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Enhancement of High-definition Map Update Service Through Coverage-aware and Reinforcement Learning (2402.14582v1)

Published 8 Feb 2024 in cs.NI, cs.AI, and cs.LG

Abstract: High-definition (HD) Map systems will play a pivotal role in advancing autonomous driving to a higher level, thanks to the significant improvement over traditional two-dimensional (2D) maps. Creating an HD Map requires a huge amount of on-road and off-road data. Typically, these raw datasets are collected and uploaded to cloud-based HD map service providers through vehicular networks. Nevertheless, there are challenges in transmitting the raw data over vehicular wireless channels due to the dynamic topology. As the number of vehicles increases, there is a detrimental impact on service quality, which acts as a barrier to a real-time HD Map system for collaborative driving in Autonomous Vehicles (AV). In this paper, to overcome network congestion, a Q-learning coverage-time-awareness algorithm is presented to optimize the quality of service for vehicular networks and HD map updates. The algorithm is evaluated in an environment that imitates a dynamic scenario where vehicles enter and leave. Results showed an improvement in latency for HD map data of $75\%$, $73\%$, and $10\%$ compared with IEEE802.11p without Quality of Service (QoS), IEEE802.11 with QoS, and IEEE802.11p with new access category (AC) for HD map, respectively.

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References (43)
  1. Gov UK Department for Transport, others, , “Self-driving revolution to boost economy and improve road safety,” online, accessed 08/15/23, August 2022, https://www.gov.uk/government/news/self-driving-revolution-to-boost-economy-and-improve-road-safety.
  2. “Autonomous Vehicles — content.naic.org,” https://content.naic.org/cipr-topics/autonomous-vehicles#:~:text=The%20Insurance%20Institute%20for%20Highway,and%204.5%20million%20by%202030., [Accessed 09-12-2023].
  3. SAE, “SAE Levels of Driving Automation™ Refined for Clarity and International Audience,” online, accessed 8/15/23, May 2021, https://www.sae.org/blog/sae-j3016-update.
  4. B. E. Dicianno, S. Sivakanthan, S. A. Sundaram, S. Satpute, H. Kulich, E. Powers, N. Deepak, R. Russell, R. Cooper, and R. A. Cooper, “Systematic review: Automated vehicles and services for people with disabilities,” Neuroscience Letters, vol. 761, p. 136103, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S030439402100481X
  5. M. Kroesen, D. Milakis, and B. van Wee, “Automated vehicles: Changes in expert opinions over time,” Transport Policy, vol. 136, pp. 1–10, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0967070X23000586
  6. J. Hwang and S. Kim, “Autonomous vehicle transportation service for people with disabilities: Policy recommendations based on the evidence from hybrid choice model,” Journal of Transport Geography, vol. 106, p. 103499, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0966692322002228
  7. ZVI GREENSTEIN (NVIDIA), “Announcing NVIDIA DRIVE Map: Scalable, Multi-Modal Mapping Engine Accelerates Deployment of Level 3 and Level 4 Autonomy,” online, accessed 8/15/23, March 2022, ”https://blogs.nvidia.com/blog/2022/03/22/drive-map-multi-modal-mapping-engine/”.
  8. H. G. Seif and X. Hu, “Autonomous driving in the icity—hd maps as a key challenge of the automotive industry,” Engineering, vol. 2, no. 2, pp. 159–162, 2016. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2095809916309432
  9. L. Martínez-Buelvas, A. Rakotonirainy, D. Grant-Smith, and O. Oviedo-Trespalacios, “A transport justice approach to integrating vulnerable road users with automated vehicles,” Transportation Research Part D: Transport and Environment, vol. 113, p. 103499, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S136192092200325X
  10. W. Qi, Q. Song, S. Wang, Z. Liu, and L. Guo, “Social prediction-based handover in collaborative-edge-computing-enabled vehicular networks,” IEEE Transactions on Computational Social Systems, vol. 9, no. 1, pp. 207–217, 2022.
  11. S. Kalwar, K.-W. Chin, and Z. Yuan, “Optimizing tdma schedule and sic-capable uav position via gibbs sampling,” IEEE Networking Letters, vol. 2, no. 3, pp. 97–100, 2020.
  12. S. Chen, Z. Yuan, and G.-M. Muntean, “Balancing energy and quality awareness: A mac-layer duty cycle management solution for multimedia delivery over wireless mesh networks,” IEEE Transactions on Vehicular Technology, vol. 66, no. 2, pp. 1547–1560, 2017.
  13. M. M. Hasan and M. Arifuzzaman, “Avaq-edca: Additional video access queue based edca technique for dense ieee 802.11 ax networks,” in 2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2), 2019, pp. 1–4.
  14. J. Redondo, Z. Yuan, and N. Aslam, “Performance analysis of high-definition map distribution in vanet,” in 2023 International Wireless Communications and Mobile Computing (IWCMC), 2023, pp. 55–60.
  15. H. Park and C. You, “Latency impact for massive real-time applications on multi link operation,” in 2021 IEEE Region 10 Symposium (TENSYMP), 2021, pp. 1–5.
  16. M. A. Salem, I. F. Tarrad, M. I. Youssef, and S. M. A. El-kader, “An adaptive edca selfishness-aware scheme for dense wlans in 5g networks,” IEEE Access, vol. 8, pp. 47 034–47 046, 2020.
  17. A. Pressas, Z. Sheng, F. Ali, and D. Tian, “A q-learning approach with collective contention estimation for bandwidth-efficient and fair access control in ieee 802.11p vehicular networks,” IEEE Transactions on Vehicular Technology, vol. 68, no. 9, pp. 9136–9150, 2019.
  18. Y. Wang, A. Ahmed, B. Krishnamachari, and K. Psounis, “Ieee 802.11p performance evaluation and protocol enhancement,” in 2008 IEEE International Conference on Vehicular Electronics and Safety, 2008, pp. 317–322.
  19. S. Eichler, “Performance evaluation of the ieee 802.11p wave communication standard,” in 2007 IEEE 66th Vehicular Technology Conference, 2007, pp. 2199–2203.
  20. M. Shinzaki, Y. Koda, K. Yamamoto, T. Nishio, and M. Morikura, “Reducing transmission delay in edca using policy gradient reinforcement learning,” in 2020 IEEE 17th Annual Consumer Communications and Networking Conference (CCNC), 2020, pp. 1–6.
  21. Q. Qi, J. Wang, Z. Ma, H. Sun, Y. Cao, L. Zhang, and J. Liao, “Knowledge-driven service offloading decision for vehicular edge computing: A deep reinforcement learning approach,” IEEE Transactions on Vehicular Technology, vol. 68, no. 5, pp. 4192–4203, 2019.
  22. C. Zhu, Y.-H. Chiang, A. Mehrabi, Y. Xiao, A. Ylä-Jääski, and Y. Ji, “Chameleon: Latency and resolution aware task offloading for visual-based assisted driving,” IEEE Transactions on Vehicular Technology, vol. 68, no. 9, pp. 9038–9048, 2019.
  23. J. Lee, K. Lee, A. Yoo, and C. Moon, “Design and implementation of edge-fog-cloud system through hd map generation from lidar data of autonomous vehicles,” Electronics, vol. 9, no. 12, 2020. [Online]. Available: https://www.mdpi.com/2079-9292/9/12/2084
  24. H. Zhang, W. Tian, and J. Liu, “Dq-edca: Dynamic queue management based edca mechanism for vehicle communication,” in 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC), 2018, pp. 1131–1136.
  25. A. J. Gopinath and B. Nithya, “Enhancement of ieee802.11p-based channel access scheme for internet of vehicle (iov),” in 2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), 2019, pp. 1–6.
  26. L. M. Rekik and M. Bourenane, “Logical edca: A novel edca mechanism for ieee 802.11 based networks,” in 2020 Second International Conference on Embedded & Distributed Systems (EDiS), 2020, pp. 99–104.
  27. A. Kumar, G. Verma, C. Rao, A. Swami, and S. Segarra, “Adaptive contention window design using deep q-learning,” in ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, pp. 4950–4954.
  28. W. Wydmański and S. Szott, “Contention window optimization in ieee 802.11ax networks with deep reinforcement learning,” in 2021 IEEE Wireless Communications and Networking Conference (WCNC), 2021, pp. 1–6.
  29. B. Li, H. Zhang, P. Hao, and J. Li, “Sojourn time estimation-based small cell selection in ultra-dense networks,” in 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 2017, pp. 1–5.
  30. “Ieee standard for information technology– local and metropolitan area networks– specific requirements– part 11: Wireless lan medium access control (mac) and physical layer (phy) specifications amendment 6: Wireless access in vehicular environments,” IEEE Std 802.11p-2010 (Amendment to IEEE Std 802.11-2007 as amended by IEEE Std 802.11k-2008, IEEE Std 802.11r-2008, IEEE Std 802.11y-2008, IEEE Std 802.11n-2009, and IEEE Std 802.11w-2009), pp. 1–51, 2010.
  31. G. Cecchini, A. Bazzi, B. M. Masini, and A. Zanella, “Performance comparison between ieee 802.11p and lte-v2v in-coverage and out-of-coverage for cooperative awareness,” in 2017 IEEE Vehicular Networking Conference (VNC), 2017, pp. 109–114.
  32. K.-Y. Ho, P.-C. Kang, C.-H. Hsu, and C.-H. Lin, “Implementation of wave/dsrc devices for vehicular communications,” in 2010 International Symposium on Computer, Communication, Control and Automation (3CA), vol. 2, 2010, pp. 522–525.
  33. “OMNeT++ Discrete Event Simulator — omnetpp.org,” https://omnetpp.org/, [Accessed 19-08-2023].
  34. “INET Framework - INET Framework — inet.omnetpp.org,” https://inet.omnetpp.org/, [Accessed 19-08-2023].
  35. C. Sommer, R. German, and F. Dressler, “Bidirectionally coupled network and road traffic simulation for improved ivc analysis,” IEEE Transactions on Mobile Computing, vol. 10, no. 1, pp. 3–15, 2011.
  36. P. A. Lopez, M. Behrisch, L. Bieker-Walz, J. Erdmann, Y.-P. Flötteröd, R. Hilbrich, L. Lücken, J. Rummel, P. Wagner, and E. Wiessner, “Microscopic traffic simulation using sumo,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 2018, pp. 2575–2582.
  37. M. Schettler, D. S. Buse, A. Zubow, and F. Dressler, “How to Train your ITS? Integrating Machine Learning with Vehicular Network Simulation,” in 12th IEEE Vehicular Networking Conference (VNC 2020).   Virtual Conference: IEEE, 12 2020.
  38. “Power BI Report — tinyurl.com,” https://www.tinyurl.com/tadudashboard, [Accessed 22-08-2023].
  39. “YouTube recommended upload encoding settings - YouTube Help — support.google.com,” https://support.google.com/youtube/answer/1722171?hl=en-#zippy=%2Ccontainer-mp%2Caudio-codec-aac-lc%2Cvideo-codec-h%2Cframe-rate%2Cbitrate, [Accessed 22-08-2023].
  40. 5GAA Automotive Association, “C-V2X Use Cases Volume II: Examples and Service Level,” online, accessed 15/08/23, October 2020, https://5gaa.org/content/uploads/2020/10/5GAAWhite-PaperC-V2X-Use-Cases-Volume-II.pdf.
  41. P. Bokare and A. Maurya, “Acceleration-deceleration behaviour of various vehicle types,” Transportation Research Procedia, vol. 25, pp. 4733–4749, 2017, world Conference on Transport Research - WCTR 2016 Shanghai. 10-15 July 2016. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2352146517307937
  42. “cisco.com,” https://www.cisco.com/c/en/us/td/docs/ios/solutions˙docs/qos˙solutions/QoSVoIP/QoSVoIP.pdf, [Accessed 14-11-2023].
  43. R. K. Jain, D.-M. W. Chiu, W. R. Hawe, et al., “A quantitative measure of fairness and discrimination,” Eastern Research Laboratory, Digital Equipment Corporation, Hudson, MA, vol. 21, 1984.
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