NDLI: Application of particle filter tracking algorithm in autonomous vehicle navigation

Content Provider	IEEE Xplore Digital Library
Author	Li, K.R. Lin, G.T. Lee, L.Y. Juang, J.C.
Copyright Year	2013
Description	Author affiliation: Electr. Eng. Dept., Nat. Cheng Kung Univ., Tainan, Taiwan (Li, K.R.; Lin, G.T.; Lee, L.Y.; Juang, J.C.)
Abstract	The paper describes the design, implementation, and test of an autonomous vehicle navigation system using vehicle model and particle filter tracking algorithm. Typically, a vehicle navigation system comprises of real-time environment perception, vehicle localization, collision avoidance, path planning, and path following. In order to achieve the features for intelligent autonomous vehicle, a sensor suite of integrated inertial measurement unit (IMU), GNSS receiver, and incremental encoder is developed for vehicle position estimation. A map-aided path planning strategy is employed to generate a reference route. To this end, a UMI (User Machine Interface) is developed to facilitate the observation of a goal-oriented path tracking situation. The system utilizes particle filter algorithm to guide the vehicle following the planned path in terms of vehicle estimation control. The recursive particle filter is able to weight the cells and response the angle as well as estimated position information. All the sensors are integrated into an embedded computer platform and able to assess the autonomous driving capability. The test is conducted on campus by installing the sensor suite and embedded computer platform into an electricintegrated inertial measurement unit vehicle.
Starting Page	250
Ending Page	255
File Size	7365240
Page Count	6
File Format	PDF
e-ISBN	9781479923830
DOI	10.1109/CACS.2013.6734141
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-02
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Estimation Particle filters Trajectory Mobile robots Vehicles Global Positioning System
Content Type	Text
Resource Type	Article

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