A Combination of Feedback Control and Vision-Based Deep Learning Mechanism for Guiding Self-Driving Cars

Lin, Wen-Yen; Hsu, Wang-Hsin; Chiang, Yi-Yuan

doi:10.1109/aivr.2018.00062

Cited by 5 publications

(2 citation statements)

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“…CNNs and Recurrent Neural Networks (RNNs) can be used to train autonomous driving. In the study of Lin et al [5], they proposed a CNN model to achieve road tracking. In the study of Viktor Rausch et al [15], they proposed a neural network model combining CNN and LSTM to achieve road tracking.…”

Section: Neural Network For Autonomous Drivingmentioning

confidence: 99%

“…The core of perception and decision-making technology for autonomous driving is artificial intelligence (AI) algorithms. For example, Lin et al [5] proposed a ten-layer convolutional neural network (CNN, Convolutional Neural Network) model to achieve autonomous driving. In the study of Valiente et al [6], they proposed a twelve-layer CNN-LSTM model to achieve autonomous driving.…”

Section: Introductionmentioning

confidence: 99%

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MFPE: A Loss Function based on Multi-task Autonomous Driving

Li¹,

Jian²

2022

ECTI-CIT Transactions

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Road tracking, traffic sign recognition, obstacle avoidance, and real-time acceleration and deceleration are some critical sub-tasks in autonomous driving. This research proposed to use a single-sensor (camera) based intelligent driving platform to achieve multi-task (four subtasks) autonomous driving. We adjusted the function combinations and hyperparameters of the model to improve the model training and model testing performance. The experiments showed that the existing function combinations could not significantly improve the autonomous driving performance, and the loss function had a significant impact on the autonomous driving performance of the model. Therefore, we designed a novel loss function (MFPE) based on multi-task autonomous driving. The models with the MFPE loss function outperformed the original and existing models in model training and actual multi-task autonomous driving performance. Meanwhile, the model with the MFPE loss function achieved multi-task autonomous driving under different lighting conditions, untrained routes, and different static obstacles, which indicates that the MFPE loss function enhances the robustness of the model. In addition, the speed of the intelligent driving platform can reach up to 5.4 Km/h.

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Section: Neural Network For Autonomous Drivingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MFPE: A Loss Function based on Multi-task Autonomous Driving

Li¹,

Jian²

2022

ECTI-CIT Transactions

View full text Add to dashboard Cite

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A novel neural network architecture and cross-model transfer learning for multi-task autonomous driving

Li,

2024

DTA

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PurposeThe purpose of this research is to achieve multi-task autonomous driving by adjusting the network architecture of the model. Meanwhile, after achieving multi-task autonomous driving, the authors found that the trained neural network model performs poorly in untrained scenarios. Therefore, the authors proposed to improve the transfer efficiency of the model for new scenarios through transfer learning.Design/methodology/approachFirst, the authors achieved multi-task autonomous driving by training a model combining convolutional neural network and different structured long short-term memory (LSTM) layers. Second, the authors achieved fast transfer of neural network models in new scenarios by cross-model transfer learning. Finally, the authors combined data collection and data labeling to improve the efficiency of deep learning. Furthermore, the authors verified that the model has good robustness through light and shadow test.FindingsThis research achieved road tracking, real-time acceleration–deceleration, obstacle avoidance and left/right sign recognition. The model proposed by the authors (UniBiCLSTM) outperforms the existing models tested with model cars in terms of autonomous driving performance. Furthermore, the CMTL-UniBiCL-RL model trained by the authors through cross-model transfer learning improves the efficiency of model adaptation to new scenarios. Meanwhile, this research proposed an automatic data annotation method, which can save 1/4 of the time for deep learning.Originality/valueThis research provided novel solutions in the achievement of multi-task autonomous driving and neural network model scenario for transfer learning. The experiment was achieved on a single camera with an embedded chip and a scale model car, which is expected to simplify the hardware for autonomous driving.

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