Optimizing Deep-Neural-Network-Driven Autonomous Race Car Using Image Scaling

Mahmoud, Yaqub; Okuyama, Yuichi; Fukuchi, Tomohide; Tanaka, Kosuke; Ando, Iori

doi:10.1051/shsconf/20207704002

Cited by 6 publications

(1 citation statement)

References 10 publications

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“…It can be controlled by a joystick, and it provides a Unity-based self-simulation program. We can train the DonkeyCar as an autopilot with Keras, by controlling the DonkeyCar on the same road multiple times [27,28]. However, in real-world situations, self-driving cars encounter untrained road environments and obstacles, so we have to deal with image data captured with a camera.…”

Section: Self-driving Algorithm Implemented By Donkeycarmentioning

confidence: 99%

Virtualization of Self-Driving Algorithms by Interoperating Embedded Controllers on a Game Engine for a Digital Twining Autonomous Vehicle

Yun

Park

2021

Electronics

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Computer simulation based on digital twin is an essential process when designing self-driving cars. However, designing a simulation program that is exactly equivalent to real phenomena can be arduous and cost-ineffective because too many things must be implemented. In this paper, we propose the method using the online game GTA5 (Grand Theft Auto5), as a groundwork for autonomous vehicle simulation. As GTA5 has a variety of well-implemented objects, people, and roads, it can be considered a suitable tool for simulation. By using OpenCV (Open source computer vision) to capture the GTA5 game screen and analyzing images with YOLO (You Only Look Once) and TensorFlow based on Python, we can build a quite accurate object recognition system. This can lead to writing of algorithms for object avoidance and lane recognition. Once these algorithms have been completed, vehicles in GTA5 can be controlled through codes composed of the basic functions of autonomous driving, such as collision avoidance and lane-departure prevention. In addition, the algorithm tested with GTA5 has been implemented with a programmable RC car (Radio control car), DonkeyCar, to increase reliability. By testing those algorithms, we can ensure that the algorithms can be conducted in real time and they cost low power and low memory size. Therefore, we have found a way to approach digital twin technology one step more easily.

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Section: Self-driving Algorithm Implemented By Donkeycarmentioning

confidence: 99%

Virtualization of Self-Driving Algorithms by Interoperating Embedded Controllers on a Game Engine for a Digital Twining Autonomous Vehicle

Yun

Park

2021

Electronics

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Reactive Control Algorithm for F1TENTH Autonomous Vehicles in Unknown Dynamic Environments

Morys - Magiera,

Lis,

Pudlo

et al. 2023

Lecture Notes in Networks and Systems

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Model vs system level testing of autonomous driving systems: a replication and extension study

2023

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Offline model-level testing of autonomous driving software is much cheaper, faster, and diversified than in-field, online system-level testing. Hence, researchers have compared empirically model-level vs system-level testing using driving simulators. They reported the general usefulness of simulators at reproducing the same conditions experienced in-field, but also some inadequacy of model-level testing at exposing failures that are observable only in online mode. In this work, we replicate the reference study on model vs system-level testing of autonomous vehicles while acknowledging several assumptions that we had reconsidered. These assumptions are related to several threats to validity affecting the original study that motivated additional analysis and the development of techniques to mitigate them. Moreover, we also extend the replicated study by evaluating the original findings when considering a physical, radio-controlled autonomous vehicle. Our results show that simulator-based testing of autonomous driving systems yields predictions that are close to the ones of real-world datasets when using neural-based translation to mitigate the reality gap induced by the simulation platform. On the other hand, model-level testing failures are in line with those experienced at the system level, both in simulated and physical environments, when considering the pre-failure site, similar-looking images, and accurate labels.

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Optimizing Deep-Neural-Network-Driven Autonomous Race Car Using Image Scaling

Cited by 6 publications

References 10 publications

Virtualization of Self-Driving Algorithms by Interoperating Embedded Controllers on a Game Engine for a Digital Twining Autonomous Vehicle

Virtualization of Self-Driving Algorithms by Interoperating Embedded Controllers on a Game Engine for a Digital Twining Autonomous Vehicle

Reactive Control Algorithm for F1TENTH Autonomous Vehicles in Unknown Dynamic Environments

Model vs system level testing of autonomous driving systems: a replication and extension study

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