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

Jihun Kim; Ziga Emersic; Dong Seog Han

doi:10.1109/ICAIIC.2019.8669081

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

Jihun Kim
, Ziga Emersic
, Dong Seog Han

School of Electronics Engineering

Kyungpook National University
University of Ljubljana

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

19 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 language	English
Title of host publication	1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	267-271
Number of pages	5
ISBN (Electronic)	9781538678220
DOIs	https://doi.org/10.1109/ICAIIC.2019.8669081
State	Published - 18 Mar 2019
Event	1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019 - Okinawa, Japan Duration: 11 Feb 2019 → 13 Feb 2019

Publication series

Name	1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019

Conference

Conference	1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019
Country/Territory	Japan
City	Okinawa
Period	11/02/19 → 13/02/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

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

Access to Document

10.1109/ICAIIC.2019.8669081

Cite this

Kim, J., Emersic, Z., & Han, D. S. (2019). Vehicle Path Prediction based on Radar and Vision Sensor Fusion for Safe Lane Changing. In 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019 (pp. 267-271). Article 8669081 (1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAIIC.2019.8669081

Kim, Jihun ; Emersic, Ziga ; Han, Dong Seog. / Vehicle Path Prediction based on Radar and Vision Sensor Fusion for Safe Lane Changing. 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 267-271 (1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019).

@inproceedings{21054f59ce40454c97bc173e9dc6b979,

title = "Vehicle Path Prediction based on Radar and Vision Sensor Fusion for Safe Lane Changing",

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.",

keywords = "Advanced Driver Assistant System, Lane Change System, Radar, Sensor Fusion., Vision",

author = "Jihun Kim and Ziga Emersic and Han, \{Dong Seog\}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019 ; Conference date: 11-02-2019 Through 13-02-2019",

year = "2019",

month = mar,

day = "18",

doi = "10.1109/ICAIIC.2019.8669081",

language = "English",

series = "1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "267--271",

booktitle = "1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019",

address = "United States",

}

Kim, J, Emersic, Z & Han, DS 2019, Vehicle Path Prediction based on Radar and Vision Sensor Fusion for Safe Lane Changing. in 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019., 8669081, 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019, Institute of Electrical and Electronics Engineers Inc., pp. 267-271, 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019, Okinawa, Japan, 11/02/19. https://doi.org/10.1109/ICAIIC.2019.8669081

Vehicle Path Prediction based on Radar and Vision Sensor Fusion for Safe Lane Changing. / Kim, Jihun; Emersic, Ziga; Han, Dong Seog.
1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 267-271 8669081 (1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Vehicle Path Prediction based on Radar and Vision Sensor Fusion for Safe Lane Changing

AU - Kim, Jihun

AU - Emersic, Ziga

AU - Han, Dong Seog

PY - 2019/3/18

Y1 - 2019/3/18

N2 - 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.

AB - 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.

KW - Advanced Driver Assistant System

KW - Lane Change System

KW - Radar

KW - Sensor Fusion.

KW - Vision

UR - https://www.scopus.com/pages/publications/85063897348

U2 - 10.1109/ICAIIC.2019.8669081

DO - 10.1109/ICAIIC.2019.8669081

M3 - Conference contribution

AN - SCOPUS:85063897348

T3 - 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019

SP - 267

EP - 271

BT - 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019

Y2 - 11 February 2019 through 13 February 2019

ER -

Kim J, Emersic Z, Han DS. Vehicle Path Prediction based on Radar and Vision Sensor Fusion for Safe Lane Changing. In 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 267-271. 8669081. (1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019). doi: 10.1109/ICAIIC.2019.8669081

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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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