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Implementation of autonomous driving using Ensemble-M in simulated environment

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Abstract

Making autonomous driving a safe, feasible, and better alternative is one of the core problems the world is facing today. The horizon of the applications of AI and deep learning has changed the perspective of the human mind. Initially, what used to be thought of as the subtle impossible task is applicable today, and that too in the feasibly efficient way. Computer vision tasks powered with highly tuned CNNs are outperforming humans in many fields. Introductory implementations of the autonomous vehicle were merely achieved using raw image processing, and hard programmed rule-based logic systems along with machine/deep learning were used as secondary objective handlers. With the autonomous driving method proposed by Nvidia, the usability of CNNs is more adequate, adaptable, and applicable. In this paper, we propose the ensemble implementation of CNN-based regression models for autonomous driving. We have taken simulator generated driving view image dataset along with a mapped file of steering angle in radians. After applying image pre-processing and augmentation, we have used two CNN models along with their ensemble and compared their performance to minimize the risks of unsafe driving. We have compared Nvidia proposed CNN, MobileNet-V2 as regression model and Ensemble-M results for comparison of their respective performance, MSE scores and compute time to process. In result analysis, the MobileNet-V2 model performs better in densely featured roads and the Nvidia model performs better in sparsely featured roads, whereas Ensemble-M normalizes the performance of both models and efficiently results in the least MSE score (0.0201) with the highest computation time utilization making autonomous driving better, safer alternative.

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References

  • Alexeev VF, Staravoitau AI, Piskun GA, Likhacheuski DV (2018) End to end learning for a driving simulator. Дoклaды Бeлopyccкoгo гocyдapcтвeннoгo yнивepcитeтa инфopмaтики и paдиoэлeктpoники, 2(112)

  • Ali Alheeti KM, Gruebler A, McDonald-Maier K (2016) Intelligent intrusion detection of grey hole and rushing attacks in self-driving vehicular networks. Computers 5(3):16

    Article  Google Scholar 

  • Babiker MA, Elawad MA, Ahmed AH (2019) convolutional neural network for a self-driving car in a virtual environment. In: 2019 International conference on computer, control, electrical, and electronics engineering (ICCCEEE).pp 1–6. IEEE

  • Badue C, Guidolini R, Carneiro RV, Azevedo P, Cardoso VB, Forechi A, De Souza AF (2020) Self-driving cars: a survey. Exp Syst Appl, p 113816

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

  • Blyth PL, Mladenovic MN, Nardi BA, Ekbia HR, Su NM (2016) Expanding the design horizon for self-driving vehicles: Distributing benefits and burdens. IEEE Technol Soc Mag 35(3):44–49

    Article  Google Scholar 

  • Bojarski M, Del Testa D, Dworakowski D, Firner B, Flepp B, Goyal P, Jackel LD, Monfort M, Muller U, Zhang J, Zieba K (2016) End to end learning for self-driving cars. arXiv preprint http://arxiv.org/abs/1604.07316

  • Brooks R (2017) The big problem with self-driving cars is people. IEEE spectrum: technology, engineering, and science News

  • Chen SC (2019) Multimedia for autonomous driving. IEEE Multimedia 26(3):5–8

    Article  Google Scholar 

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

  • Chen C, Demir E, Huang Y, Qiu R (2021) The adoption of self-driving delivery robots in last mile logistics. Transp Res Part E Logist Transp Rev 146:102214

    Article  Google Scholar 

  • Chishti SO, Riaz S, BilalZaib M, Nauman M (2018) Self-driving cars using CNN and Q-learning. In: 2018 IEEE 21st International Multi-Topic Conference (INMIC). pp 1–7. IEEE

  • Chopra R, Roy SS (2020) End-to-end reinforcement learning for self-driving car. Advanced computing and intelligent engineering. Springer, Singapore, pp 53–61

    Chapter  Google Scholar 

  • Da Conceicao MA, Degadwala S (2020) Steering angle prediction based on road direction using convolution neural network (CNN)

  • Daily M, Medasani S, Behringer R, Trivedi M (2017) Self-driving cars. Computer 50(12):18–23

    Article  Google Scholar 

  • del Egio J, Bergasa LM, Romera E, Huélamo CG, Araluce J, Barea R (2018) Self-driving a car in simulation through a CNN. Workshop of physical agents. Springer, Cham, pp 31–43

    Google Scholar 

  • Drews P, Williams G, Goldfain B, Theodorou EA, Rehg JM (2017) Aggressive deep driving: model predictive control with a cnn cost model. arXiv preprint http://arxiv.org/abs/1707.05303

  • Faas SM, Baumann M (2019) Light-based external human machine interface: color evaluation for self-driving vehicle and pedestrian interaction. In: Proceedings of the human factors and ergonomics society annual meeting. vol 63, no 1, pp 1232–1236. SAGE Publications, Los Angeles

  • Garimella G, Funke J, Wang C, Kobilarov M (2017) Neural network modeling for steering control of an autonomous vehicle. In: 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS). pp 2609–2615. IEEE

  • Goodall NJ (2016) Can you program ethics into a self-driving car? IEEE Spectr 53(6):28–58

    Article  Google Scholar 

  • Hossain S, Fayjie AR, Doukhi O, Lee DJ (2018) CAIAS simulator: self-driving vehicle simulator for AI research. In: International conference on intelligent computing and optimization. pp 187–195. Springer, Cham

  • Hsieh W (2017) First order driving simulator (Doctoral dissertation, Master’s thesis, EECS Department, University of California, Berkeley)

  • Jhung J, Bae I, Moon J, Kim T, Kim J, Kim S (2018)End-to-end steering controller with cnn-based closed-loop feedback for autonomous vehicles. In: 2018 IEEE intelligent vehicles symposium (IV). pp 617–622. IEEE

  • Lehnert L, Tellex S, Littman ML (2017) Advantages and limitations of using successor features for transfer in reinforcement learning. arXiv preprint http://arxiv.org/abs/1708.00102.

  • Mori K, Fukui H, Murase T, Hirakawa T, Yamashita T, Fujiyoshi H (2019) Visual explanation by attention branch network for end-to-end learning-based self-driving. In: 2019 IEEE intelligent vehicles symposium (IV). pp 1577–1582. IEEE

  • Ndikumana A, Tran NH, Kim KT, Hong CS (2020) Deep learning based caching for self-driving cars in multi-access edge computing. IEEE Trans Intell Transp Syst

  • Ni J, Chen Y, Chen Y, Zhu J, Ali D, Cao W (2020) A survey on theories and applications for self-driving cars based on deep learning methods. Appl Sci 10(8):2749

    Article  Google Scholar 

  • Ohn-Bar E, Trivedi MM (2016) Looking at humans in the age of self-driving and highly automated vehicles. IEEE Trans Intell Veh 1(1):90–104

    Article  Google Scholar 

  • Olaverri-Monreal C (2020) Promoting trust in self-driving vehicles. Nature Electronics 3(6):292–294

    Article  Google Scholar 

  • Oluwatosin HS (2014) Client-server model. IOSRJ. Comput Eng 16(1):2278–8727

    Google Scholar 

  • Ouyang Z, Niu J, Liu Y, Guizani M (2019) Deep CNN-based real-time traffic light detector for self-driving vehicles. IEEE Trans Mob Comput 19(2):300–313

    Article  Google Scholar 

  • Paden B, Čáp M, Yong SZ, Yershov D, Frazzoli E (2016) A survey of motion planning and control techniques for self-driving urban vehicles. IEEE Trans Intell Veh 1(1):33–55

    Article  Google Scholar 

  • Pearah PJ (2017) Opening the door to self-driving cars: how will this change the rules of the road. J High Tech l 18:38

    Google Scholar 

  • Qiu T, Huang Z (2019) Learning a steering decision policy for end-to-end control of autonomous vehicle. In: 2019 5th international conference on control, automation and robotics (ICCAR). pp 347–351. IEEE

  • Sattler T, Zhou Q, Pollefeys M, Leal-Taixe L (2019) Understanding the limitations of cnn-based absolute camera pose regression. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 3302–3312

  • Savelsbergh MW, Sol M (1995) The general pickup and delivery problem. Transp Sci 29(1):17–29

    Article  Google Scholar 

  • Shalamov V, Filchenkov A, Shalyto A (2016) Genetic search of pickup and delivery problem solutions for self-driving taxi routing. In: IFIP international conference on artificial intelligence applications and innovations, pp 348–355. Springer, Cham

  • Smith RG, Eckroth J (2017) Building AI applications: Yesterday, today, and tomorrow. AI Mag 38(1):6–22

    Google Scholar 

  • Smolyakov MV, Frolov AI, Volkov VN, Stelmashchuk IV (2018) Self-driving car steering angle prediction based on deep neural network an example of CarND udacity simulator. In: 2018 IEEE 12th international conference on application of information and communication technologies (AICT). pp 1–5. IEEE

  • Urmson C (2008) Self-driving cars and the urban challenge. IEEE Intell Syst 23(2):66–68

    Article  Google Scholar 

  • Valiente R, Zaman M, Ozer S, Fallah YP (2019) Controlling steering angle for cooperative self-driving vehicles utilizing cnn and lstm-based deep networks. In: 2019 IEEE intelligent vehicles symposium (IV). pp 2423–2428. IEEE

  • Viswanath P, Nagori S, Mody M, Mathew M, Swami P (2018) End to end learning based self-driving using JacintoNet. In 2018 IEEE 8th International Conference on Consumer Electronics-Berlin (ICCE-Berlin). pp 1–4. IEEE

  • Wang Y, Liu D, Jeon H, Chu Z, Matson ET (2019) End-to-end learning approach for autonomous driving: a convolutional neural network model. In ICAART (2), pp 833–839.

  • Wang YE, Wei GY, Brooks D (2019) Benchmarking tpu, gpu, and cpu platforms for deep learning. arXiv preprint http://arxiv.org/abs/1907.10701.

  • West DM (2016) Moving forward: self-driving vehicles in China, Europe, Japan, Korea, and the United States. Center for Technology Innovation at Brookings, Washington

  • Xia H, Yang H (2018) Is last-mile delivery a’killer app’for self-driving vehicles? Commun ACM 61(11):70–75

    Article  Google Scholar 

  • Zeng W, Wang S, Liao R, Chen Y, Yang B, Urtasun R (2020) Dsdnet: deep structured self-driving network. In European conference on computer vision, pp 156–172. Springer, Cham

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Authors and Affiliations

  1. Department of CSE, Chandigarh University, Punjab, India

    Meenu Gupta, Vikalp Upadhyay & Prince Kumar

  2. Department of Artificial Intelligence Engineering, Research Centre for AI and IoT, Near East University, Nicosia, Mersin 10, Turkey

    Fadi Al-Turjman

Authors
  1. Meenu Gupta
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  2. Vikalp Upadhyay
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  3. Prince Kumar
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  4. Fadi Al-Turjman
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Contributions

All authors conceived and designed the study. MG and VU conducted the experiments, analyzed the data, and wrote the paper. All authors contributed to manuscript revision. All authors approved the final version of the manuscript and agree to be held accountable for the content therein.

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Correspondence to Meenu Gupta.

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Communicated by Vicente Garcia Diaz.

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Gupta, M., Upadhyay, V., Kumar, P. et al. Implementation of autonomous driving using Ensemble-M in simulated environment. Soft Comput 25, 12429–12438 (2021). https://doi.org/10.1007/s00500-021-05954-4

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