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

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Youwei Li
Jian Qu


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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How to Cite
Y. Li and J. Qu, “MFPE: A Loss Function based on Multi-task Autonomous Driving”, ECTI-CIT Transactions, vol. 16, no. 4, pp. 393–409, Oct. 2022.
Research Article


J. Stilgoe, “Machine learning, social learning and the governance of self-driving cars,” Social studies of science, vol. 48, no. 1, pp. 25-56, 2018.

K. L. Lee and H. Y. Lam, “Development of Deep Learning Autonomous Car Using Raspberry Pi,” Progress in Engineering Application and Technology, vol. 2, no. 1, pp. 534-548, 2021.

X. Du, M. H. Ang, and D. Rus, “Car detection for autonomous vehicle: LIDAR and vision fusion approach through deep learning framework,” 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 749-754, 2017.

S. Ding, and J. Qu, “Smart car with road tracking and obstacle avoidance based on Resnet18CBAM,” 2022 7th International Conference on Business and Industrial Research (ICBIR), pp. 582-585, 2022.

W. Y. Lin, W. H. Hsu, and Y. Y. Chiang, “A combination of feedback control and visionbased deep learning mechanism for guiding selfdriving cars,” 2018 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), pp. 262-266, 2018.

R. Valiente, M. Zaman, S. Ozer, and Y. P. Fallah, “Controlling steering angle for cooperative self-driving vehicles utilizing cnn and lstm-based deep networks,” 2019 IEEE intelligent vehicles symposium (IV), pp.2423-2428, 2019.

M. G. Bechtel, E. McEllhiney, M. Kim, and H. Yun, “Deeppicar: A low-cost deep neural network-based autonomous car,” 2018 IEEE 24th international conference on embedded and real-time computing systems and applications (RTCSA), pp. 11-21, 2018.

T. D. Do, M. T. Duong, Q. V. Dang, and M. H. Le, “Real-time self-driving car navigation using deep neural network,” 2018 4th International Conference on Green Technology and Sustainable Development (GTSD), pp. 7-12, 2018.

U. Karni, S. S. Ramachandran, K. Sivaraman, and A. K. Veeraraghavan, “Development of autonomous downscaled model car using neural networks and machine learning,” 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), pp. 1089- 1094, 2019.

Y. Li and J. Qu, “Intelligent Road Tracking and Real-time Acceleration-deceleration for Autonomous Driving Using Modified Convolutional Neural Networks,” Current Applied Science and Technology, vol. 22, no. 6, pp.1-26, 2022.

V. Mnih, et al., “Human-level control through deep reinforcement learning,” Nature, vol. 518, no. 7540, pp. 529-533, 2015.

A. Boloor, K. Garimella, X. He, C. Gill, Y. Vorobeychik, and X. Zhang, “Attacking vision- based perception in end-to-end autonomous driving models,” Journal of Systems Architecture, vol. 110, no. 101766, 2020.

Y. Bae, E. Gomez, A. Haywood, J. Lazo, P. Whitson, and Y. Wang, “Prototyping a System of Cost-Effective Autonomous Guided Vehicles,” Proceedings of the Annual General Donald R. Keith Memorial Conference, pp. 138-143, 2021.

S. Yuenyong and J. Qu, “Generating synthetic training images for deep reinforcement learning of a mobile robot,” Journal of Intelligent Informatics and Smart Technology, vol. 2, pp. 16-20, 2017.

V. Rausch, A. Hansen, E. Solowjow, C. Liu, E. Kreuzer, and J. K. Hedrick, “Learning a deep neural net policy for end-to-end control of autonomous vehicles,” 2017 American Control Conference (ACC), pp. 4914-4919, 2017.

Y. Xiao, F. Codevilla, A. Gurram, O. Urfalioglu, and A. M. L ́opez, “Multimodal end-to-end autonomous driving,” IEEE Transactions on Intelligent Transportation Systems, pp. 1-11, 2020.

Z. Chen and X. Huang, “End-to-end learning for lane keeping of self-driving cars,” 2017 IEEE Intelligent Vehicles Symposium (IV), pp. 1856- 1860, 2017.

A. Seth, A. James, and S. C. Mukhopadhyay, “1/10th scale autonomous vehicle based on convolutional neural network,” International Journal on Smart Sensing and Intelligent Systems, vol. 13, no. 1, pp. 1-17, 2020.

D. Molchanov, A. Ashukha, and D. Vetrov, “Variational dropout sparsifies deep neural networks,” International Conference on Machine Learning, pp. 2498-2507, 2017.

S. Phiphitphatphaisit and O. Surinta, “Deep feature extraction technique based on Conv1D and LSTM network for food image recognition,” Engineering and Applied Science Research, vol. 48, no. 5, pp. 581-592, 2021.

S. M. J. Jalali, P. M. Kebria, A. Khosravi, K. Saleh, D. Nahavandi, and S. Nahavandi, “Optimal autonomous driving through deep imitation learning and neuroevolution,” 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 1215-1220, 2019.

Y. H. Ko, K. J. Kim, and C. H. Jun, “A new loss function-based method for multiresponse optimization,” Journal of Quality Technology, vol. 37, no. 1, pp. 50-59, 2005.

J. Qi, J. Du, S. M. Siniscalchi, X. Ma, and C. H. Lee, “On mean absolute error for deep neural network based vector-to-vector regression,” IEEE Signal Processing Letters, vol. 27, pp. 1485-1489, 2020.

J. M. Martin-Donas, A. M. Gomez, J. A. Gonzalez, and A. M. Peinado, “A deep learning loss function based on the perceptual evaluation of the speech quality,” IEEE Signal processing letters, vol. 25, no. 11, pp. 1680-1684, 2018.

S. Fatimah, “Artificial neural network for modelling the removal of pollutants: A review,” Engineering and Applied Science Research, vol. 47, no. 3, pp. 339-347, 2020.

A. D. Rasamoelina, F. Adjailia, and P. Sinˇc ́ak, “A review of activation function for artificial neural network,” 2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI), pp. 281-286, 2020.

J. Schmidt-Hieber, “Nonparametric regression using deep neural networks with ReLU activation function,” The Annals of Statistics, vol. 48, no. 4, pp. 1875-1897, 2020.

P. Bohra, J. Campos, H. Gupta, S. Aziznejad, and M. Unser, “Learning activation functions in deep (spline) neural networks,” IEEE Open Journal of Signal Processing, vol. 1, pp. 295-309, 2020.

M. Carvalho, B. Le Saux, P. Trouv ́e-Peloux, A. Almansa, and F. Champagnat, “On regression losses for deep depth estimation,” 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 2915-2919, 2018.

X. Zhu, H. I. Suk, and D. Shen, “A novel matrixsimilarity based loss function for joint regression and classification in AD diagnosis,” NeuroImage, vol. 100, pp. 91-105, 2014.

Z. Allen-Zhu, Y. Li, and Z. Song, “A convergence theory for deep learning via overparameterization,” International Conference on Machine Learning (PMLR), vol. 97, pp. 242-252, 2019.

D. O. Melinte, and L. Vladareanu, “Facial expressions recognition for human–robot interaction using deep convolutional neural networks with rectified Adam optimizer,” Sensors, vol. 20, no. 8, pp. 2393, 2020.

J. Yang, and G. Yang, “Modified convolutional neural network based on dropout and the stochastic gradient descent optimizer,” Algorithms, vol. 11, no. 3, pp. 28, 2018.

A. M. Taqi, A. Awad, F. Al-Azzo, and M. Mi- lanova, “The impact of multi-optimizers and data augmentation on TensorFlow convolutional neural network performance,” 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), pp. 140-145, 2018.