Abstract
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.
Abstract (translated)
在离散环境中驾驶对于自动驾驶车辆来说具有挑战性，因为复杂和多样化的地形会带来问题。为了确保平稳和高效的行驶，车辆需要考虑并平衡环境因素，例如起伏，粗糙度和障碍，以生成能够适应变化场景的最优轨迹。然而，传统的运动规划器通常使用固定成本函数进行轨迹优化，这使得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变得困难。为了应对这些问题，我们提出了一个基于人类认知和成本评估的离散驾驶运动规划器。首先，我们构建了一个多层地图，描述离散地形的不同特征，包括地形高度，粗糙度，障碍和人工潜在场。然后，我们使用卷积神经网络-长短时记忆网络（CNN-LSTM）学习人类驾驶员在各种离散场景中计划的各种轨迹。接着，根据不同环境下人类生成的轨迹，我们设计了一个基于原型的轨迹规划器，旨在模仿人类轨迹和成本权衡选择，生成与离散车辆动态一致的轨迹。最后，我们计算最优成本权重并选择和扩展行为原型，以生成高度自适应、稳定和高效的轨迹。我们在具有复杂地形和多样公路条件的沙漠离散环境中通过实验验证了所提出方法的有效性。实验结果表明，所提出的具有人类相似运动规划器的自适应性非常好。它显示了在多样和具有挑战性的场景中的实时操作、更高的稳定性和更人类似规划能力。
URL
https://arxiv.org/abs/2404.17820
