research-article

Multivehicle Cooperative Driving Using Cooperative Perception: Design and Experimental Validation

Authors:

Zhuang Jie Chong,

Marcelo H. Ang,

Emilio Frazzoli,

Daniela RusAuthors Info & Claims

IEEE Transactions on Intelligent Transportation Systems, Volume 16, Issue 2

Pages 663 - 680

https://doi.org/10.1109/TITS.2014.2337316

Published: 27 March 2015 Publication History

Abstract

In this paper, we present a multivehicle cooperative driving system architecture using cooperative perception along with experimental validation. For this goal, we first propose a multimodal cooperative perception system that provides see-through, lifted-seat, satellite and all-around views to drivers. Using the extended range information from the system, we then realize cooperative driving by a see-through forward collision warning, overtaking/lane-changing assistance, and automated hidden obstacle avoidance. We demonstrate the capabilities and features of our system through real-world experiments using four vehicles on the road.

References

[1]

L. Merino, F. Caballero, J. M. de Dios, J. Ferruz, and A. Ollero, “ A cooperative perception system for multiple UAVs: Application to automatic detection of forest fires,” J. Field Robot., vol. 23, no. 3/4, pp. 165– 184, Apr. 2006.

[2]

A. Rauch, F. Klanner, R. Rasshofer, and K. Dietmayer, “ Car2X-based perception in a high-level fusion architecture for cooperative perception systems,” in Proc. IEEE Intell. Veh. Symp., pp. 270– 275.

[3]

S.-W. Kim, et al., “ Cooperative perception for autonomous vehicle control on the road: Motivation and experimental results,” in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., .

[4]

S.-W. Kim, et al., “ Multiple vehicle driving control for traffic flow efficiency,” in Proc. IEEE Intell. Veh. Symp., pp. 462– 468.

[5]

B. van Arem, C. J. G. van Driel, and R. Visser, “ The impact of cooperative adaptive cruise control on traffic-flow characteristics,” IEEE Trans. Intell. Transp. Syst., vol. 7, no. 4, pp. 429– 436, Dec. 2006.

Digital Library

[6]

K. Konolige, D. Fox, B. Limketkai, J. Ko, and B. Stewart, “ Map merging for distributed robot navigation,” in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., 2003, vol. 1, pp. 212– 217.

[7]

A. Howard, M. J. Mataric, and G. S. Sukhatme, “ Putting the'I'in'Team': An ego-centric approach to cooperative localization,” in Proc. IEEE Int. Conf. Robot. Autom., 2003, vol. 1, pp. 868– 874.

[8]

I. Rekleitis, G. Dudek, and E. Milios, “ Experiments in free-space triangulation using cooperative localization,” in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., 2003, vol. 2, pp. 1777– 1782.

[9]

N. Trawny, X. S. Zhou, K. X. Zhou, and S. I. Roumeliotis, “ 3D relative pose estimation from distance-only measurements,” in Proc. IEEE/RSJ Int. Conf. IROS, 2007, pp. 1071– 1078.

[10]

G. Dedeoglu, and G. Sukhatme, “ Landmark-based matching algorithm for cooperative mapping by autonomous robots,” in Distributed Autonomous Robotic Systems 4, Tokyo, Japan: Springer-Verlag, 2000, pp. 251– 260.

[11]

W. Huang, and K. Beevers, “ Topological map merging,” Int. J. Robot. Res., vol. 24, no. 8, pp. 601– 613, Aug. 2005.

Digital Library

[12]

A. Birk, and S. Carpin, “ Merging occupancy grid maps from multiple robots,” Proc. IEEE, vol. 94, no. 7, pp. 1384– 1397, Jul. 2006.

[13]

F. Amigoni, S. Gasparini, and M. Gini, “ Merging partial maps without using odometry,” in Multi-Robot Systems. From Swarms to Intelligent Automata Volume III, Amsterdam, The Netherlands: Springer-Verlag, 2005, pp. 133– 144.

[14]

F. Lu, and E. Milios, “ Robot pose estimation in unknown environments by matching 2D range scans,” J. Intell. Robot. Syst., vol. 18, no. 3, pp. 249– 275, Mar. 1997.

Digital Library

[15]

D. Nistér, O. Naroditsky, and J. Bergen, “ Visual odometry,” in Proc. IEEE CVPR, 2004, vol. 1, pp. 652– 659.

[16]

P. J. Besl, and N. D. McKay, “ Method for registration of 3-D shapes,” in Proc. Int. Soc. Opt. Photon., 1992 Robotics-DL Tentative, pp. 586– 606.

[17]

C. Yang, and G. Medioni, “ Object modelling by registration of multiple range images,” Image Vis. Comput., vol. 10, no. 3, pp. 145– 155, Apr. 1992.

Digital Library

[18]

S. Rusinkiewicz, and M. Levoy, “ Efficient variants of the ICP algorithm,” in Proc. 3rd Int. Conf. 3-D Digit. Imag. Model., 2001, pp. 145– 152.

[19]

A. Censi, L. Iocchi, and G. Grisetti, “ Scan matching in the hough domain,” in Proc. IEEE ICRA, 2005, pp. 2739– 2744.

[20]

E. B. Olson, “ Real-time correlative scan matching,” in Proc. IEEE ICRA, 2009, pp. 4387– 4393.

[21]

T. Rofer, “ Using histogram correlation to create consistent laser scan maps,” in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., 2002, vol. 1, pp. 625– 630.

[22]

M. Bosse, and J. Roberts, “ Histogram matching and global initialization for laser-only SLAM in large unstructured environments,” in Proc. IEEE Int. Conf. Robot. Autom., 2007, pp. 4820– 4826.

[23]

F. Pomerleau, F. Colas, R. Siegwart, and S. Magnenat, “ Comparing ICP variants on real-world data sets,” Autonom. Robots, vol. 34, no. 3, pp. 133– 148, Apr. 2013.

Digital Library

[24]

H.-S. Tan, and J. Huang, “ DGPS-based vehicle-to-vehicle cooperative collision warning: Engineering feasibility viewpoints,” IEEE Trans. Intell. Transp. Syst., vol. 7, no. 4, pp. 415– 428, Dec. 2006.

Digital Library

[25]

S. Wender, and K. Dietmayer, “ Extending onboard sensor information by wireless communication,” in Proc. IEEE Intell. Veh. Symp., pp. 535– 540.

[26]

Z. J. Chong, et al., “ Autonomy for mobility on demand,” in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 4235– 4236.

[27]

B. Steux, C. Laurgeau, L. Salesse, and D. Wautier, “ Fade: A vehicle detection and tracking system featuring monocular color vision and radar data fusion,” in Proc. IEEE Intell. Veh. Symp., 2002, vol. 2, pp. 632– 639.

[28]

R. Labayrade, C. Royere, D. Gruyer, and D. Aubert, “ Cooperative fusion for multi-obstacles detection with use of stereovision and laser scanner,” Autonom. Robots, vol. 19, no. 2, pp. 117– 140, Sep. 2005.

Digital Library

[29]

D. Gruyer, A. Cord, and R. Belaroussi, “ Vehicle detection and tracking by collaborative fusion between laser scanner and camera,” in Proc. IEEE/RSJ Int. Conf. IROS, 2013, pp. 5207– 5214.

[30]

O. Ludwig, C. Premebida, U. Nunes, and R. Araújo, “ Evaluation of boosting-SVM and SRM-SVM cascade classifiers in laser and vision-based pedestrian detection,” in Proc. 14th Int. IEEE ITSC, 2011, pp. 1574– 1579.

[31]

Q. Baig, O. Aycard, T. D. Vu, and T. Fraichard, “ Fusion between laser and stereo vision data for moving objects tracking in intersection like scenario,” in Proc. IEEE IV, 2011, pp. 362– 367.

[32]

H. Li, and F. Nashashibi, “ Multi-vehicle cooperative perception and augmented reality for driver assistance: A possibility to see through front vehicle,” in Proc. 14th Int. IEEE ITSC, 2011, pp. 242– 247.

[33]

C. Olaverri-Monreal, P. Gomes, R. Fernandes, F. Vieira, and M. Ferreira, “ The see-through system: A VANET-enabled assistant for overtaking maneuvers,” in Proc. IEEE Intell. Veh. Symp., 2010, pp. 123– 128.

[34]

P. Gomes, C. Olaverri-Monreal, and M. Ferreira, “ Making vehicles transparent through V2V video streaming,” IEEE Trans. Intell. Transp. Syst., vol. 13, no. 2, pp. 930– 938, Jun. 2012.

Digital Library

[35]

A. Vinel, E. Belyaev, K. Egiazarian, and Y. Koucheryavy, “ An overtaking assistance system based on joint beaconing and real-time video transmission,” IEEE Trans. Veh. Technol., vol. 61, no. 5, pp. 2319– 2329, Jun. 2012.

[36]

E. Belyaev, A. Vinel, K. Egiazarian, and Y. Koucheryavy, “ Power control in see-through overtaking assistance system,” IEEE Commun. Lett., vol. 17, no. 3, pp. 612– 615, Mar. 2013.

[37]

A. Girard, J. de Sousa, J. Misener, and J. Hedrick, “ A control architecture for integrated cooperative cruise control and collision warning systems,” in Proc. IEEE Conf. Decis. Control, vol. 2, pp. 1491– 1496.

[38]

S. Kato, S. Tsugawa, K. Tokuda, T. Matsui, and H. Fujii, “ Vehicle control algorithms for cooperative driving with automated vehicles and intervehicle communications,” IEEE Trans. Intell. Transp. Syst., vol. 3, no. 3, pp. 155– 161, Sep. 2002.

Digital Library

[39]

S. Tsugawa, S. Kato, T. Matsui, H. Naganawa, and H. Fujii, “ An architecture for cooperative driving of automated vehicles,” in Proc. IEEE Intell. Transp. Syst., 2000, pp. 422– 427.

[40]

J. Kolodko, and L. Vlacic, “ Cooperative autonomous driving at the intelligent control systems laboratory,” IEEE Intell. Syst., vol. 18, no. 4, pp. 8– 11, Jul./Aug. 2003.

Digital Library

[41]

J. Baber, J. Kolodko, T. Noel, M. Parent, and L. Vlacic, “ Cooperative autonomous driving: Intelligent vehicles sharing city roads,” IEEE Robot. Autom. Mag., vol. 12, no. 1, pp. 44– 49, Mar. 2005.

[42]

W. Liu, S.-W. Kim, Z. J. Chong, X. Shen, and M. H. Ang Jr., “ Motion planning using cooperative perception on urban road,” in Proc. IEEE Int. Conf. Cybern. Intell. Syst., Robot., Autom. Mechantron., pp. 130– 137.

[43]

B. Rebsamen, et al., “ Utilizing the infrastructure to assist autonomous vehicles in a mobility on demand context,” in Proc. IEEE TENCON, pp. 1– 5.

[44]

D. Jiang, and L. Delgrossi, “ IEEE 802.11p: Towards an international standard for wireless access in vehicular environments,” in Proc. IEEE VTC Spring, 2008, pp. 2036– 2040.

[45]

S. Biswas, R. Tatchikou, and F. Dion, “ Vehicle-to-vehicle wireless communication protocols for enhancing highway traffic safety,” IEEE Commun. Mag., vol. 44, no. 1, pp. 74– 82, Jan. 2006.

Digital Library

[46]

A. Rauch, F. Klanner, and K. Dietmayer, “ Analysis of V2X communication parameters for the development of a fusion architecture for cooperative perception systems,” in Proc. IEEE Intell. Veh. Symp., 2011, pp. 685– 690.

[47]

A. Von Arnim, M. Perrollaz, A. Bertrand, and J. Ehrlich, “ Vehicle identification using near infrared vision and applications to cooperative perception,” in Proc. IEEE Intell. Veh. Symp., 2007, pp. 290– 295.

[48]

M. X. Punithan, and S.-W. Seo, “ King's graph-based neighbor-vehicle mapping framework,” IEEE Trans. Intell. Transp. Syst., vol. 14, no. 3, pp. 1313– 1330, Sep. 2013.

Digital Library

[49]

K. S. Arun, T. S. Huang, and S. D. Blostein, “ Least-squares fitting of two 3-D point sets,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-9, no. 5, pp. 698– 700, Sep. 1987.

Digital Library

[50]

K. Konolige, and K. Chou, “ Markov localization using correlation,” in Proc. Int. Joint Conf. Artif. Intell., 1999, pp. 1154– 1159.

[51]

S. Thrun, W. Burgard, and D. Fox, “ A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping,” in Proc. IEEE ICRA, 2000, vol. 1, pp. 321– 328.

[52]

B. Qin, et al., “ Curb-intersection feature based Monte Carlo localization on urban roads,” in Proc. IEEE Int. Conf. Robot. Autom., 2012, pp. 2640– 2646.

[53]

H. A. Mallot, H. H. Blthoff, J. Little, and S. Bohrer, “ Inverse perspective mapping simplifies optical flow computation and obstacle detection,” Biol. Cybern., vol. 64, no. 3, pp. 177– 185, Jan. 1991.

Digital Library

[54]

M. Bertozzi, and A. Broggi, “ Gold: A parallel real-time stereo vision system for generic obstacle and lane detection,” IEEE Trans. Image Process., vol. 7, no. 1, pp. 62– 81, Jan. 1998.

Digital Library

[55]

B. Qin, et al., “ A general framework for road marking detection and analysis,” in Proc. 16th Int. IEEE ITSC, 2013, pp. 619– 625.

[56]

E. Dagan, O. Mano, G. P. Stein, and A. Shashua, “ Forward collision warning with a single camera,” in Proc. IEEE Intell. Veh. Symp., 2004, pp. 37– 42.

[57]

R. Parasuraman, P. Hancock, and O. Olofinboba, “ Alarm effectiveness in driver-centered collision-warning systems,” Ergonomics, vol. 40, no. 3, pp. 390– 399, 1997.

[58]

M. Quigley, et al., “ ROS: An open-source robot operating system,” in Proc. ICRA Workshop Open Source Softw., 2009, vol. 3, no. 2, pp. 1– 6.

[59]

J. L. B. Claraco, Development of Scientific Applications with the Mobile Robot Programming Toolkit. The MRPT Reference book , Málaga, Spain: Mach. Perception Intell. Robot. Lab., Univ. Málaga, 2008.

[60]

Z. Chong, et al., “ Synthetic 2D LIDAR for precise vehicle localization in 3D urban environment,” in Proc. IEEE ICRA, 2013, pp. 1554– 1559.

[61]

S. Karaman, M. R. Walter, A. Perez, E. Frazzoli, and S. Teller, “ Anytime motion planning using the RRT*,” in Proc. IEEE Int. Conf. Robot. Autom., pp. 1478– 1483.

[62]

ITU-R, “ Attenuation by Atmospheric Gases”, Geneva, SwitzerlandP.676-8, Oct. 2009.

[63]

B. Yamauchi, “ All-weather perception for man-portable robots using ultra-wideband radar,” in Proc. IEEE ICRA, 2010, pp. 3610– 3615.

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Multivehicle Cooperative Driving Using Cooperative Perception: Design and Experimental Validation

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cover image IEEE Transactions on Intelligent Transportation Systems

IEEE Transactions on Intelligent Transportation Systems Volume 16, Issue 2

April 2015

535 pages

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IEEE Press

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Published: 27 March 2015

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