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.

翻译：越野环境因地形复杂多变，对自动驾驶车辆构成严峻挑战。为确保稳定高效行驶，车辆需综合考虑和平衡起伏度、粗糙度、障碍物等环境因素，生成能适应动态场景的最优轨迹。然而，传统运动规划器多采用固定代价函数进行轨迹优化，难以在复杂不规则地形与罕见场景中适配不同驾驶策略。针对上述问题，我们提出一种基于类人认知与代价评估的越野驾驶自适应运动规划器。首先，构建描述越野地形多维度特征的多层地图，包含地形高程、粗糙度、障碍物及人工势场图。其次，采用CNN-LSTM网络学习人类驾驶员在多种越野场景下的轨迹规划模式。接着，基于类人在不同环境中生成的轨迹，设计基元式轨迹规划器，通过模仿人类轨迹与代价权重选择，生成符合越野车辆动力学的轨迹。最后，计算最优代价权重，选择并扩展行为基元，生成强自适应、高稳定性与高效率的轨迹。通过在沙漠越野环境中复杂地形与多变道路条件的实验，验证了所提方法的有效性。实验结果表明，所提出的类人运动规划器对不同越野条件具有卓越适应性，在多样化挑战场景中展现了实时运行能力、更高稳定性及更强的类人规划能力。