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Publication
Adaptive Convolutional Neural Network for Predicting Steering Angle and Acceleration on Autonomous Driving Scenario
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Authors
Advisor(s)
Abstract(s)
This paper introduces a novel approach to autonomous vehicle
control using an end-to-end learning framework. While existing solutions
in the field often rely on computationally expensive architectures,
our proposed lightweight model achieves comparable efficiency. We leveraged
the Car Learning to Act (CARLA) simulator to generate training
data by recording sensor inputs and corresponding control actions during
simulated driving. The Mean Squared Error (MSE) loss function served
as a performance metric during model training. Our end-to-end learning
architecture demonstrates promising results in predicting steering angle
and throttle, offering a practical and accessible solution for autonomous
driving. Results of the experiment showed that our proposed network
is ≈ 5.4 times lighter than Nvidia’s PilotNet and had a slightly lower
testing loss. We showed that our network is offering a balance between
performance and computational efficiency. By eliminating the need for
handcrafted feature engineering, our approach simplifies the control process
and reduces computational demands. Experimental evaluation on a
testing map showcases the model’s effectiveness in real-world scenarios
whilst being competitive with other existing models.
Description
Keywords
Autonomous vehicles End-to-end learning CARLA simulator Deep learning Convolutional neural network
Pedagogical Context
Citation
Vasiljević, Ive; Musić, Josip; Mendes, João; Lima, José (2024). Adaptive Convolutional Neural Network for Predicting Steering Angle and Acceleration on Autonomous Driving Scenario. In 3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023). Cham: Springer Nature, Vol. 2, p. 132–147. ISBN 978-3-031-53035-7
Publisher
Springer Nature