Simulation Framework for Development and Testing of Autonomous Vehicles

Abdelhamed, Ahmed; Tewolde, Girma; Kwon, Jaerock

doi:10.1109/iemtronics51293.2020.9216334

Cited by 9 publications

(3 citation statements)

References 5 publications

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“…According to the SAE definition [9], the levels of autonomous driving range from level 0 (fully human-driven) to level 5 (fully autonomous), with most passenger vehicles currently at levels 1-3. The development of level 4 and 5 vehicles requires the successful implementation of algorithms [10,11] for navigation, path tracking, and control, as well as robustness and self-diagnosis capabilities to handle critical situations. However, a significant challenge in this field is to make autonomous vehicles behave more like human drivers, particularly in safety-critical situations.…”

Section: Related Workmentioning

confidence: 99%

Personalized Driving Styles in Safety-Critical Scenarios for Autonomous Vehicles: An Approach Using Driver-in-the-Loop Simulations

Buzdugan,

Butnariu,

Roșu

et al. 2023

Vehicles

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This paper explores the use of driver-in-the-loop simulations to detect personalized driving styles in autonomous vehicles. The driving simulator used in this study is modular and adaptable, allowing for the testing and validation of control and data-collecting systems, as well as the incorporation and proof of car models. The selected scenario is a double lane change maneuver to overtake a stationary obstacle at a relatively high speed. The user’s behavior was recorded, and lateral accelerations during the maneuver were used as criteria to compare the user-driven vehicle and the autonomous one. The tuning parameters of the lateral and longitudinal controllers were modified to obtain different lateral accelerations of the autonomous vehicle. A neural network was developed to find the combination of the two controllers’ tuning parameters to match the driver’s lateral accelerations in the same double lane change overtaking action. The results are promising, and this study suggests that driver-in-the-loop simulations can help increase autonomous vehicles’ safety while preserving individual driving styles. This could result in creating more individualized and secure autonomous driving systems that consider the preferences and behavior of the driver.

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Section: Related Workmentioning

confidence: 99%

Personalized Driving Styles in Safety-Critical Scenarios for Autonomous Vehicles: An Approach Using Driver-in-the-Loop Simulations

Buzdugan,

Butnariu,

Roșu

et al. 2023

Vehicles

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“…Thus, the simulation results are only as good as the simulator used for testing, and the simulated scenarios could be far from real-world conditions. Lately, more dedicated AV simulators have been available for testing selfdriving systems, which are used by industrial developers and academic researchers (7)(8)(9)(10). Such simulators often allow for incorporation of virtual sensors for vehicles in realistic virtual three-dimensional (3D) environments to support testing functionalities such as perception, localization, and vehicle control.…”

mentioning

confidence: 99%

Extensible Co-Simulation Framework for Supporting Cooperative Driving Automation Research

Cui

Shen

Yang

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Autonomous vehicles (AVs) and cooperative automated vehicles (CAVs) are expected to largely reshape our mobility systems. The limited deployment of AVs and CAVs on roads makes it difficult to fully assess their impact and interactions with other road users. Advanced simulations are often sought for conducting accelerated tests of AVs and CAVs in a virtual environment. However, existing off-the-shelf simulators are typically focused on conventional traffic simulation and human-driving simulation. Advanced simulators that enable core functionalities (e.g., sensing and communication) of AVs and CAVs have been underexploited. In this paper, the authors aim to develop a realistic co-simulation framework for testing autonomous driving and cooperative driving automation (CDA). The proposed co-simulation framework utilizes the open-source concept to support the AV and CAV community in developing and deploying AV and CAV technologies. This framework integrates multiple open-source platforms, including Eclipse MOSAIC™ simulation framework, Eclipse Simulation of Urban Mobility (SUMO™) traffic simulator, and CARLA AV driving simulator. The framework enables AV and CAV simulation in mixed traffic environments. The developed co-simulation models have been tested with different scales of networks and traffic flow. The assessment of the co-simulation framework shows that it can support faster-than-real-time simulation for use in accelerated tests with more realistic scenarios. In addition, the developed co-simulation framework is proven to be extensible with the inclusion of other network simulators for supporting vehicle-to-everything (V2X) communication among vehicles.

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“…AbdelHamed et al (2020) utilized Gazebo simulator for testing autonomous driving algorithms in realistic scenarios. The authors developed an environment and a vehicle based on ROS and implemented autonomous vehicle algorithms for testing different use case scenarios.…”

mentioning

confidence: 99%

A 3D Simulation Εnvironment for Αssessing Interactions with Autonomous Vehicles

Karamitsos,

Karamanos,

Vlachos

et al. 2022

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Self-driving passenger cars are already on public roads to support the vision of a sustainable city. The research community and academia should further support the human-machine symbiosis of autonomous vehicles, manually driven vehicles, and even pedestrians. At the time being, most vehicles operate at level 2 of the Society of Engineers (SAE) autonomy scale as they only have partial automation. However, they are expected to escalate due to cost reduction of essential hardware components (sensors and actuators) and software advancements in Deep Neural Networks (DNN). The main challenge is thoroughly testing self-driving behaviors and human-machine symbiosis scenarios for improving safety conditions and engaging citizens in this concept. Video game simulators could provide a basis for testing and showcasing the innovative features of autonomous driving. Unreal Engine and Unity are two prominent examples of software tools for creating photorealistic graphical environments. In addition, the scientific community and academia elaborate on open-source simulators due to their interoperability features for data exchange and their inherent connections with robotic frameworks. The proposed research work presents an integration framework for the CARLA simulator. The framework uses an integrated Graphical User Interface (GUI) to facilitate the deployment of a fully functional simulation that enables users to include an environment for the passenger cars and several Non-Playable Characters (NPCs) that will participate in the simulation. The purpose of this study is to exploit the interactions of manually driven and autonomous vehicles in a 3D simulation environment and provide evidence that further supports human-machine symbiosis on public roads.

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Simulation Framework for Development and Testing of Autonomous Vehicles

Cited by 9 publications

References 5 publications

Personalized Driving Styles in Safety-Critical Scenarios for Autonomous Vehicles: An Approach Using Driver-in-the-Loop Simulations

Personalized Driving Styles in Safety-Critical Scenarios for Autonomous Vehicles: An Approach Using Driver-in-the-Loop Simulations

Extensible Co-Simulation Framework for Supporting Cooperative Driving Automation Research

A 3D Simulation Εnvironment for Αssessing Interactions with Autonomous Vehicles

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