Driving in an off-road environment is challenging for autonomous vehicles due
to the complex and varied terrain. To ensure stable and efficient travel, the
vehicle requires consideration and balancing of environmental factors, such as
undulations, roughness, and obstacles, to generate optimal trajectories that
can adapt to changing scenarios. However, traditional motion planners often
utilize a fixed cost function for trajectory optimization, making it difficult
to adapt to different driving strategies in challenging irregular terrains and
uncommon scenarios. To address these issues, we propose an adaptive motion
planner based on human-like cognition and cost evaluation for off-road driving.
First, we construct a multi-layer map describing different features of off-road
terrains, including terrain elevation, roughness, obstacle, and artificial
potential field map. Subsequently, we employ a CNN-LSTM network to learn the
trajectories planned by human drivers in various off-road scenarios. Then,
based on human-like generated trajectories in different environments, we design
a primitive-based trajectory planner that aims to mimic human trajectories and
cost weight selection, generating trajectories that are consistent with the
dynamics of off-road vehicles. Finally, we compute optimal cost weights and
select and extend behavioral primitives to generate highly adaptive, stable,
and efficient trajectories.
We validate the effectiveness of the proposed method through experiments in a
desert off-road environment with complex terrain and varying road conditions.
The experimental results show that the proposed human-like motion planner has
excellent adaptability to different off-road conditions. It shows real-time
operation, greater stability, and more human-like planning ability in diverse
and challenging scenarios.

提出一种基于人类认知和成本评估的自适应运动规划器，以实现自动驾驶车辆在复杂地形和挑战性场景中的稳定高效行驶。通过构建多层地图描述不同的越野地形特征，并利用 CNN-LSTM 网络学习人类驾驶员在各种越野情景下规划的轨迹，设计了基于原始轨迹和成本权重选择的轨迹规划器，能生成与越野车辆动力学一致的高度自适应、稳定、高效的轨迹。在复杂地形和不同路况的沙漠越野环境中进行实验证明，提出的人类化运动规划器在不同越野条件下具有极好的适应性，实时操作、更大稳定性，并且在多样化和挑战性场景中具备更加人类化的规划能力。