通过模仿周围车辆学习驾驶
Learning to Drive by Imitating Surrounding Vehicles
AI总结：
本文提出利用周围车辆轨迹作为额外演示的模仿学习数据增强策略，通过车辆选择采样与过滤提升自动驾驶性能，仅用10%数据即可达到或超过完整数据集效果，降低碰撞率并改善安全指标。
摘要：
模仿学习是一种通过模仿专家驾驶员行为来训练自动驾驶车辆（AV）在复杂交通环境中导航的有前景的方法。虽然现有的模仿学习框架侧重于利用专家演示，但它们往往忽视了来自周围交通参与者的额外复杂驾驶数据的潜力。在本文中，我们研究了一种数据增强策略，该策略利用自动驾驶车辆传感器捕获的附近车辆的观测轨迹作为额外的演示。我们引入了一种简单的车辆选择采样和过滤策略，该策略优先考虑信息丰富且多样化的驾驶行为，从而为训练贡献更丰富的数据集。我们使用具有代表性的基于学习的规划器在大型真实世界数据集上评估了这一想法，并展示了在复杂驾驶场景中的性能提升。具体来说，与基线相比，该方法降低了碰撞率并改善了安全指标。值得注意的是，即使仅使用原始数据集的10%，该方法的性能也能达到或超过完整数据集。通过消融实验，我们分析了选择标准，并表明简单的随机选择可能会降低性能。我们的研究结果突显了在模仿学习中利用多样化真实世界轨迹数据的价值，并为自动驾驶的数据增强策略提供了见解。
英文摘要：
Imitation learning is a promising approach for training autonomous vehicles (AV) to navigate complex traffic environments by mimicking expert driver behaviors. While existing imitation learning frameworks focus on leveraging expert demonstrations, they often overlook the potential of additional complex driving data from surrounding traffic participants. In this paper, we study a data augmentation strategy that leverages the observed trajectories of nearby vehicles, captured by the AV's sensors, as additional demonstrations. We introduce a simple vehicle-selection sampling and filtering strategy that prioritizes informative and diverse driving behaviors, contributing to a richer dataset for training. We evaluate this idea with a representative learning-based planner on a large real-world dataset and demonstrate improved performance in complex driving scenarios. Specifically, the approach reduces collision rates and improves safety metrics compared to the baseline. Notably, even when using only 10 percent of the original dataset, the method matches or exceeds the performance of the full dataset. Through ablations, we analyze selection criteria and show that naive random selection can degrade performance. Our findings highlight the value of leveraging diverse real-world trajectory data in imitation learning and provide insights into data augmentation strategies for autonomous driving.
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。
