Modeling dynamics is often the first step to making a vehicle autonomous. While on-road autonomous vehicles have been extensively studied, off-road vehicles pose many challenging modeling problems. An off-road vehicle encounters highly complex and difficult-to-model terrain/vehicle interactions, as well as having complex vehicle dynamics of its own. These complexities can create challenges for effective high-speed control and planning. In this paper, we introduce a framework for multistep dynamics prediction that explicitly handles the accumulation of modeling error and remains scalable for sampling-based controllers. Our method uses a specially-initialized Long Short-Term Memory (LSTM) over a limited time horizon as the learned component in a hybrid model to predict the dynamics of a 4-person seating all-terrain vehicle (Polaris S4 1000 RZR) in two distinct environments. By only having the LSTM predict over a fixed time horizon, we negate the need for long term stability that is often a challenge when training recurrent neural networks. Our framework is flexible as it only requires odometry information for labels. Through extensive experimentation, we show that our method is able to predict millions of possible trajectories in real-time, with a time horizon of five seconds in challenging off road driving scenarios.

翻译：动力学建模通常是实现车辆自主化的首要步骤。尽管道路自主驾驶车辆已被广泛研究，但非道路车辆仍面临诸多具有挑战性的建模问题。非道路车辆不仅自身具有复杂的车辆动力学特性，还会遇到高度复杂且难以建模的地形/车辆交互作用。这些复杂性为有效的高速控制与规划带来了挑战。本文提出了一种面向多步动力学预测的框架，该框架显式处理了建模误差的累积问题，并保持了对基于采样的控制器的可扩展性。该方法利用特殊初始化的长短期记忆网络在有限时间范围内作为混合模型中的学习组件，以预测四座全地形车辆在两种不同环境中的动力学特性。通过仅让长短期记忆网络在固定时间范围内进行预测，我们消除了对长期稳定性的需求——而后者通常是训练循环神经网络时的难点。本框架具有灵活性，仅需里程计信息作为标签。通过大量实验证明，该方法能够在苛刻的非道路驾驶场景中，以五秒的时间范围实时预测数百万条可能的轨迹。