Study of ROS-Based Autonomous Vehicles in Snow-Covered Roads by Means of Behavioral Cloning Using 3DCoAutoSim

Liu, Yuzhou; Morales-Alvarez, Walter; Novotny, Georg; Olaverri-Monreal, Cristina

doi:10.1007/978-3-031-04829-6_19

Cited by 1 publication

(3 citation statements)

References 17 publications

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“…We used the Unity3D based 3DCoAutoSim [42] platform to simulate the environment with snowfall, friction, and car tracks on the snow. The test ground setup used in this work is based on [10], which is a snow covered 4-lane dual carriageway circular test track. The friction between the road surface and the wheels is directly achieved by setting the Unity WheelColliders, which will be explained in more detail later.…”

Section: Experimental Setup and Simulation Environmentmentioning

confidence: 99%

“…The four wheels were respectively con- trolled by four Unity WheelColliders, and their steering occurred in conformity with the Ackermann steering model. The control scripts set of the vehicle relied on [10], which was implemented via the RealisticCar-Control V3 [44].…”

Section: Experimental Setup and Simulation Environmentmentioning

confidence: 99%

“…The issue of estimating the VSA becomes particularly relevant under non-optimal weather conditions such as cases involving snow-covered roadways, as described in [10]. As observer-based estimators drastically rely on models of not only the vehicle but also of the tire-road interaction, the performance of these varies according to the accuracy of the model as well as the sensor systems utilized.…”

Section: Introductionmentioning

confidence: 99%

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Vehicle side-slip angle estimation under snowy conditions using machine learning

Novotny,

Liu,

Morales-Alvarez

et al. 2024

ICA

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Adverse weather conditions, such as snow-covered roads, represent a challenge for autonomous vehicle research. This is particularly challenging as it might cause misalignment between the longitudinal axis of the vehicle and the actual direction of travel. In this paper, we extend previous work in the field of autonomous vehicles on snow-covered roads and present a novel approach for side-slip angle estimation that combines perception with a hybrid artificial neural network pushing the prediction horizon beyond existing approaches. We exploited the feature extraction capabilities of convolutional neural networks and the dynamic time series relationship learning capabilities of gated recurrent units and combined them with a motion model to estimate the side-slip angle. Subsequently, we evaluated the model using the 3DCoAutoSim simulation platform, where we designed a suitable simulation environment with snowfall, friction, and car tracks in snow. The results revealed that our approach outperforms the baseline model for prediction horizons ⩾ 2 seconds. This extended prediction horizon has practical implications, by providing drivers and autonomous systems with more time to make informed decisions, thereby enhancing road safety.

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Section: Experimental Setup and Simulation Environmentmentioning

confidence: 99%

Section: Experimental Setup and Simulation Environmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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