论文标题
车辆类型特定的航点生成
Vehicle Type Specific Waypoint Generation
论文作者
论文摘要
我们开发了一种通用机制，用于从概率的驾驶行为基础模型中生成车辆型特定的航路点序列。许多基础行为模型都经过了不包括车辆信息的数据培训，这些数据限制了其在下游应用程序（例如计划）中的实用程序。我们的新方法有条件地将这种行为预测模型专门为车型的行为预测模型通过利用用于生产特定车辆控制器的强化学习算法的副产品。我们展示了如何使用通用的概率行为模型组成车辆特定的价值函数估计，以生成车辆型特定的路线序列，而这些序列序列更可能在物理上是可行的，而不是其车辆敏捷的序列。
We develop a generic mechanism for generating vehicle-type specific sequences of waypoints from a probabilistic foundation model of driving behavior. Many foundation behavior models are trained on data that does not include vehicle information, which limits their utility in downstream applications such as planning. Our novel methodology conditionally specializes such a behavior predictive model to a vehicle-type by utilizing byproducts of the reinforcement learning algorithms used to produce vehicle specific controllers. We show how to compose a vehicle specific value function estimate with a generic probabilistic behavior model to generate vehicle-type specific waypoint sequences that are more likely to be physically plausible then their vehicle-agnostic counterparts.
