Study on Multi-Task Learning for Autonomous Driving 


Vol. 48,  No. 9, pp. 1152-1160, Sep.  2023
10.7840/kics.2023.48.9.1152


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  Abstract

For autonomous driving, we explored a method for safe autonomous driving based on the given hardware conditions, taking into account the performance (accuracy, processing speed) of image recognition tasks performed by the corresponding sensors. In particular, we analyzed the performance of multiple image recognition optimization tasks through multi-task learning (MTL), which can process several tasks simultaneously, and proposed a MDE (Multi-task Decision and Enhancement) algorithm for optimization. Using this MDE algorithm, it is possible to determine multiple working sets that can minimize the overall delay time while optimizing accuracy. As a result of the experiment, we achieved up to around 15-54% reduction in execution time with similar accuracy performance through this strategy.

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[IEEE Style]

W. Jun, D. Kwak, S. Lee, "Study on Multi-Task Learning for Autonomous Driving," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 9, pp. 1152-1160, 2023. DOI: 10.7840/kics.2023.48.9.1152.

[ACM Style]

Woomin Jun, Daewon Kwak, and Sungjin Lee. 2023. Study on Multi-Task Learning for Autonomous Driving. The Journal of Korean Institute of Communications and Information Sciences, 48, 9, (2023), 1152-1160. DOI: 10.7840/kics.2023.48.9.1152.

[KICS Style]

Woomin Jun, Daewon Kwak, Sungjin Lee, "Study on Multi-Task Learning for Autonomous Driving," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 9, pp. 1152-1160, 9. 2023. (https://doi.org/10.7840/kics.2023.48.9.1152)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880