Motion Prediction (MP) of multiple surroundings agents is a crucial task in
arbitrarily complex environments, from simple robots to Autonomous Driving
Stacks (ADS). Current techniques tackle this problem using end-to-end
pipelines, where the input data is usually a rendered top-view of the physical
information and the past trajectories of the most relevant agents; leveraging
this information is a must to obtain optimal performance. In that sense, a
reliable ADS must produce reasonable predictions on time. However, despite many
approaches use simple ConvNets and LSTMs to obtain the social latent features,
State-Of-The-Art (SOTA) models might be too complex for real-time applications
when using both sources of information (map and past trajectories) as well as
little interpretable, specially considering the physical information. Moreover,
the performance of such models highly depends on the number of available inputs
for each particular traffic scenario, which are expensive to obtain,
particularly, annotated High-Definition (HD) maps.
In this work, we propose several efficient baselines for the well-known
Argoverse 1 Motion Forecasting Benchmark. We aim to develop compact models
using SOTA techniques for MP, including attention mechanisms and GNNs. Our
lightweight models use standard social information and interpretable map
information such as points from the driveable area and plausible centerlines by
means of a novel preprocessing step based on kinematic constraints, in
opposition to black-box CNN-based or too-complex graphs methods for map
encoding, to generate plausible multimodal trajectories achieving up-to-pair
accuracy with less operations and parameters than other SOTA methods. Our code
is publicly available at this https URL .

我们提出了一种基于 Argoverse 的有效基准线方法，旨在利用用于 Motion Prediction 的最新技术，包括注意力机制和 GNNs，来生成可行的多模态轨迹，以较少的操作和参数实现与其他 SOTA 方法相当的准确性。