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。

自动驾驶中运动预测的高效基线

Efficient Baselines for Motion Prediction in Autonomous Driving

The success of motion prediction for autonomous driving relies on integration
of information from the HD maps. As maps are naturally graph-structured,
investigation on graph neural networks (GNNs) for encoding HD maps is
burgeoning in recent years. However, unlike many other applications where GNNs
have been straightforwardly deployed, HD maps are heterogeneous graphs where
vertices (lanes) are connected by edges (lane-lane interaction relationships)
of various nature, and most graph-based models are not designed to understand
the variety of edge types which provide crucial cues for predicting how the
agents would travel the lanes. To overcome this challenge, we propose
Path-Aware Graph Attention, a novel attention architecture that infers the
attention between two vertices by parsing the sequence of edges forming the
paths that connect them. Our analysis illustrates how the proposed attention
mechanism can facilitate learning in a didactic problem where existing graph
networks like GCN struggle. By improving map encoding, the proposed model
surpasses previous state of the art on the Argoverse Motion Forecasting
dataset, and won the first place in the 2021 Argoverse Motion Forecasting
Competition.

提出了一种基于路径感知的图形注意力机制，用于对高清地图进行编码，以帮助自主驾驶汽车预测路径，并在 Argoverse Motion Forecasting 数据集上取得了最优结果。

路径感知图注意力在动作预测的应用

Path-Aware Graph Attention for HD Maps in Motion Prediction

We propose a motion forecasting model that exploits a novel structured map
representation as well as actor-map interactions. Instead of encoding
vectorized maps as raster images, we construct a lane graph from raw map data
to explicitly preserve the map structure. To capture the complex topology and
long range dependencies of the lane graph, we propose LaneGCN which extends
graph convolutions with multiple adjacency matrices and along-lane dilation. To
capture the complex interactions between actors and maps, we exploit a fusion
network consisting of four types of interactions, actor-to-lane, lane-to-lane,
lane-to-actor and actor-to-actor. Powered by LaneGCN and actor-map
interactions, our model is able to predict accurate and realistic multi-modal
trajectories. Our approach significantly outperforms the state-of-the-art on
the large scale Argoverse motion forecasting benchmark.

通过构建一个从原始地图数据中构造出来的车道图，结合 LaneGCN 和 Actormap 交互设计了一个动态预测模型，可以准确地预测和展示多种行车情景，取得了比现有最好技术更好的表现。