Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset and Consensus-Based Models
This addresses motion planning challenges for autonomous vehicles in densely populated developing countries, representing an incremental advance with new data and a modeling framework.
The paper tackles the problem of autonomous vehicle motion planning on understructured roads lacking right-of-way rules by presenting a novel dataset and consensus-based modeling framework, showing that this approach effectively explains priority orders in the dataset.
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.