让自动驾驶车学会预判风险并动态调速，真实路况下表现更聪明。
Multistep Belief Space Dynamics Learning For Risk-Aware Control
- 基于多步信念空间动态学习，实时预测驾驶不确定性演化
- 在复杂越野场景中实现安全速度自适应，行驶数英里无事故
- 结构设计关键，偏离设计会导致规划性能显著下降
随着自动驾驶车辆从简化研究环境走向实际应用，人类驾驶与自主系统之间的动态行为差距依然巨大。为满足现实世界需求，必须自然地发展出风险感知行为。当前风险感知规划与控制的主要挑战在于如何预测动态不确定性随时间的演变，并在此基础上优化计划，避免过度保守。本文提出一种学习框架，用于预测可用于模型预测控制（MPC）的分布式动态。我们探讨了在MPC中学习分布式动态时结构的重要性。在大规模真实非铺装道路驾驶数据集上进行了严谨的消融实验，验证了偏离所提结构对性能的影响。此外，我们在全尺寸车辆上部署了学习模型与规划系统，在严苛非铺装条件下表现良好。该规划架构能根据环境自然调节车速，持续展现出智能行为，覆盖多样地形行驶数英里。
原文摘要 · Abstract (English)
As autonomous vehicles move from a simplified research setting to practical use, there exists a large gap between the dynamic behavior of a human driving and an autonomous system. Risk-aware behavior needs to naturally develop in order to scale to the demands of the real world. A major issue for risk-aware planning and control has been predicting how dynamical uncertainty evolves through time and optimizing plans that account for this without being overly conservative. Here, we present a learning framework to predict distributional dynamics that can be optimized in real time for Model Predictive Control (MPC). We explore the importance of structure when learning distributional dynamics for use in MPC. A rigorous ablation study is conducted on a large dataset of real world off-road driving that shows the impact of deviations from our proposed structure. Furthermore, we deploy our learned model and planning stack on a full sized vehicle in challenging off-road conditions. Our planning architecture is able to naturally regulate the speed of the vehicle based on the environment and consistently demonstrates intelligent behavior over miles of diverse terrain.
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