Reliable prediction of surround vehicle motion is a critical requirement for
path planning for autonomous vehicles. In this paper we propose a unified
framework for surround vehicle maneuver classification and motion prediction
that exploits multiple cues, namely, the estimated motion of vehicles, an
understanding of typical motion patterns of freeway traffic and inter-vehicle
interaction. We report our results in terms of maneuver classification accuracy
and mean and median absolute error of predicted trajectories against the ground
truth for real traffic data collected using vehicle mounted sensors on
freeways. An ablative analysis is performed to analyze the relative importance
of each cue for trajectory prediction. Additionally, an analysis of execution
time for the components of the framework is presented. Finally, we present
multiple case studies analyzing the outputs of our model for complex traffic
scenarios

本文提出了一种基于多个线索（交通工具的估计运动，高速公路交通典型运动模式和车辆间互动）的统一框架，用于自动驾驶汽车路径规划中环绕车辆的机动分类和运动预测。通过在高速公路上使用车载传感器收集的真实交通数据，我们对机动分类准确性和对地面实况的预测轨迹的平均绝对误差和中位数绝对误差进行了评估和报告。此外，还进行了消融分析来分析相对于预测轨迹，每个线索的相对重要性，并提供了框架组件执行时间的分析。最后，我们通过多个案例研究分析了我们模型的输出，以应对复杂的交通场景。