Semantic segmentation of urban scenes is an essential component in various applications of autonomous driving. It makes great progress with the rise of deep learning technologies. Most of the current semantic segmentation networks use single-modal sensory data, which are usually the RGB images produced by visible cameras. However, the segmentation performance of these networks is prone to be degraded when lighting conditions are not satisfied, such as dim light or darkness. We find that thermal images produced by thermal imaging cameras are robust to challenging lighting conditions. Therefore, in this article, we propose a novel RGB and thermal data fusion network named FuseSeg to achieve superior performance of semantic segmentation in urban scenes. The experimental results demonstrate that our network outperforms the state-of-the-art networks.

Drifting is a complicated task for autonomous vehicle control. Most traditional methods in this area are based on motion equations derived by the understanding of vehicle dynamics, which is difficult to be modeled precisely. We propose a robust drift controller without explicit motion equations, which is based on the latest model-free deep reinforcement learning algorithm soft actor-critic. The drift control problem is formulated as a trajectory following task, where the errorbased state and reward are designed. After being trained on tracks with different levels of difficulty, our controller is capable of making the vehicle drift through various sharp corners quickly and stably in the unseen map. The proposed controller is further shown to have excellent generalization ability, which can directly handle unseen vehicle types with different physical properties, such as mass, tire friction, etc.

Visual Simultaneous Localization and Mapping (visual SLAM) has attracted more and more researchers in recent decades and many state-of-the-art algorithms have been proposed with rather satisfactory performance in static scenarios. However, in dynamic scenarios, the performance of current visual SLAM algorithms degrades significantly due to the disturbance of the dynamic objects. To address this problem, we propose a novel method which uses optical flow to distinguish and eliminate the dynamic feature points from the extracted ones using RGB images as the only input. The static feature points are fed into the visual SLAM system for the camera pose estimation. We integrate our method with the original ORB-SLAM system and validate the proposed method with the challenging dynamic sequences from the TUM dataset and our recorded office dataset. The whole system can work in real time. Qualitative and quantitative evaluations demonstrate that our method significantly improves the performance of ORB-SLAM in dynamic scenarios.