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。

基于惩罚的仿生学习，融合跨语义生成传感器融合于自主驾驶

Penalty-Based Imitation Learning With Cross Semantics Generation Sensor Fusion for Autonomous Driving

Environmental disturbances, such as sensor data noises, various lighting
conditions, challenging weathers and external adversarial perturbations, are
inevitable in real self-driving applications. Existing researches and testings
have shown that they can severely influence the vehicles perception ability and
performance, one of the main issue is the false positive detection, i.e., the
ghost object which is not real existed or occurs in the wrong position (such as
a non-existent vehicle). Traditional navigation methods tend to avoid every
detected objects for safety, however, avoiding a ghost object may lead the
vehicle into a even more dangerous situation, such as a sudden break on the
highway. Considering the various disturbance types, it is difficult to address
this issue at the perceptual aspect. A potential solution is to detect the
ghost through relation learning among the whole scenario and develop an
integrated end-to-end navigation system. Our underlying logic is that the
behavior of all vehicles in the scene is influenced by their neighbors, and
normal vehicles behave in a logical way, while ghost vehicles do not. By
learning the spatio-temporal relation among surrounding vehicles, an
information reliability representation is learned for each detected vehicle and
then a robot navigation network is developed. In contrast to existing works, we
encourage the network to learn how to represent the reliability and how to
aggregate all the information with uncertainties by itself, thus increasing the
efficiency and generalizability. To the best of the authors knowledge, this
paper provides the first work on using graph relation learning to achieve
end-to-end robust navigation in the presence of ghost vehicles. Simulation
results in the CARLA platform demonstrate the feasibility and effectiveness of
the proposed method in various scenarios.

为解决自动驾驶中的幽灵物体误检问题，本文提出了一种基于关系学习的端到端导航系统，利用空间 - 时间关系进行信息可信度建模，并优化导航网络以达到鲁棒性。

征服幽灵：关系学习，用于信息可靠性表征与端到端鲁棒导航

Conquering Ghosts: Relation Learning for Information Reliability Representation and End-to-End Robust Navigation

Learning from demonstration for motion planning is an ongoing research topic.
In this paper we present a model that is able to learn the complex mapping from
raw 2D-laser range findings and a target position to the required steering
commands for the robot. To our best knowledge, this work presents the first
approach that learns a target-oriented end-to-end navigation model for a
robotic platform. The supervised model training is based on expert
demonstrations generated in simulation with an existing motion planner. We
demonstrate that the learned navigation model is directly transferable to
previously unseen virtual and, more interestingly, real-world environments. It
can safely navigate the robot through obstacle-cluttered environments to reach
the provided targets. We present an extensive qualitative and quantitative
evaluation of the neural network-based motion planner, and compare it to a
grid-based global approach, both in simulation and in real-world experiments.

本文提出了一种基于神经网络的运动规划方法，使用学习演示来训练机器人在动态障碍物环境下进行端到端导航，此方法可通过原始 2D 激光测距数据将目标位置映射到机器人的导航命令。模型已通过模拟和实际测试的方式进行了有效验证与比较。