Journal of Sensor Science and Technology (센서학회지)

The Korean Sensors Society (한국센서학회)
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Lane Detection System Based on Vision Sensors Using a Robust Filter for Inner Edge Detection

차선 인접 에지 검출에 강인한 필터를 이용한 비전 센서 기반 차선 검출 시스템

  • Shin, Juseok (Dept. of Autonomous Driving System) ;
  • Jung, Jehan (Dept. of Autonomous Driving System) ;
  • Kim, Minkyu (Dept. of Automation System, Ulsan Campus of KOREA POLYTECHNIC)
  • 신주석 (이인텔리전스 자율주행시스템개발팀) ;
  • 정제한 (이인텔리전스 자율주행시스템개발팀) ;
  • 김민규 (한국폴리텍대학교 울산캠퍼스 자동화시스템과)
  • Received : 2019.04.08
  • Accepted : 2019.05.03
  • Published : 2019.05.31
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Abstract

In this paper, a lane detection and tracking algorithm based on vision sensors and employing a robust filter for inner edge detection is proposed for developing a lane departure warning system (LDWS). The lateral offset value was precisely calculated by applying the proposed filter for inner edge detection in the region of interest. The proposed algorithm was subsequently compared with an existing algorithm having lateral offset-based warning alarm occurrence time, and an average error of approximately 15ms was observed. Tests were also conducted to verify whether a warning alarm is generated when a driver departs from a lane, and an average accuracy of approximately 94% was observed. Additionally, the proposed LDWS was implemented as an embedded system, mounted on a test vehicle, and was made to travel for approximately 100km for obtaining experimental results. Obtained results indicate that the average lane detection rates at day time and night time are approximately 97% and 96%, respectively. Furthermore, the processing time of the embedded system is found to be approximately 12fps.

Keywords

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Fig. 1. System overview

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Fig. 2. Result of the pre-processing phase: (a) Input image, (b) Result of the Gaussian blur, (c) Result of the Canny Edge, (d) Result of the applied look-up table of the lane thickness.

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Fig. 3. Robust filter for detection of the Inner edge: (a) Filter applied to the left lane, (b) Filter applied to the right lane, (c) Specified ROI after select of the ego-lane, (d) Result of applying the proposed filter.

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Fig. 4. Select Ego-Lane: (a) Result of the Hough Transform, (b) Set the candidates for the left and right lanes, (c) Method of the Select Ego-Lane, (d) Result of the Select Ego-Lane.

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Fig. 5. Calculation of Lateral offset: (a) Measurement of the worldcoordinate for the lane width, (b) Measurement of the number of the pixel for the lane width in the image-coordinate.

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Fig. 6. I.MX6Q board

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Fig. 7. System architecture

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Fig. 8. Experimental result at daytime to the straight lines: (a) A shadowy road, (b) A lot of road markings, (c) A lot of vehicles and weak lane marking.

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Fig. 9. Experimental result at night time to the straight lines: (a) Weak lane marking, (b) A lot of road markings, (c) A lot of vehicles.

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Fig. 10. Experimental result at daytime to the curve lines: (a) Weak lane marking, (b) Vehicle exist near the curve lanes, (c) Guard rail on the left side of the curve lane.

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Fig. 11. Experimental result of fail case: (a) Double yellow left lanes, (b) Weak yellow left lane, (c) White lane with severe damage.

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Fig. 12. Lateral offset based departure timing test. (a) White straight lane test using Canny Edge filter, (b) White straight lane test using proposed filter, (c) Yellow straight lane test using proposed filter

Table 1. The result of accuracy of lane detection

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Table 2. The result of the accuracy test for lane departure warning

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