自动驾驶中基于多项式表示的长期交通场景预测
Long-term Traffic Scene Prediction via Polynomial Representations in Autonomous Driving
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中文总结 AI 辅助
本研究提出基于多项式表示的自动驾驶交通场景预测模型，结合理论分析与实证验证，在标准基准上接近最优准确率，提升分布偏移下的泛化能力，生成更合理的多智能体场景，降低计算成本。
中文摘要 AI 辅助
本论文通过引入基于多项式表示的鲁棒且计算高效的模型，解决了自动驾驶中交通场景预测的基础挑战。尽管传统的基于序列的表示方法常受噪声和泛化能力的困扰，但本研究表明多项式表示在计算效率、泛化能力和预测合理性方面具有显著优势。通过理论分析和实证验证，本论文证明中等次数的多项式能够高保真地捕捉现实世界的运动动力学，同时不会限制预测性能。在此基础上，一种同时用多项式表示轨迹和地图几何的预测模型，在标准基准上达到了接近当前最优（state-of-the-art）的准确率，同时在分布偏移下的泛化能力得到了大幅提升。将这一概念扩展后，一种基于扩散（diffusion）的生成框架能够实现多智能体场景生成，生成的交通延续结果比传统基线方法产生的结果更合理，运动学上也更一致。在Argoverse 2和Waymo Open数据集上的评估证实，多项式表示降低了计算成本，增强了跨数据集泛化能力，并且生成的轨迹更平滑、行为合理性更高。研究结果表明，标准的分布内评估和基于回归的指标可能无法反映模型的真实泛化能力和预测合理性。通过提供理论依据和实证验证，本论文确立了多项式轨迹表示作为安全关键型自动驾驶中交通场景预测的一种高效、具表达力且可泛化的基础。
英文摘要
This thesis addresses fundamental challenges in traffic scene prediction for autonomous driving by introducing robust and computationally efficient models based on polynomial representations. While conventional sequence-based representations often struggle with noise and generalization, this work demonstrates that polynomial representations offer significant advantages in computational efficiency, generalization, and prediction plausibility. Through theoretical analysis and empirical validation, this thesis demonstrates that moderate-degree polynomials capture real-world motion dynamics with high fidelity without constraining predictive performance. Building on this foundation, a prediction model representing both trajectories and map geometry with polynomial representations achieves near state-of-the-art accuracy on standard benchmarks while substantially improving generalization under distribution shift. Extending this concept, a diffusion- based generative framework enables multi-agent scene generation, producing traffic continuations that are more plausible and kinematically consistent than those generated by conventional baselines. Evaluations on the Argoverse 2 and Waymo Open datasets confirm that polynomial representations reduce computational cost, enhance cross-dataset generalization, and yield smoother trajectories and higher behavioral plausibility. The findings reveal that standard in-distribution evaluation and regression-based metrics may fail to reflect true model generalization and prediction plausibility. By providing theoretical justification and empirical validation, this dissertation estab- lishes polynomial trajectory representations as an efficient, expressive, and generalizable foundation for traffic scene prediction in safety critical autonomous driving.
