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- W2897045329 abstract "For any operation involving autonomous vehicles, localization of mobile agents is a critical module. Unfortunately, localization methods are still dependant on tweaked parameters, under assumptions of poor trajectories, and that can fail dis- gracefully under real-world conditions. As our systems evolve to deployment-ready state, we see the looming need for more adaptive responses, where sudden changes of conditions can be accommodated by the localization algorithm. This paper addresses this gap, by proposing a parameter self-tuning scheme based on learning from acquired data." @default.
- W2897045329 created "2018-10-26" @default.
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- W2897045329 date "2018-08-01" @default.
- W2897045329 modified "2023-09-26" @default.
- W2897045329 title "Parameter-free network localization for mobile vehicles by learning from data" @default.
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- W2897045329 doi "https://doi.org/10.1109/ucomms.2018.8493177" @default.
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