The field of motion prediction for automated driving has seen tremendous progress recently, bearing ever-more mighty neural network architectures. Leveraging these powerful models bears great potential for the closely related planning task. In this letter we propose a novel goal-conditioning method and show its potential to transform a state-of-the-art prediction model into a goal-directed planner. Our key insight is that conditioning prediction on a navigation goal at the behaviour level outperforms other widely adopted methods, with the additional benefit of increased model interpretability. We train our model on a large open-source dataset and show promising performance in a comprehensive benchmark.

翻译：自动驾驶运动预测领域近期取得了巨大进展，涌现出性能日益强大的神经网络架构。利用这些强大模型对于密切相关的规划任务具有巨大潜力。本文提出了一种新颖的目标条件方法，并展示了其将最先进的预测模型转化为目标导向规划器的潜力。我们的关键洞见在于：在行为层面对预测进行导航目标条件约束，不仅优于其他广泛采用的方法，还具备提升模型可解释性的额外优势。我们在大规模开源数据集上训练模型，并在综合性基准测试中展示了良好的性能表现。