Motion prediction (MP) of multiple agents is a crucial task in arbitrarily
complex environments, from social robots to self-driving cars. Current
approaches tackle this problem using end-to-end networks, where the input data
is usually a rendered top-view of the scene and the past trajectories of all
the agents; leveraging this information is a must to obtain optimal
performance. In that sense, a reliable Autonomous Driving (AD) system must
produce reasonable predictions on time, however, despite many of these
approaches use simple ConvNets and LSTMs, models might not be efficient enough
for real-time applications when using both sources of information (map and
trajectory history). Moreover, the performance of these models highly depends
on the amount of training data, which can be expensive (particularly the
annotated HD maps). In this work, we explore how to achieve competitive
performance on the Argoverse 1.0 Benchmark using efficient attention-based
models, which take as input the past trajectories and map-based features from
minimal map information to ensure efficient and reliable MP. These features
represent interpretable information as the driveable area and plausible goal
points, in opposition to black-box CNN-based methods for map processing.

本文将使用基于 Efficient Attention 的模型，从 Argoverse 1.0 基准测试中的最小地图信息中提取过去的轨迹和地图特征，以实现关于自动驾驶的运动预测。