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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2209.08763 |
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| _version_ | 1866916691952271360 |
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| author | Wu, Fangyu Wang, Dequan Hwang, Minjune Hao, Chenhui Lu, Jiawei Zhang, Jiamu Chou, Christopher Darrell, Trevor Bayen, Alexandre |
| author_facet | Wu, Fangyu Wang, Dequan Hwang, Minjune Hao, Chenhui Lu, Jiawei Zhang, Jiamu Chou, Christopher Darrell, Trevor Bayen, Alexandre |
| contents | A significant portion of roads, particularly in densely populated developing countries, lacks explicitly defined right-of-way rules. These understructured roads pose substantial challenges for autonomous vehicle motion planning, where efficient and safe navigation relies on understanding decentralized human coordination for collision avoidance. This coordination, often termed "social driving etiquette," remains underexplored due to limited open-source empirical data and suitable modeling frameworks. In this paper, we present a novel dataset and modeling framework designed to study motion planning in these understructured environments. The dataset includes 20 aerial videos of representative scenarios, an image dataset for training vehicle detection models, and a development kit for vehicle trajectory estimation. We demonstrate that a consensus-based modeling approach can effectively explain the emergence of priority orders observed in our dataset, and is therefore a viable framework for decentralized collision avoidance planning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_08763 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset and Consensus-Based Models Wu, Fangyu Wang, Dequan Hwang, Minjune Hao, Chenhui Lu, Jiawei Zhang, Jiamu Chou, Christopher Darrell, Trevor Bayen, Alexandre Robotics Computer Vision and Pattern Recognition A significant portion of roads, particularly in densely populated developing countries, lacks explicitly defined right-of-way rules. These understructured roads pose substantial challenges for autonomous vehicle motion planning, where efficient and safe navigation relies on understanding decentralized human coordination for collision avoidance. This coordination, often termed "social driving etiquette," remains underexplored due to limited open-source empirical data and suitable modeling frameworks. In this paper, we present a novel dataset and modeling framework designed to study motion planning in these understructured environments. The dataset includes 20 aerial videos of representative scenarios, an image dataset for training vehicle detection models, and a development kit for vehicle trajectory estimation. We demonstrate that a consensus-based modeling approach can effectively explain the emergence of priority orders observed in our dataset, and is therefore a viable framework for decentralized collision avoidance planning. |
| title | Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset and Consensus-Based Models |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2209.08763 |