论文标题
概率的端到端车辆在具有多模式传感器融合的复杂动态环境中
Probabilistic End-to-End Vehicle Navigation in Complex Dynamic Environments with Multimodal Sensor Fusion
论文作者
论文摘要
全天和全天候导航是自动驾驶的关键能力，这需要对各种环境条件和复杂的代理行为进行适当的反应。最近，随着深度学习的兴起，对自动驾驶汽车的端到端控制进行了充分的研究。但是，大多数作品仅基于视觉信息，这些信息可以通过挑战性的照明条件（例如昏暗的光或完全黑暗）来降低。此外，它们通常会生成并应用确定性控制命令，而无需考虑将来的不确定性。在本文中，基于模仿学习，我们提出了一种利用相机，LiDar和Radar中信息的概率驾驶模型。我们进一步在我们的新驾驶基准上在线评估其驾驶性能，其中包括各种环境条件（例如，城市和农村地区，交通密度，一天中的天气和时间）和动态障碍（例如，车辆，行人，摩托车手和骑自行车的人）。结果表明，我们提出的模型优于基线，并且在繁重的交通和极端天气的看不见的环境中实现了出色的概括性能。
All-day and all-weather navigation is a critical capability for autonomous driving, which requires proper reaction to varied environmental conditions and complex agent behaviors. Recently, with the rise of deep learning, end-to-end control for autonomous vehicles has been well studied. However, most works are solely based on visual information, which can be degraded by challenging illumination conditions such as dim light or total darkness. In addition, they usually generate and apply deterministic control commands without considering the uncertainties in the future. In this paper, based on imitation learning, we propose a probabilistic driving model with ultiperception capability utilizing the information from the camera, lidar and radar. We further evaluate its driving performance online on our new driving benchmark, which includes various environmental conditions (e.g., urban and rural areas, traffic densities, weather and times of the day) and dynamic obstacles (e.g., vehicles, pedestrians, motorcyclists and bicyclists). The results suggest that our proposed model outperforms baselines and achieves excellent generalization performance in unseen environments with heavy traffic and extreme weather.
