Bo Cheng

TL;DR: This method is based on convolutional neural network (CNN) and image upsampling theory and can obtain informative feature representation for object classification in autonomous vehicle environment using the integrated vision and LIDAR data.

TL;DR: In this article, a conditional likelihood maximization method was employed to extract typical maneuver transition patterns that could represent driving style strategies, from the 144 maneuver transition probabilities obtained by the random forest algorithm.

TL;DR: A drowsiness on-line detection system for monitoring driver fatigue level under real driving conditions, based on the data of steering wheel angles collected from sensors mounted on the steering lever, confirms that the proposed method based on SWA signal is valuable for applications in preventing traffic accidents caused by driver fatigue.

TL;DR: This study presents a hierarchical reinforcement learning method for decision making of self-driving cars, which does not depend on a large amount of labelled driving data and comprehensively considers both high-level manoeuvre selection and low-level motion control in both lateral and longitudinal directions.

TL;DR: This paper focuses on eco-departure operations of connected vehicles equipped with an internal combustion engine and a step-gear automatic transmission and proposes a near-optimal departing strategy to quickly determine the behavior of the engine and transmission.