Robust lane tracking algorithm for forward target detection of automated driving vehicles

Abstract

Detection of a primary forward target is one of the most important factors in a longitudinal control system of automated driving vehicles. If there is no special event such as a lane change, a lane detected by a vision sensor can be deemed as a predicted driving path of the ego vehicle. Based on this, it is possible to determine the state of the primary forward target vehicle accurately using the detected lanes. However, malfunction of vision sensors can induce unrecognized and misrecognized lane detection problems on account of internal/external environment factors. Furthermore, if a detection point is getting farther from the subject vehicle, the detection accuracy is getting lower because of inaccuracy of lane model parameters. To solve these problems, a novel method of lane tracking has been investigated. First, an integrated sensor module that combines a virtual sensor and a vision sensor has been developed. The virtual sensor is combined kinematic and dynamic vehicle model to be used in the full-speed range. Second, a lane estimator to improve lane detection accuracy at a long distance has been developed. The lane width in the public road can be assumed to be constant in the same road type. Based on this assumption, the clothoid parameters can be restored and consequently improve the lane detection accuracy. The performance of the proposed algorithm was verified by actual vehicle tests on public roads with manual driving and on proving ground with an automated driving system. The proposed algorithm has been compared with a conventional method which is based on in-vehicle yaw rate sensor. The test results have shown significant improvement of lane tracking performance over the conventional method.

Keywords

Lane tracking lane estimator fail safe analytical sensor redundancy forward target decision

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References

Razaei

Klette

Computer vision for driver assistance: simultaneous traffic and driver monitoring. New York: Springer, 2017, pp.1–18.

SAE J3016_201609. Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles.

Jochen

. Consolidated Document after 6th session. In: Automatically Commanded Steering Function (ACSF), Working Party on Brakes and Running Gear (GRRF), Home Vehicle Regulations, Transport - Vehicle Regulations, 28 June 2016. United Nations Economic Commission for Europe(UNECE), https://wiki.unece.org/display/trans/ACSF+6th+session.

Realpe

Vintimilla

Vlacic

Multi-sensor fusion module in a fault tolerant perception system for autonomous vehicles. J Automat Control Eng 2016; 4: 430–436.

Heimes

Nagel

Towards active machine-vision-based driver assistance for urban areas. Int J Comput Vision 2002; 50: 5–34.

Clanton

Bebly

Hodel

A low-cost solution for an integrated multisensor lane departure warning system. IEEE T Intell Transp Syst 2009; 10: 47–59.

Son

Kim

Lee

et al . Predictive virtual lane method using relative motions between a vehicle and lanes. Int J Control Autom 2015; 13: 146–155.

Nedevschi

Schmidt

Graf

et al . 3D lane detection system based on stereovision. In: Proceedings of 7th IEEE conference on intelligent transportation systems, Washington, DC, 3–6 October 2004, pp.161–166. New York: IEEE.

Gong

Wang

et al . A lane marking detection and tracking algorithm based on sub-regions. In: International conference on informative and cybernetics for computational social systems (ICCSS), Qingdao, China, 9–10, October 2014. New York: IEEE.

10.

McCall

Trivedi

Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation. IEEE T Intell Transp Syst 2006; 7: 20–37.

11.

Gackstatter

Thomas

Heinemann

et al . Advanced microsystems for automotive applications. Berlin: Springer, 2010, pp.133–143.

12.

Southall

Taylor

Stochastic road shape estimation. In: Proceedings of eighth international conference on computer vision, Vancouver, BC, Canada, 7–14 July 2001, pp.205–212. New York: IEEE.

13.

Apostoloff

Zelinsky

Robust vision based lane tracking using multiple cues and particle filtering. In: Proceedings of IEEE intelligent vehicles symposium, Columbus, OH, 9–11 June 2003, pp.558–563. New York: IEEE.

14.

Lotfy

Kassem

Nassief

et al . Lane departure warning tracking system based on score mechanism. In: 59th IEEE international Midwest symposium on circuits and systems (MWSCAS), Abu Dhabi, UAE, 16–19 October 2016. New York: IEEE.

15.

Dickmanns

Mysliwetz

Recursive 3-D road and relative ego-state recognition. IEEE T Pattern Anal 1992; 14: 119–213.

16.

Song

Kim

et al . A novel approach to the enhancement of lane estimator by using Kalman filter. In: 17th international conference on control, automation and systems (ICCAS), Jeju, South Korea, 18–21 October 2017. New York: IEEE.

17.

Chapuis

Aufrere

Chasusse

Recovering the 3D shape of a road by vision. In: Seventh international conference on image processing and its applications, Manchester, 13–15 July 1999. New York: IEEE.

18.

Rajamani

Vehicle dynamics and control. New York: Springer, 2005.

19.

Isermann

Fault-diagnosis systems. New York: Springer, 2006.