Autonomous systems operate in environments that may change over time. An example is the control of a self-driving vehicle among pedestrians and human-controlled vehicles whose behavior may change based on factors such as traffic density, road visibility, and social norms. Therefore, the environment encountered during deployment rarely mirrors the environment and data encountered during training -- a phenomenon known as distribution shift -- which can undermine the safety of autonomous systems. Conformal prediction (CP) has recently been used along with data from the training environment to provide prediction regions that capture the behavior of the environment with a desired probability. When embedded within a model predictive controller (MPC), one can provide probabilistic safety guarantees, but only when the deployment and training environments coincide. Once a distribution shift occurs, these guarantees collapse. We propose a planning framework that is robust under distribution shifts by: (i) assuming that the underlying data distribution of the environment is parameterized by a nuisance parameter, i.e., an observable, interpretable quantity such as traffic density, (ii) training a conditional diffusion model that captures distribution shifts as a function of the nuisance parameter, (iii) observing the nuisance parameter online and generating cheap, synthetic data from the diffusion model for the observed nuisance parameter, and (iv) designing an MPC that embeds CP regions constructed from such synthetic data. Importantly, we account for discrepancies between the underlying data distribution and the diffusion model by using robust CP. Thus, the plans computed using robust CP enjoy probabilistic safety guarantees, in contrast with plans obtained from a single, static set of training data. We empirically demonstrate safety under diverse distribution shifts in the ORCA simulator.

翻译：自主系统运行的环境会随时间变化。以自动驾驶车辆在行人和人工驾驶车辆中的控制为例，这些交通参与者的行为可能随交通密度、道路能见度和社会规范等因素而改变。因此，部署阶段遇到的环境很少与训练阶段遇到的环境和数据完全一致——这种现象被称为分布偏移——可能危及自主系统的安全性。保形预测（CP）最近常与训练环境数据结合使用，以提供能以期望概率捕捉环境行为的预测区域。当嵌入模型预测控制器（MPC）时，可以给出概率安全保证，但这仅适用于部署环境与训练环境一致的情况。一旦发生分布偏移，这些保证就会失效。我们提出一种在分布偏移下具有鲁棒性的规划框架，其核心在于：（i）假设环境的基础数据分布由干扰参数（即可观测、可解释的量，如交通密度）参数化；（ii）训练一个条件扩散模型，以干扰参数为函数捕捉分布偏移；（iii）在线观测干扰参数，并根据观测值从扩散模型生成低成本合成数据；（iv）设计一个嵌入基于此类合成数据构建的CP区域的MPC。重要的是，我们通过使用鲁棒CP来弥补基础数据分布与扩散模型之间的差异。因此，与从单一静态训练数据集获得的规划方案相比，采用鲁棒CP计算的规划方案享有概率安全保证。我们在ORCA仿真器中通过实验验证了该方法在多种分布偏移下的安全性。