Predicting the future motion of observed vehicles is a crucial enabler for safe autonomous driving. The field of motion prediction has seen large progress recently with State-of-the-Art (SotA) models achieving impressive results on large-scale public benchmarks. However, recent work revealed that learning-based methods are prone to predict off-road trajectories in challenging scenarios. These can be created by perturbing existing scenarios with additional turns in front of the target vehicle while the motion history is left unchanged. We argue that this indicates that SotA models do not consider the map information sufficiently and demonstrate how this can be solved, by representing model inputs and outputs in a Frenet frame defined by lane centreline sequences. To this end, we present a general wrapper that leverages a Frenet representation of the scene and that can be applied to SotA models without changing their architecture. We demonstrate the effectiveness of this approach in a comprehensive benchmark using two SotA motion prediction models. Our experiments show that this reduces the off-road rate on challenging scenarios by more than 90\%, without sacrificing average performance.

翻译：预测观测车辆的未来运动是实现安全自动驾驶的关键能力。近年来，运动预测领域取得了重大进展，最先进的（SotA）模型在大规模公共基准测试中取得了令人瞩目的成果。然而，近期研究表明，基于学习的方法在复杂场景下容易预测出越野轨迹。这些场景可通过在目标车辆前方添加额外转弯（同时保持运动历史不变）来扰动现有场景生成。我们认为这表明SotA模型未能充分考虑地图信息，并展示如何通过将模型输入输出表示为车道中心线序列定义的Frenet框架来解决该问题。为此，我们提出一种通用包装器，利用场景的Frenet表示，且可在不改变模型架构的情况下应用于SotA模型。我们在综合基准测试中使用两种SotA运动预测模型验证了该方法的有效性。实验表明，该方法在复杂场景下将越野率降低超过90%，且不牺牲平均性能。