Driving by the Rules: A Benchmark for Integrating Traffic Sign Regulations into Vectorized HD Map
Ensuring adherence to traffic sign regulations is essential for both human and autonomous vehicle navigation. While current online mapping solutions often prioritize the construction of the geometric and connectivity layers of HD maps, overlooking the construction of the traffic regulation layer wit...
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| creator | Chang, Xinyuan Xue, Maixuan Liu, Xinran Pan, Zheng Wei, Xing |
| description | Ensuring adherence to traffic sign regulations is essential for both human
and autonomous vehicle navigation. While current online mapping solutions often
prioritize the construction of the geometric and connectivity layers of HD
maps, overlooking the construction of the traffic regulation layer within HD
maps. Addressing this gap, we introduce MapDR, a novel dataset designed for the
extraction of Driving Rules from traffic signs and their association with
vectorized, locally perceived HD Maps. MapDR features over $10,000$ annotated
video clips that capture the intricate correlation between traffic sign
regulations and lanes. Built upon this benchmark and the newly defined task of
integrating traffic regulations into online HD maps, we provide modular and
end-to-end solutions: VLE-MEE and RuleVLM, offering a strong baseline for
advancing autonomous driving technology. It fills a critical gap in the
integration of traffic sign rules, contributing to the development of reliable
autonomous driving systems. |
| doi_str_mv | 10.48550/arxiv.2410.23780 |
| format | Article |
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and autonomous vehicle navigation. While current online mapping solutions often
prioritize the construction of the geometric and connectivity layers of HD
maps, overlooking the construction of the traffic regulation layer within HD
maps. Addressing this gap, we introduce MapDR, a novel dataset designed for the
extraction of Driving Rules from traffic signs and their association with
vectorized, locally perceived HD Maps. MapDR features over $10,000$ annotated
video clips that capture the intricate correlation between traffic sign
regulations and lanes. Built upon this benchmark and the newly defined task of
integrating traffic regulations into online HD maps, we provide modular and
end-to-end solutions: VLE-MEE and RuleVLM, offering a strong baseline for
advancing autonomous driving technology. It fills a critical gap in the
integration of traffic sign rules, contributing to the development of reliable
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and autonomous vehicle navigation. While current online mapping solutions often
prioritize the construction of the geometric and connectivity layers of HD
maps, overlooking the construction of the traffic regulation layer within HD
maps. Addressing this gap, we introduce MapDR, a novel dataset designed for the
extraction of Driving Rules from traffic signs and their association with
vectorized, locally perceived HD Maps. MapDR features over $10,000$ annotated
video clips that capture the intricate correlation between traffic sign
regulations and lanes. Built upon this benchmark and the newly defined task of
integrating traffic regulations into online HD maps, we provide modular and
end-to-end solutions: VLE-MEE and RuleVLM, offering a strong baseline for
advancing autonomous driving technology. It fills a critical gap in the
integration of traffic sign rules, contributing to the development of reliable
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and autonomous vehicle navigation. While current online mapping solutions often
prioritize the construction of the geometric and connectivity layers of HD
maps, overlooking the construction of the traffic regulation layer within HD
maps. Addressing this gap, we introduce MapDR, a novel dataset designed for the
extraction of Driving Rules from traffic signs and their association with
vectorized, locally perceived HD Maps. MapDR features over $10,000$ annotated
video clips that capture the intricate correlation between traffic sign
regulations and lanes. Built upon this benchmark and the newly defined task of
integrating traffic regulations into online HD maps, we provide modular and
end-to-end solutions: VLE-MEE and RuleVLM, offering a strong baseline for
advancing autonomous driving technology. It fills a critical gap in the
integration of traffic sign rules, contributing to the development of reliable
autonomous driving systems.</abstract><doi>10.48550/arxiv.2410.23780</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Artificial Intelligence Computer Science - Computer Vision and Pattern Recognition |
| title | Driving by the Rules: A Benchmark for Integrating Traffic Sign Regulations into Vectorized HD Map |
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