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
this https URL

通过与自动驾驶公司 May Mobility 的合作，我们提出了 MARS 数据集，它统合了多智能体、多次遍历和多模态自动驾驶研究场景，能够促进自动驾驶车辆的感知、预测和规划能力的转变。

多智能体多遍历多模态自动驾驶：开放 MARS 数据集

Multiagent Multitraversal Multimodal Self-Driving: Open MARS Dataset

Effective understanding of dynamically evolving multiagent interactions is
crucial to capturing the underlying behavior of agents in social systems. It is
usually challenging to observe these interactions directly, and therefore
modeling the latent interactions is essential for realizing the complex
behaviors. Recent work on Dynamic Neural Relational Inference (DNRI) captures
explicit inter-agent interactions at every step. However, prediction at every
step results in noisy interactions and lacks intrinsic interpretability without
post-hoc inspection. Moreover, it requires access to ground truth annotations
to analyze the predicted interactions, which are hard to obtain. This paper
introduces DIDER, Discovering Interpretable Dynamically Evolving Relations, a
generic end-to-end interaction modeling framework with intrinsic
interpretability. DIDER discovers an interpretable sequence of inter-agent
interactions by disentangling the task of latent interaction prediction into
sub-interaction prediction and duration estimation. By imposing the consistency
of a sub-interaction type over an extended time duration, the proposed
framework achieves intrinsic interpretability without requiring any post-hoc
inspection. We evaluate DIDER on both synthetic and real-world datasets. The
experimental results demonstrate that modeling disentangled and interpretable
dynamic relations improves performance on trajectory forecasting tasks.

DIDER 是一个通用的端到端交互建模框架，其提供了内在的可解释性，通过将潜在的交互预测任务分解为子交互预测和持续时间估计，利用子交互类型在扩展时间范围内的一致性来实现内在的可解释性，实验结果表明，建模可解释动态关系可以提高轨迹预测任务的性能