  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.
