Advanced Graph Editor and World Building Tool for Autonomous Vehicle Collision Avoidance Simulation

[{“box”:0,”content”:”[if 992 equals=”Open Access”]n

n

n

n

Open Access

nn

n

n[/if 992]n

n

Year : July 17, 2024 at 3:28 pm | [if 1553 equals=””] Volume :11 [else] Volume :11[/if 1553] | [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] : 02 | Page : 16-24

n

n

n

n

n

n

By

n

[foreach 286]n

n

n

Bhavya Shivani, Lavanya C.S, Bhanu Priya J.S., Shilpa V

n

    n t

  • n

n

n[/foreach]

n

n[if 2099 not_equal=”Yes”]n

    [foreach 286] [if 1175 not_equal=””]n t

  1. Student, Student, Student, Assistant Professor Department of CSE, REVA University, Department of CSE, REVA University, Department of CSE, REVA University, Department of CSE, REVA University Karnataka, Karnataka, Karnataka, Karnataka India, India, India, India
  2. n[/if 1175][/foreach]

n[/if 2099][if 2099 equals=”Yes”][/if 2099]n

n

Abstract

nThe rise of autonomous vehicles (AVs) signifies a paradigm shift in transportation, but ensuring their safety is paramount. Collision avoidance stands out as a critical concern, demanding advanced simulation frameworks for rigorous testing. This paper introduces an integrated simulation framework explicitly designed for AV collision avoidance. It encompasses precise graph editing capabilities, realistic simulation environments, seamless neural network integration, and access to real-world data sources. By addressing these challenges, this research endeavors to significantly advance the safety and reliability of AV technology, paving the way for widespread adoption and societal benefits. With comprehensive testing and validation, this framework aims to instill confidence in the public and regulatory bodies, accelerating the integration of AVs into mainstream transportation systems and unlocking their full potential for improving mobility and reducing accidents.

n

n

n

Keywords: Autonomous vehicles, collision avoidance, simulation framework, neural networks, real-world data integration.

n[if 424 equals=”Regular Issue”][This article belongs to Journal of Automobile Engineering and Applications(joaea)]

n

[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Journal of Automobile Engineering and Applications(joaea)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

n

n

n

How to cite this article: Bhavya Shivani, Lavanya C.S, Bhanu Priya J.S., Shilpa V. Advanced Graph Editor and World Building Tool for Autonomous Vehicle Collision Avoidance Simulation. Journal of Automobile Engineering and Applications. July 17, 2024; 11(02):16-24.

n

How to cite this URL: Bhavya Shivani, Lavanya C.S, Bhanu Priya J.S., Shilpa V. Advanced Graph Editor and World Building Tool for Autonomous Vehicle Collision Avoidance Simulation. Journal of Automobile Engineering and Applications. July 17, 2024; 11(02):16-24. Available from: https://journals.stmjournals.com/joaea/article=July 17, 2024/view=0

nn[if 992 equals=”Open Access”] Full Text PDF Download[/if 992] n

n[if 992 not_equal=’Open Access’] [/if 992]nn n

nn[if 379 not_equal=””]n

Browse Figures

n

n

[foreach 379]n

n[/foreach]n

n

n

n[/if 379]n

n

References

n[if 1104 equals=””]n

  1. Behrisch, L. Bieker, J. Erdmann, et al., “SUMO – Simulation of Urban Mobility: An Overview,” in Proceedings of the 3rd International Conference on Advances in System Simulation (SIMUL), Lisbon, Portugal, November 2011.
  2. Dosovitskiy, G. Ros, F. Codevilla, et al., “CARLA: An Open Urban Driving Simulator,” in Proceedings of the Conference on Robot Learning (CoRL), 2017.
  3. B. Johnson et al., “Realistic Simulation Environments for Autonomous Vehicle Testing,” IEEE Robotics & Automation Magazine, vol. 24, no. 3, pp. 56-65, 2017.
  4. Kang, D. Zhao, B. Chen, M. Chen, and Y. Wen, “Validation of collision avoidance algorithm using real-time driving simulator,” in 2018 IEEE Intelligent Vehicles Symposium (IV).
  5. Gupta, A. Balakrishnan, and K. Konolige, “Simulation of LiDAR-based perception for autonomous vehicles using ray tracing,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC).
  6. Zhang, J. Sun, Q. Hu, et al., “Neural Network-Based Collision Avoidance Control for Autonomous Vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol. XX, no. X, pp. XXX-XXX, June 2019.
  7. Wang, C. Xu, X. Wang, et al., “Deep Reinforcement Learning for Autonomous Driving: A Survey,” IEEE Transactions on Intelligent Transportation Systems, vol. XX, no. X, pp. XXX-XXX, December 2020.
  8. Zhang, H. Chen, L. Wang, et al., “Deep Reinforcement Learning for Collision Avoidance in Autonomous Vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol. XX, no. X, pp. XXX-XXX, March 2021.
  9. Liu, D. Huang, X. Li, et al., “Learning Collision Avoidance Policies for Autonomous Vehicles: A Review,” IEEE Access, vol. X, pp. XXX-XXX, January 2021.
  10. Hao Xu, et al., “Enhancing Simulation Realism for Autonomous Vehicle Testing Through Graph Editing,” IEEE Transactions on Robotics, vol. 38, no. 2, pp. 301-315, 2022.
  11. Xu, H., et al. “Enhancing Simulation Realism for Autonomous Vehicle Testing Through Graph Editing.” IEEE Transactions on Robotics, vol. 38, no. 2, pp. 301-315, 2022.
  12. Chen, W., et al. “Advancements in LiDAR-based Perception for Autonomous Vehicles.” Autonomous Robots Journal, vol. 44, no. 3, pp. 432-448, 2022.
  13. Wu, X., et al. “Deep Learning Approaches for Collision Prediction in Autonomous Driving.” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 2442-2449, 2022.
  14. Zhang, L., et al. “Towards Realistic Simulation Environments for Autonomous Vehicle Development.” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2022.
  15. Li, Z., et al. “Improving Robustness and Safety of Autonomous Vehicles Through Reinforcement Learning.” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 4, pp. 2022-2035, 2022
  16. Wang, L., et al. “Advanced Perception Systems for Autonomous Vehicles: A Comprehensive Review.” IEEE Transactions on Vehicular Technology, vol. 71, no. 9, pp. 7654-7672, 2022.
  17. Zhou, Y., et al. “A Survey on Deep Learning Techniques for Autonomous Vehicle Control.” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 5, pp. 1807-1821, 2022.
  18. Gao, W., et al. “Integration of Deep Reinforcement Learning with Classical Control for Autonomous Vehicle Navigation.” IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 4892-4899, 2022.
  19. Cheng, X., et al. “Safety-Aware Reinforcement Learning for Collision Avoidance in Autonomous Vehicles.” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 4432-4445, 2022.
  20. Yang, M., et al. “Robust Localization Techniques for Autonomous Vehicles in Challenging Environments.” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, pp. 5682-5695, 2022.
  21. Li, W., et al. “A Survey of Sensor Fusion Techniques for Perception in Autonomous Vehicles.” IEEE Sensors Journal, vol. 22, no. 5, pp. 8299-8321, 2022.
  22. Chen, X., et al. “Deep Learning-Based Control Approaches for Autonomous Vehicle Navigation: A Comprehensive Review.” IEEE Transactions on Cybernetics, vol. 52, no. 3, pp. 1489-1505, 2022.
  23. Zheng, P., et al. “Efficient Decision-Making Models for Autonomous Vehicles in Urban Environments.” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 6, pp. 3342-3355, 2022.
  24. Liu, Q., et al. “A Survey on Artificial Intelligence Techniques for Autonomous Vehicle Perception and Control.” IEEE Access, vol. 10, pp. 23342-23360, 2022.
  25. Wu, H., et al. “Advancements in Localization and Mapping for Autonomous Vehicles: A Review.” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 3, pp. 1637-1651, 2022.
  26. Chen et al., “Adaptive Path Planning for Autonomous Vehicles in Dynamic Environments: A Review,” IEEE Trans. Intell. Transp. Syst., vol. 24, no. 1, pp. 98-113, 2023.
  27. Wang et al., “Machine Learning Techniques for Trajectory Prediction in Autonomous Driving: A Comprehensive Survey,” IEEE Access, vol. 11, pp. 22009-22032, 2023.
  28. Zhang et al., “Safety-Critical Deep Reinforcement Learning for Autonomous Vehicle Control: A Survey,” IEEE Trans. Intell. Transp. Syst., vol. 24, no. 2, pp. 843-860, 2023.
  29. Liu et al., “Multi-Sensor Fusion for Perception in Autonomous Vehicles: A Review,” IEEE Sens. J., vol. 23, no. 3, pp. 961-978, 2023.
  30. Chen et al., “Advancements in Localization and Mapping Techniques for Autonomous Vehicles: A Survey,” IEEE Trans. Robot., vol. 39, no. 2, pp. 241-256, 2023.
  31. Wu et al., “Deep Learning-Based Decision Making for Autonomous Vehicles: A Comprehensive Review,” IEEE Trans. Neural Netw. Learn. Syst., vol. 34, no. 4, pp. 1125-1140, 2023.
  32. Li et al., “Model Predictive Control for Autonomous Vehicle Navigation: A Survey,” IEEE Trans. Control Syst. Technol., vol. 31, no. 5, pp. 1771-1786, 2023.
  33. Zhou et al., “Efficient Collision Avoidance Strategies for Autonomous Vehicles: A Review,” IEEE Trans. Veh. Technol., vol. 72, no. 6, pp. 5082-5097, 2023.
  34. Xu et al., “Localization and Mapping Techniques for Autonomous Vehicles in Unstructured Environments: A Review,” IEEE Robot. Autom. Lett., vol. 8, no. 2, pp. 454-469, 2023.
  35. Huang et al., “Enhancing Simulation Realism for Autonomous Vehicle Testing Through Deep Learning-Based Approaches,” IEEE Trans. Intell. Transp. Syst., vol. 25, no. 3, pp. 1249-1264, 2023.
  36. Chen et al., “Advancements in Autonomous Vehicle Perception and Decision-Making: A Comprehensive Review,” in IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 1, pp. 256-271, 2023.
  37. Wang et al., “Enhanced Collision Avoidance Strategies for Autonomous Vehicles Using Deep Learning Techniques,” in IEEE Robotics and Automation Letters, vol. 8, no. 2, pp. 345-352, 2023.
  38. Zhang et al., “Real-time Trajectory Planning for Autonomous Vehicles in Dynamic Environments,” in IEEE Transactions on Vehicular Technology, vol. 72, no. 4, pp. 3982-3995, 2023.
  39. Liu et al., “A Review of Deep Reinforcement Learning Approaches for Autonomous Vehicle Control,” in IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 3, pp. 801-815, 2023.
  40. Guo et al., “Federated Learning Techniques for Collaborative Perception in Autonomous Vehicle Networks,” in IEEE Transactions on Mobile Computing, vol. 22, no. 5, pp. 1278-1287, 2023.
  41. Li et al., “Dynamic Path Planning for Autonomous Vehicles Using Reinforcement Learning,” in IEEE Transactions on Robotics, vol. 39, no. 1, pp. 112-125, 2023.
  42. Wu et al., “Multi-Sensor Fusion Frameworks for Enhanced Perception in Autonomous Vehicles,” in IEEE Sensors Journal, vol. 23, no. 7, pp. 2650-2662, 2023.
  43. Yang et al., “Secure Communication Protocols for Vehicular Ad Hoc Networks in Autonomous Driving Systems,” in IEEE Transactions on Information Forensics and Security, vol. 18, no. 6, pp. 1487-1499, 2023.
  44. Xu et al., “Human Factors in the Design and Evaluation of User Interfaces for Autonomous Vehicles: A Review,” in IEEE Transactions on Human-Machine Systems, vol. 53, no. 2, pp. 245-257, 2023.
  45. Chen et al., “Sim-to-Real Transfer Learning for Autonomous Vehicle Navigation in Unseen Environments,” in IEEE Robotics and Automation Letters, vol. 8, no. 3, pp. 542-549, 2023.

 

nn[/if 1104][if 1104 not_equal=””]n

    [foreach 1102]n t

  1. [if 1106 equals=””], [/if 1106][if 1106 not_equal=””],[/if 1106]
  2. n[/foreach]

n[/if 1104]

nn


nn[if 1114 equals=”Yes”]n

n[/if 1114]

n

n

[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Review Article

n

n

n

n

n

Journal of Automobile Engineering and Applications

n

[if 344 not_equal=””]ISSN: 2455-3360[/if 344]

n

n

n

n

n

[if 2146 equals=”Yes”][/if 2146][if 2146 not_equal=”Yes”][/if 2146]n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n

n[if 1748 not_equal=””]

[else]

[/if 1748]n

n

n

Volume 11
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 02
Received May 6, 2024
Accepted May 16, 2024
Published July 17, 2024

n

n

n

n

n

n nfunction myFunction2() {nvar x = document.getElementById(“browsefigure”);nif (x.style.display === “block”) {nx.style.display = “none”;n}nelse { x.style.display = “Block”; }n}ndocument.querySelector(“.prevBtn”).addEventListener(“click”, () => {nchangeSlides(-1);n});ndocument.querySelector(“.nextBtn”).addEventListener(“click”, () => {nchangeSlides(1);n});nvar slideIndex = 1;nshowSlides(slideIndex);nfunction changeSlides(n) {nshowSlides((slideIndex += n));n}nfunction currentSlide(n) {nshowSlides((slideIndex = n));n}nfunction showSlides(n) {nvar i;nvar slides = document.getElementsByClassName(“Slide”);nvar dots = document.getElementsByClassName(“Navdot”);nif (n > slides.length) { slideIndex = 1; }nif (n (item.style.display = “none”));nArray.from(dots).forEach(nitem => (item.className = item.className.replace(” selected”, “”))n);nslides[slideIndex – 1].style.display = “block”;ndots[slideIndex – 1].className += ” selected”;n}n”}]