Multiagent Multitraversal Multimodal Self-Driving: Open MARS Dataset

TL;DR

The paper introduces the MARS dataset, a large-scale multiagent, multitraversal, multimodal autonomous vehicle dataset designed to enhance perception, prediction, and planning in autonomous driving through multiagent interactions and repeated traversals.

Contribution

It presents the MARS dataset, enabling multiagent, multitraversal, and multimodal research in autonomous driving, which was lacking in existing datasets.

Findings

Demonstrated improved place recognition and neural reconstruction.

Enabled research on multiagent perception and multitraversal 3D reconstruction.

Provided a new benchmark for autonomous vehicle perception tasks.

Abstract

Large-scale datasets have fueled recent advancements in AI-based autonomous vehicle research. However, these datasets are usually collected from a single vehicle's one-time pass of a certain location, lacking multiagent interactions or repeated traversals of the same place. Such information could lead to transformative enhancements in autonomous vehicles' perception, prediction, and planning capabilities. To bridge this gap, in collaboration with the self-driving company May Mobility, we present the MARS dataset which unifies scenarios that enable MultiAgent, multitraveRSal, and multimodal autonomous vehicle research. More specifically, MARS is collected with a fleet of autonomous vehicles driving within a certain geographical area. Each vehicle has its own route and different vehicles may appear at nearby locations. Each vehicle is equipped with a LiDAR and surround-view RGB cameras. We curate two subsets in MARS: one facilitates collaborative driving with multiple vehicles simultaneously present at the same location, and the other enables memory retrospection through asynchronous traversals of the same location by multiple vehicles. We conduct experiments in place recognition and neural reconstruction. More importantly, MARS introduces new research opportunities and challenges such as multitraversal 3D reconstruction, multiagent perception, and unsupervised object discovery. Our data and codes can be found at https://ai4ce.github.io/MARS/.