Anticipating the motion of neighboring vehicles is crucial for autonomous
driving, especially on congested highways where even slight motion variations
can result in catastrophic collisions. An accurate prediction of a future
trajectory does not just rely on the previous trajectory, but also, more
importantly, a simulation of the complex interactions between other vehicles
nearby. Most state-of-the-art networks built to tackle the problem assume
readily available past trajectory points, hence lacking a full end-to-end
pipeline with direct video-to-output mechanism. In this article, we thus
propose a novel end-to-end architecture that takes raw video inputs and outputs
future trajectory predictions. It first extracts and tracks the 3D location of
the nearby vehicles via multi-head attention-based regression networks as well
as non-linear optimization. This provides the past trajectory points which then
feeds into the trajectory prediction algorithm consisting of an attention-based
LSTM encoder-decoder architecture, which allows it to model the complicated
interdependence between the vehicles and make an accurate prediction of the
future trajectory points of the surrounding vehicles. The proposed model is
evaluated on the large-scale BLVD dataset, and has also been implemented on
CARLA. The experimental results demonstrate that our approach outperforms
various state-of-the-art models.

本文提出了一种新颖的端到端架构，该架构接受原始视频输入并输出未来车辆运动轨迹预测，以解决自主驾驶中的邻近车辆运动预测问题。该架构通过多头注意力回归网络和非线性优化提取和跟踪附近车辆的三维位置，然后使用基于注意力的 LSTM 编码器 - 解码器算法对车辆之间的相互依赖关系建模，从而达到了高精度的未来车辆轨迹预测，实验结果表明，该算法优于目前各种先进模型。