Abstract

In recent years, many methods of safe vehicle navigation and partial motion planning (PMP) have been proposed in vehicular ad-hoc network (VANET) field. In order to improve the limitation of traditional PMP, this paper presents a novel effective way to plan motion with cooperation of roadside fixed sensors (RFSs). With their cooperation, the vehicles can get the surrounding information quickly and effectively, and give highly accurate projections about the near future conditions on road. After proposing our algorithm, the worst case is analyzed and methods are found to solve the problem. Finally we conduct one elemental contrast experiment, driver situation awareness, with or without the “cooperation” of RFSs in highway scenarios. The result shows that the vehicles can make a better PMP based on the forward conditions received from RFSs, and extend the warning distance obviously when emergency happens.

Highlights

  • It is a dream of human beings to achieve autonomous mobile vehicles since the invention of cars

  • The 2007 DARPA Urban Challenge presented the fact that there are plenty of works to do if achieving fully autonomous driving especially in urban environment with the extremely complicated conditions, such as traffic lights and pedestrians, and safe navigation is a huge problem

  • Traditional safe navigation of partial motion planning is based on single cars

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Summary

Introduction

It is a dream of human beings to achieve autonomous mobile vehicles since the invention of cars. With the rapid development of science and technology, and through the persistent and unremitting efforts by scientists, many novel tools which are equipped with modern technologies are used in cars such as GPS navigators and PSD sensors. By using those tools, unnecessary accidents can be avoided and destination can be achieved as well [1]. There are two main paradigms in partial motion planning [4] They are the plans based on prior information and real time information.

Previous Work
Limitation
An Effective Improved Method: the “Cooperation” with RFSs
Algorithms to Achieve “Cooperation”
Algorithm of “Cooperation”
Worst Case
Experiments in Highway Scenarios
Contrast Experiments
Conclusions and Future Work
Full Text
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