Patent application EP21180195A (EP), filed 2021-06-18
Publication number: EP4105818A1
Title: METHOD AND SYSTEM FOR ESTIMATING ROAD LANE GEOMETRY
Abstract: The invention relates to a method for estimating road lane geometry (14). A camera lane estimation comprising at least one camera-estimated lane segment (6), based on camera (3) detection of road markings is provided. Further, a leading vehicle lane estimation comprising at least one leading-vehicle-estimated lane segment (11), based on traces (10) of at least one leading vehicle (9) is provided. Then, the at least one camera-estimated lane segment (6) and the at least one leading-vehicle-estimated lane segment (11) are stitched together to obtain the estimated road lane geometry (14). Further, a system (2) for estimating road lane geometry (14) and a vehicle (1) comprising such a system (2) are provided.
IPC codes: G06K   9/    00            A I
G06V  20/    56            A I
Claims: number: 1
text: Method for estimating road lane geometry (14), the method comprising:
number: 2
text: Method according to claim 1, wherein the at least one camera-estimated lane segment (6) and the at least one leading-vehicle-estimated lane segment (11) are each given by their left edge (7; 12) and their right edge (8; 13) and the left edges (7; 12) and rights edges (8; 13) are given, in particular, as a list of two-dimensional points.
number: 3
text: Method according to claim 1 or 2, wherein the at least one camera-estimated lane segment (6) is obtained by image recognition of road markings on images provided by at least one camera (3) and the road markings are, in particular, at least one out of a group, the group comprising lane markings, curb stones, guide posts and guardrails.
number: 4
text: Method according to any of claims 1 to 3, wherein the at least one leading-vehicle-estimated lane segment (11) is obtained by tracking at least one leading vehicle (9), in particular by camera (3), lidar and/or radar (5), storing temporal location information of said at least one leading vehicle (9) to obtain a trace (10) of the at least one leading vehicle (9) and creating the at least one leading-vehicle-estimated lane segment (11) at lane width around the trace (10) of the at least one leading vehicle (9).
number: 5
text: Method according to any of claims 2 to 4, wherein the at least one camera-estimated lane segment (6) and/or the at least one leading-vehicle-estimated lane segment (11) is smoothened, in particular by making a polynomial fit to the list of two-dimensional points and then sampling the fitted data back to a list of two-dimensional points.
number: 6
text: Method according to any of claims 1 to 5, wherein the at least one camera-estimated lane segment (6) and/or the at least one leading-vehicle-estimated lane segment (11) are extrapolated, in particular until they reach and/or overlap with an adjacent lane segment.
number: 7
text: Method according to any of claims 1 to 6, wherein adjacent lane segments are stitched together if the overlap of the adjacent lane segments in a direction perpendicular to a driving direction of the lane is greater than 50 %, preferably greater than 65 %, more preferably greater than 80 %.
number: 8
text: Method according to any of claims 1 to 7, wherein a stitching quality measure is assigned to each of the stitches of the adjacent lane segments, in particular based on the overlap of the adjacent lane segments in a direction perpendicular to the driving direction of the lane.
number: 9
text: Method according to any of claims 1 to 8, wherein an extra road lane is added if the overlap of a leading-vehicle-estimated lane segment (11) with other lane segments in a direction perpendicular to the driving direction of the lane is less than 15 %, preferably less than 5 %, more preferably 0 %.
number: 10
text: Method according to any of claims 1 to 9, further comprising providing a road boundary (15) estimation based on radar (5) detection of road boundaries and rejecting leading-vehicle-estimated lane segments (11) that lie more than 25 %, preferably more than 15 %, more preferably more than 5 % outside the estimated road boundary (15).
number: 11
text: Method according to any of claims 1 to 10, further comprising broadcasting the estimated road lane geometry (14), in particular to an advanced driver assistance system.
number: 12
text: System for estimating road lane geometry (14), the system (2) comprising:
number: 13
text: Vehicle, comprising a system (2) for estimating road lane geometry (14) according to claim 12.
Office action: search_report, dated 2022-12-21
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ref_id: - REN JIANQIANG ET AL: "Lane Detection in Video-Based Intelligent Transportation Monitoring via Fast Extracting and Clustering of Vehicle Motion Trajectories", MATHEMATICAL PROBLEMS IN ENGINEERING, vol. 2014, 1 January 2014 (2014-01-01), CH, pages 1 - 12, XP055861690, ISSN: 1024-123X, Retrieved from the Internet <URL:https://downloads.hindawi.com/journals/mpe/2014/156296.pdf> DOI: 10.1155/2014/156296
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ref_id: - GUPTA ANY ET AL, "A Framework for Lane Prediction Based on Vehicle Detection and Tracking : 7th National Conference, NCVPRIPG 2019, Hubballi, India, December 22-24, 2019, Revised Selected Papers", MEDICAL IMAGE UNDERSTANDING AND ANALYSIS, 21ST ANNUAL CONFERENCE, MIUA 2017, EDINBURGH, UK, JULY 11-13, 2017, DE, vol. 1249, doi:10.1007/978-981-15-8697-2_54, ISSN 1865-0929, (20200101), pages 578 - 588, URL: http://link.springer.com/content/pdf/10.1007/978-981-15-8697-2_54, XP55861963 [I] 1,3-5,10-13 * abstract; figures 1,2 * * sec. 3.2, 3.3, 3.4 *
ref_type: npl
severity: background
ref_id: - ALEXEY ABRAMOV ET AL, "Multi-Lane Perception Using Feature Fusion Based on GraphSLAM", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, (20170614), doi:10.1109/IROS.2016.7759481, XP080769694 [I] 1,3-5,10-13 * abstract; figures 1,2,7 *
ref_type: npl
severity: background
ref_id: - GUO CHUNZHAO ET AL, "Improved lane detection based on past vehicle trajectories", 17TH INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), IEEE, doi:10.1109/ITSC.2014.6957992, (20141008), pages 1956 - 1963, (20141114), XP032685535 [A] 1-13 * the whole document *
ref_type: npl
severity: background
ref_id: - REN JIANQIANG ET AL, "Lane Detection in Video-Based Intelligent Transportation Monitoring via Fast Extracting and Clustering of Vehicle Motion Trajectories", MATHEMATICAL PROBLEMS IN ENGINEERING, CH, vol. 2014, doi:10.1155/2014/156296, ISSN 1024-123X, (20140101), pages 1 - 12, URL: https://downloads.hindawi.com/journals/mpe/2014/156296.pdf, XP055861690 [A] 1-13 * the whole document *
ref_type: npl
severity: background
Final disposition: pending (2022-12-21)