Abstract
The multitarget vehicle tracking and motion state estimation are crucial for controlling the host vehicle accurately and preventing collisions. However, current multitarget tracking methods are inconvenient to deal with multivehicle issues due to the dynamically complex driving environment. Driving environment perception systems, as an indispensable component of intelligent vehicles, have the potential to solve this problem from the perspective of image processing. Thus, this study proposes a novel driving environment perception system of intelligent vehicles by using deep learning methods to track multitarget vehicles and estimate their motion states. Firstly, a panoramic segmentation neural network that supports end-to-end training is designed and implemented, which is composed of semantic segmentation and instance segmentation. A depth calculation model of the driving environment is established by adding a depth estimation branch to the feature extraction and fusion module of the panoramic segmentation network. These deep neural networks are trained and tested in the Mapillary Vistas Dataset and the Cityscapes Dataset, and the results showed that these methods performed well with high recognition accuracy. Then, Kalman filtering and Hungarian algorithm are used for the multitarget vehicle tracking and motion state estimation. The effectiveness of this method is tested by a simulation experiment, and results showed that the relative relation (i.e., relative speed and distance) between multiple vehicles can be estimated accurately. The findings of this study can contribute to the development of intelligent vehicles to alert drivers to possible danger, assist drivers’ decision-making, and improve traffic safety.
Highlights
Driver inattention is one of the leading causes of traffic accidents
To achieve a precise detection and tracking result, this study proposes a multivehicle tracking and motion state estimation method based on visual perception systems
Current studies on vehicle detection and tracking show the following: (1) e estimation of vehicle position acquired by Lidar sensor may be inaccurate over time
Summary
Driver inattention is one of the leading causes of traffic accidents. It was reported that approximately 80 percent of vehicle crashes and 65 percent of near-crashes involved driver inattention within three seconds prior to the incident in the USA (National Highway Traffic Safety Administration (NHTSA)) [1]. Erefore, this study aims to develop a novel driving environment perception system of intelligent vehicles to track multitarget vehicles and estimate their motion states, which can alert drivers to possible danger, assist drivers’ decision-making, and improve traffic safety. Us, we introduce current studies from two aspects: (1) multitarget vehicle tracking methods for estimating the position and speed of moving vehicles and (2) driving environment perception systems, which recognize vehicles in the forward driving scenario through panoramic segmentation and calculate the distance between vehicles through depth estimation. Li et al [18] first recognized the front vehicles through a semantic segmentation network, determined different vehicle instances according to the connectivity of the segmented vehicle area, and used monocular ranging and Kalman filtering to determine the vehicle’s position and speed This method still can be improved from some aspects. To fill this research gap, a novel multitarget vehicle trajectory tracking system based on image segmentation neural networks was presented in our study
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call