Open Access
Issue
ITM Web Conf.
Volume 57, 2023
Fifth International Conference on Advances in Electrical and Computer Technologies 2023 (ICAECT 2023)
Article Number 01002
Number of page(s) 18
Section Software Engineering & Information Technology
DOI https://doi.org/10.1051/itmconf/20235701002
Published online 10 November 2023
  1. P. Scheffe, T. M. Henneken, M. Kloock and B. Alrifaee, “Sequential Convex Programming Methods for Real-time Optimal Trajectory Planning in Autonomous Vehicle Racing, ” in IEEE Transactions on Intelligent Vehicles, doi: 10.1109/TIV.2022.3168130. [Google Scholar]
  2. K. Samal, M. Wolf and S. Mukhopadhyay, “Closed-loop Approach to Perception in Autonomous System, ” 2021 Design, Automation Test in Europe Conference Exhibition (DATE), pp. 463-468, (2021), doi: 10.23919/DATE51398.2021.9474243. [Google Scholar]
  3. F. Huizong, S. Bowang, F. Mingchi and G. Xiaoqian, “3D object detection and state estimation method based on stereo vision and LIDAR fusion, ” 2021 China Automation Congress (CAC), pp. 3125-3130, (2021), doi: 10.1109/CAC53003.2021.9727940. [Google Scholar]
  4. T. Wen et al., “Roadside HD Map Object Reconstruction Using Monocular Camera, ” in IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 7722-7729, July (2022), doi: 10.1109/LRA.2022.3185367. [CrossRef] [MathSciNet] [Google Scholar]
  5. S. Mandal, S. Biswas, V. E. Balas, R. N. Shaw and A. Ghosh, “Motion Prediction for Autonomous Vehicles from Lyft Dataset using Deep Learning, ” 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), pp. 768-773, (2020), doi: 10.1109/ICCCA49541.2020.9250790. [Google Scholar]
  6. H. Wang, P. Cai, Y. Sun, L. Wang and M. Liu, “Learning Interpretable End-to-End Vision-Based Motion Planning for Autonomous Driving with Optical Flow Distillation, ” 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 13731-13737, (2021), doi: 10.1109/ICRA48506.2021.9561334. [Google Scholar]
  7. Dominique Paret; Hassina Rebaine; B. A. Engel, “The Buzz about Autonomous and Connected Vehicles, ” in Autonomous and Connected Vehicles: Network Architectures from Legacy Networks to Automotive Ethernet, Wiley, pp.3-22, (2022), doi: 10.1002/9781119816140.ch1. [Google Scholar]
  8. X. Chen, J. Xue and S. Pang, “Sparse Semantic Map-Based Monocular Localization in Traffic Scenes Using Learned 2D-3D Point-Line Correspondences, ” in IEEE Robotics and Automation Letters, (2022), doi: 10.1109/LRA.2022.3207800. [Google Scholar]
  9. L. Dai, Z. Zhang and H. Xu, “Roadside Edge Sensed and Fused Three-dimensional Localization using Camera and LiDAR, ” 2022 7th International Conference on Image, Vision and Computing (ICIVC), pp. 762-766 (2022), doi: 10.1109/ICIVC55077.2022.9886834. [Google Scholar]
  10. Y. Yoon, C. Kim, J. Lee and K. Yi, “Interaction-Aware Probabilistic Trajectory Prediction of Cut-In Vehicles Using Gaussian Process for Proactive Control of Autonomous Vehicles, ” in IEEE Access, vol. 9, pp. 63440-63455, (2021), doi: 10.1109/ACCESS.2021.3075677. [CrossRef] [Google Scholar]
  11. Y. Jeong and K. Yi, “Target Vehicle Motion Prediction-Based Motion Planning Framework for Autonomous Driving in Uncontrolled Intersections, ” in IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 1, pp. 168-177, Jan. (2021), doi: 10.1109/TITS.2019.2955721. [CrossRef] [Google Scholar]
  12. H. Chae and K. Yi, “Virtual Target-Based Overtaking Decision, Motion Planning, and Control of Autonomous Vehicles, ” in IEEE Access, vol. 8, pp. 51363-51376, (2020), doi: 10.1109/ACCESS.2020.2980391. [CrossRef] [Google Scholar]
  13. M. Morsali, E. Frisk and J. Åslund, “Spatio-Temporal Planning in Multi-Vehicle Scenarios for Autonomous Vehicle Using Support Vector Machines, ” in IEEE Transactions on Intelligent Vehicles, vol. 6, no. 4, pp. 611-621, Dec. (2021), doi: 10.1109/TIV.2020.3042087. [CrossRef] [Google Scholar]
  14. Y. Huang et al., “A Motion Planning and Tracking Framework for Autonomous Vehicles Based on Artificial Potential Field Elaborated Resistance Network Approach, ” in IEEE Transactions on Industrial Electronics, vol. 67, no. 2, pp. 1376-1386, Feb. (2020), doi: 10.1109/TIE.2019.2898599. [CrossRef] [Google Scholar]
  15. S. Aradi, “Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles, ” in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 2, pp. 740-759, Feb. (2022), doi: 10.1109/TITS.2020.3024655. [CrossRef] [Google Scholar]
  16. K. Zhang, H. Zhang, W. Xue and R. Zhang, “A Robust Control Scheme for Autonomous Vehicles Path Tracking under Unreliable Communication, ” 2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS), pp. 1413-1418, (2022), doi: 10.1109/DDCLS55054.2022.9858512. [Google Scholar]
  17. R. C. Shit and S. Sharma, “Localization for Autonomous Vehicle: Analysis of Importance of IoT Network Localization for Autonomous Vehicle Applications, ” 2018 International Conference on Applied Electromagnetics, Signal Processing and Communication (AESPC), pp. 1-6, (2022), doi: 10.1109/AESPC44649.2018.9033329. [Google Scholar]
  18. Badue, C., Guidolini, R., Carneiro, R., Azevedo, P., Cardoso, V., Forechi, A., Jesus, L., Berriel, R., Paixão, T., Mutz, F., de Paula Veronese, L., Oliveira-Santos, T. and De Souza, A., Self-driving cars: A survey. Expert Systems with Applications, 165, p.113816 (2021). [CrossRef] [Google Scholar]
  19. L. Dekker, “Industrial-Scale Autonomous Vehicle Path Following by Feedback Linearized Iterative Learning Control, ”. Queen’s University, Canada, (2018). [Google Scholar]
  20. X. Li, Z. Sun, D. Cao, Z. He, and Q. Zhu, “Real-time trajectory planning for autonomous urban driving: framework, algorithms, and verifications, ” IEEE/ASME Trans. Mechatronics. vol. 21, no. 2, pp. 740-753, (2016). [CrossRef] [Google Scholar]
  21. “Welcome to L5Kit!, ” woven-planet.github.io. https://woven-planet.github.io/l5kit/ [Google Scholar]
  22. V. E. Balas, N. Shaw, and A. Ghosh, Ed., “Motion Prediction for Autonomous Vehicles from Lyft Dataset using Deep Learning Sampurna Mandal, Swagatam Biswas, ” in 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) Galgotias University, Greater Noida, UP, India, (2020). [Google Scholar]
  23. M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional Neural Networks, ” arXiv [cs.LG], (2019). [Google Scholar]

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