Abstract
In recent years, high-definition (HD) maps are considered to be significant complements to the on-board sensors of autonomous vehicles. Due to the highly dynamic characteristic of HD map, how to update HD maps fast and efficiently has become the pivotal issue which attracts great attentions. Currently, most of the research on this issue focuses on the crowdsourcing protocol design or specific updating method meanwhile disregarding the need of communication and computing resource in HD map update. Therefore, in this paper, we propose an integrated update framework including both an adaptive data transmission scheme and an adaptive task scheduling scheme to realize fast data collection and fast data processing for HD map update. To the best of our knowledge, this is the first work that addresses the problem of HD map update which takes the need of communication and computing resource into consideration. In this paper, we firstly illustrate the system architecture of our HD map update framework. Based on that, to realize fast data collection, we propose an adaptive data transmission scheme, which optimizes the transmission rate on different links of each vehicle considering the wireless channel capacity and fronthaul link capacity of the access points. Besides, to realize fast data processing, we propose an adaptive task scheduling scheme, which enables cooperative computing between the edge nodes and cloud computing server, considering the constraints of computing capacity and backhaul link capacity. To the end, we carry out simulations to verify the effectiveness of our proposed policies.
This work was supported by the Natural Science Foundation of China (NSFC) under Grants 61871362 and U19B2023, and by the Strategic Priority Research Program of Chinese Academy of Sciences under Grant XDA17040517.
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References
Automated vehicles require intelligent HD maps for reliable and safe driving. https://nds-association.org/hd-maps/. Accessed 12 Aug 2020
Extending the vision of automated vehicles with HD Maps and ADASIS. https://download.tomtom.com/open/banners. Accessed 30 Mar 2020
The future of maps: technologies, processes, and ecosystem. https://www.here.com/sites/g/files/odxslz166/files/2019-01/. Accessed 30 Mar 2020
Fratta, L., Gerla, M., Kleinrock, L.: The flow deviation method: An approach to store-and-forward communication network design. Networks 3(2), 97–133 (1973)
Hussain, R., Zeadally, S.: Autonomous cars: research results, issues, and future challenges. IEEE Commun. Surv. Tutorials 21(2), 1275–1313 (2018)
Kim, K., Cho, S., Chung, W.: HD map update for autonomous driving with crowdsourced data. IEEE Robot. Autom. Lett. 6(2), 1895–1901 (2021). https://doi.org/10.1109/LRA.2021.3060406
Liebner, M., Jain, D., Schauseil, J., Pannen, D., Hackelöer, A.: Crowdsourced HD map patches based on road model inference and graph-based slam. In: 2019 IEEE Intelligent Vehicles Symposium (IV), pp. 1211–1218 (2019). https://doi.org/10.1109/IVS.2019.8813860
Luo, S., Chen, X., Wu, Q., Zhou, Z., Yu, S.: HFEL: joint edge association and resource allocation for cost-efficient hierarchical federated edge learning. IEEE Trans. Wirel. Commun. 19(10), 6535–6548 (2020)
Pannen, D., Liebner, M., Hempel, W., Burgard, W.: How to keep HD maps for automated driving up to date. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 2288–2294 (2020). https://doi.org/10.1109/ICRA40945.2020.9197419
Ren, J., Yu, G., He, Y., Li, G.Y.: Collaborative cloud and edge computing for latency minimization. IEEE Trans. Veh. Technol. 68(5), 5031–5044 (2019)
Sawahashi, M., Kishiyama, Y., Morimoto, A., Nishikawa, D., Tanno, M.: Coordinated multipoint transmission/reception techniques for LTE-advanced [coordinated and distributed MIMO]. IEEE Wirel. Commun. 17(3), 26–34 (2010). https://doi.org/10.1109/MWC.2010.5490976
Seif, H.G., Hu, X.: Autonomous driving in the icity—HD maps as a key challenge of the automotive industry. Engineering 2(2), 159–162 (2016)
Wang, Y., Sheng, M., Wang, X., Wang, L., Li, J.: Mobile-edge computing: partial computation offloading using dynamic voltage scaling. IEEE Trans. Commun. 64(10), 4268–4282 (2016)
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Liu, J., Wang, Z., Zhang, C., Gu, C., Pan, M., Zhang, X. (2021). Adaptive Data Transmission and Task Scheduling for High-Definition Map Update. In: Liu, Z., Wu, F., Das, S.K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2021. Lecture Notes in Computer Science(), vol 12939. Springer, Cham. https://doi.org/10.1007/978-3-030-86137-7_48
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DOI: https://doi.org/10.1007/978-3-030-86137-7_48
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