Extended H∞ Filter Adaptation Based on Innovation Sequence for Advanced Ego-Vehicle Motion Estimation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Estimation of vehicle motion is a pivotal requirement for autonomous vehicles. This paper proposes a robust ego-vehicle motion estimation to achieve precise localization and tracking, especially in the case of highly dynamic driving. An extended H ∞ filter, based on a kinematic motion model assuming constant turn-rate and acceleration is used to fuse LiDAR, IMU, and vehicle dynamic sensors’ measurements. Measurements from a real high-performance autonomous race car, the so-called DevBot 2.0, have been used to validate the fusion approach in a Roborace competition and compared to a standard Kalman-filter approach. The proposed estimation concept adapts the H ∞ robustness bound based on the innovation sequence of the filter. This provides very fast tracking when it comes to highly dynamic movement, but still achieves minimal estimation uncertainty in case of stationary conditions with lower innovation. Furthermore, a pure kinematic model is used, which is robust against vehicle parameters, changes in the tire-road conditions, and changes in driving maneuvers. The resulting estimation concept shows outstanding performance for considered autonomous race scenario and can be used for a wide range of different applications, such as highway driving, urban driving, platooning, etc.
Original languageEnglish
Title of host publication2020 IEEE 3rd Connected and Automated Vehicles Symposium, CAVS 2020 - Proceedings
Pages1-5
Number of pages5
ISBN (Electronic)9781728190013
DOIs
Publication statusPublished - Nov 2020
Event3rd IEEE Connected and Automated Vehicles Symposium: CAVS 2020 - Virtual, Victoria, Canada
Duration: 18 Nov 202016 Dec 2020
https://ieeexplore.ieee.org/xpl/conhome/9334548/proceeding

Publication series

Name2020 IEEE 3rd Connected and Automated Vehicles Symposium, CAVS 2020 - Proceedings

Conference

Conference3rd IEEE Connected and Automated Vehicles Symposium
Abbreviated titleIEEE CAVS 2020
CountryCanada
CityVirtual, Victoria
Period18/11/2016/12/20
Internet address

Keywords

  • Advanced Motion Models
  • Autonomous Racing
  • Autonomous Vehicles
  • Extended H Filter
  • Robust Performance
  • Sensor Fusion
  • State Estimation

ASJC Scopus subject areas

  • Information Systems and Management
  • Control and Optimization
  • Computer Networks and Communications
  • Automotive Engineering

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