Vehicle Path Prediction based on Radar and Vision Sensor Fusion for Safe Lane Changing

Jihun Kim, Ziga Emersic, Dong Seog Han

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Scopus citations

Abstract

Reported traffic accidents often occur due to rear-view blind spots. While there are many existing commercial solutions available, there is still many possible improvements. To address open issues we propose a novel approach to safe lane changing, based on radar and vision sensor fusion, which offers good accuracy with small footprint and fast performance. In the vehicle's surrounding environment we perform deep-learning-based vehicle detection and recognition. Each vehicle is then tracked across the video sequence, with linear Kalman filter used for the spatio-Temporal constraint in path prediction. Our approach achieves an accuracy of 95% in the path estimation of a vehicle approaching a blind spot.

Original languageEnglish
Title of host publication1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages267-271
Number of pages5
ISBN (Electronic)9781538678220
DOIs
StatePublished - 18 Mar 2019
Event1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019 - Okinawa, Japan
Duration: 11 Feb 201913 Feb 2019

Publication series

Name1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019

Conference

Conference1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019
Country/TerritoryJapan
CityOkinawa
Period11/02/1913/02/19

Keywords

  • Advanced Driver Assistant System
  • Lane Change System
  • Radar
  • Sensor Fusion.
  • Vision

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