With the rapid development of Pattern Recognition and Computer Vision
technologies, tasks like object detection or semantic segmentation have
achieved even better accuracy than human beings. Based on these solid
foundations, autonomous driving is becoming an important research direction,
aiming to revolute the future of transportation and mobility. Sensors are
critical to autonomous driving's security and feasibility to perceive the
surrounding environment. Multi-Sensor fusion has become a current research hot
spot because of its potential for multidimensional perception and integration
ability. In this paper, we propose a novel feature-level multi-sensor fusion
technology for end-to-end autonomous driving navigation with imitation
learning. Our paper mainly focuses on fusion technologies for Lidar and RGB
information. We also provide a brand-new penalty-based imitation learning
method to reinforce the model's compliance with traffic rules and unify the
objective of imitation learning and the metric of autonomous driving.

本文提出了一种新颖的基于特征融合的多传感器融合技术，结合仿真学习实现端到端自动驾驶导航。论文的重点在于 Lidar 和 RGB 信息的融合技术，同时提出基于惩罚的仿真学习方法，以加强模型对交通规则的遵守，并统一仿真学习和自动驾驶指标的目标。