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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.07218 |
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| _version_ | 1866912022534291456 |
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| author | Yildirim, Mustafa Dagda, Barkin Asodia, Vinal Fallah, Saber |
| author_facet | Yildirim, Mustafa Dagda, Barkin Asodia, Vinal Fallah, Saber |
| contents | How effective are recent advancements in autonomous vehicle perception systems when applied to real-world autonomous vehicle control? While numerous vision-based autonomous vehicle systems have been trained and evaluated in simulated environments, there is a notable lack of real-world validation for these systems. This paper addresses this gap by presenting the real-world validation of state-of-the-art perception systems that utilize Behavior Cloning (BC) for lateral control, processing raw image data to predict steering commands. The dataset was collected using a scaled research vehicle and tested on various track setups. Experimental results demonstrate that these methods predict steering angles with low error margins in real-time, indicating promising potential for real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_07218 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Behavioral Cloning Models Reality Check for Autonomous Driving Yildirim, Mustafa Dagda, Barkin Asodia, Vinal Fallah, Saber Robotics Artificial Intelligence Computer Vision and Pattern Recognition How effective are recent advancements in autonomous vehicle perception systems when applied to real-world autonomous vehicle control? While numerous vision-based autonomous vehicle systems have been trained and evaluated in simulated environments, there is a notable lack of real-world validation for these systems. This paper addresses this gap by presenting the real-world validation of state-of-the-art perception systems that utilize Behavior Cloning (BC) for lateral control, processing raw image data to predict steering commands. The dataset was collected using a scaled research vehicle and tested on various track setups. Experimental results demonstrate that these methods predict steering angles with low error margins in real-time, indicating promising potential for real-world applications. |
| title | Behavioral Cloning Models Reality Check for Autonomous Driving |
| topic | Robotics Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.07218 |