基于类人认知与权重自适应的越野自动驾驶运动规划
Motion planning for off-road autonomous driving based on human-like cognition and weight adaptation
AI总结：
针对越野自动驾驶中固定代价函数难以适应复杂地形的问题，提出基于类人认知与权重自适应的运动规划器，通过构建多层地图并利用CNN-LSTM学习人类驾驶轨迹，生成高适应性、稳定且高效的轨迹。
摘要：
由于复杂多变的地形，在越野环境中驾驶对自动驾驶车辆而言是一项挑战。为确保稳定高效的行驶，车辆需要考虑并平衡起伏、粗糙度和障碍物等环境因素，以生成能够适应不断变化场景的最优轨迹。然而，传统运动规划器通常采用固定代价函数进行轨迹优化，难以在具有挑战性的不规则地形和罕见场景中适应不同的驾驶策略。为解决这些问题，我们提出了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，我们构建了描述越野地形不同特征的多层地图，包括地形高程图、粗糙度图、障碍物图和人工势场图。随后，我们采用CNN-LSTM网络学习人类驾驶员在各种越野场景下规划的轨迹。接着，基于不同环境下的类人生成轨迹，我们设计了一个基于基元的轨迹规划器，旨在模仿人类轨迹和代价权重选择，生成与越野车辆动力学一致的轨迹。最后，我们计算最优代价权重，并选择和扩展行为基元，以生成具有高度适应性、稳定性和高效性的轨迹。我们通过在具有复杂地形和多变道路条件的沙漠越野环境中进行实验，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的越野条件具有出色的适应性。它在多样且具挑战性的场景中展现出实时运行能力、更高的稳定性以及更类人的规划能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
