High-speed autonomous driving in off-road environments has immense potential
for various applications, but it also presents challenges due to the complexity
of vehicle-terrain interactions. In such environments, it is crucial for the
vehicle to predict its motion and adjust its controls proactively in response
to environmental changes, such as variations in terrain elevation. To this end,
we propose a method for learning terrain-aware kinodynamic model which is
conditioned on both proprioceptive and exteroceptive information. The proposed
model generates reliable predictions of 6-degree-of-freedom motion and can even
estimate contact interactions without requiring ground truth force data during
training. This enables the design of a safe and robust model predictive
controller through appropriate cost function design which penalizes sampled
trajectories with unstable motion, unsafe interactions, and high levels of
uncertainty derived from the model. We demonstrate the effectiveness of our
approach through experiments on a simulated off-road track, showing that our
proposed model-controller pair outperforms the baseline and ensures robust
high-speed driving performance without control failure.

提出一种适用于自主驾驶的、适应于车辆周围复杂地形信息的、六自由度动态学模型，并设计出相应的安全稳定的模型预测控制器。仿真实验表明，相比基线模型，该模型 - 控制器组合表现出更好的高速行驶性能，具备较高的安全性和稳健性。