It is desirable to predict the behavior of traffic participants conditioned on different planned trajectories of the autonomous vehicle. This allows the downstream planner to estimate the impact of its decisions. Recent approaches for conditional behavior prediction rely on a regression decoder, meaning that coordinates or polynomial coefficients are regressed. In this work we revisit set-based trajectory prediction, where the probability of each trajectory in a predefined trajectory set is determined by a classification model, and first-time employ it to the task of conditional behavior prediction. We propose RESET, which combines a new metric-driven algorithm for trajectory set generation with a graph-based encoder. For unconditional prediction, RESET achieves comparable performance to a regression-based approach. Due to the nature of set-based approaches, it has the advantageous property of being able to predict a flexible number of trajectories without influencing runtime or complexity. For conditional prediction, RESET achieves reasonable results with late fusion of the planned trajectory, which was not observed for regression-based approaches before. This means that RESET is computationally lightweight to combine with a planner that proposes multiple future plans of the autonomous vehicle, as large parts of the forward pass can be reused.

翻译：对交通参与者在自动驾驶车辆不同规划轨迹条件下的行为进行预测是理想的，这使下游规划器能够评估其决策的影响。近期用于条件行为预测的方法依赖于回归解码器，即对坐标或多项式系数进行回归。本项目重新审视了基于集合的轨迹预测方法，其中预定义轨迹集合中各轨迹的概率由分类模型决定，并首次将其应用于条件行为预测任务。我们提出了RESET，它结合了新的度量驱动轨迹集合生成算法与基于图的编码器。对于无条件预测，RESET实现了与基于回归方法相当的性能。由于基于集合方法的特性，它具备预测灵活数量的轨迹而不影响运行时间或复杂度的优势。对于条件预测，RESET通过晚融合规划轨迹取得了合理结果，这一现象在基于回归的方法中此前未被观察到。这意味着RESET在与提出多种未来规划的自动驾驶车辆规划器相结合时计算量轻便，因为前向传递的大部分计算可以被复用。