The ability to accurately predict feasible multimodal future trajectories of surrounding traffic participants is crucial for behavior planning in autonomous vehicles. The Motion Transformer (MTR), a state-of-the-art motion prediction method, alleviated mode collapse and instability during training and enhanced overall prediction performance by replacing conventional dense future endpoints with a small set of fixed prior motion intention points. However, the fixed prior intention points make the MTR multi-modal prediction distribution over-scattered and infeasible in many scenarios. In this paper, we propose the ControlMTR framework to tackle the aforementioned issues by generating scene-compliant intention points and additionally predicting driving control commands, which are then converted into trajectories by a simple kinematic model with soft constraints. These control-generated trajectories will guide the directly predicted trajectories by an auxiliary loss function. Together with our proposed scene-compliant intention points, they can effectively restrict the prediction distribution within the road boundaries and suppress infeasible off-road predictions while enhancing prediction performance. Remarkably, without resorting to additional model ensemble techniques, our method surpasses the baseline MTR model across all performance metrics, achieving notable improvements of 5.22% in SoftmAP and a 4.15% reduction in MissRate. Our approach notably results in a 41.85% reduction in the cross-boundary rate of the MTR, effectively ensuring that the prediction distribution is confined within the drivable area.

翻译：准确预测周围交通参与者的可行多模态未来轨迹，对于自动驾驶车辆的行为规划至关重要。运动Transformer（MTR）作为最先进的运动预测方法，通过用少量固定的先验运动意图点替代传统的密集未来端点，缓解了训练过程中的模态坍缩和不稳定性，并提升了整体预测性能。然而，固定的先验意图点导致MTR的多模态预测分布在许多场景中过于分散且不可行。本文提出ControlMTR框架，通过生成场景合规意图点并额外预测驾驶控制指令来解决上述问题，这些控制指令随后通过带软约束的简单运动学模型转化为轨迹。这些控制生成的轨迹将通过辅助损失函数引导直接预测的轨迹。结合我们提出的场景合规意图点，该方法能有效将预测分布限制在道路边界内，抑制不可行的越界预测，同时提升预测性能。值得注意的是，无需借助额外的模型集成技术，我们的方法在所有性能指标上均超越基线MTR模型，SoftmAP提升5.22%，MissRate降低4.15%。此外，该方法使MTR的越界率显著降低41.85%，有效确保预测分布被约束在可行驶区域内。