Method of a Self-Driving Architecture for Unexpected Pedestrian Scenario in CARLA Simulator

Cited by 6 publications

References 17 publications

Emerging Trends in Realistic Robotic Simulations: A Comprehensive Systematic Literature Review

Emerging Trends in Realistic Robotic Simulations: A Comprehensive Systematic Literature Review

RSS Model Improvement Considering Road Conditions for the Application of a Variable Focus Function Camera

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

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