Road Tracking Enhancements for Self-Driving Cars Applications

Raid Rafi Omar Al-Nima*, Musab T.S. Al-Kaltakchi, Tingting Han, Wai Lok Woo

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review


Road tracking (RT) can be considered as one of trendy research topics, as it is essential in self-driving car applications. In this paper, we are proposing two main contributions. First of all, we propose a new Deep Reinforcement Support vector machine Learning for Road Tracking (DRSL-RT) approach. Secondly, we combine the DRSL-RT with the novel Deep Reinforcement Learning for Road Tracking (DRL-RT) that was suggested in [1], to further enhance the RT performances. Databases from the SYNTHIA-SEQS-05 are employed in this work, where databases from four environments are considered. These are the rain, heavy-rain, fog and spring environments. The four environments are employed and evaluated separately and all together. In addition, various comparisons with other networks are constructed. Here, final results are significantly increased to 91.79%, 92.74%, 96.48%, 98.48% and 97.75% for rain, heavy-rain, fog, spring and all environments, respectively.

Original languageEnglish
Article number040004
JournalAIP Conference Proceedings
Issue number1
Early online date29 Sept 2023
Publication statusPublished - 2023
Event2022 International Conference on Innovations in Science, Hybrid Materials, and Vibration Analysis, IC-ISHVA 2022 - Virtual, Online, India
Duration: 16 Jul 202217 Jul 2022

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