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Localization and Perception for Control and Decision Making of a Low Speed Autonomous Shuttle in a Campus Pilot Deployment
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 03, 2018 by SAE International in United States
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Future SAE Level 4 and Level 5 autonomous vehicles will require novel applications of localization, perception, control and artificial intelligence technology in order to offer innovative and disruptive solutions to current mobility problems. This paper concentrates on low speed autonomous shuttles that are transitioning from being tested in limited traffic, dedicated routes to being deployed as SAE Level 4 automated driving vehicles in urban environments like college campuses and outdoor shopping centers within smart cities. The Ohio State University has designated a small segment in an underserved area of campus as an initial autonomous vehicle (AV) pilot test route for the deployment of low speed autonomous shuttles. This paper presents initial results of ongoing work on developing solutions to the localization and perception challenges of this planned pilot deployment. The paper treats autonomous driving with real time kinematics GPS (Global Positioning Systems) with an inertial measurement unit (IMU), combined with simultaneous localization and mapping (SLAM) with three-dimensional light detection and ranging (LIDAR) sensor, which provides solutions to scenarios where GPS is not available or a lower cost and hence lower accuracy GPS is desirable. Our in-house automated low speed electric vehicle is used in experimental evaluation and verification. In addition, the experimental vehicle has vehicle to everything (V2X) communication capability and utilizes a dedicated short-range communication (DSRC) modem. It is able to communicate with instrumented traffic lights and with pedestrians and bicyclists with DSRC enabled smartphones. Before real-world experiments, our connected and automated driving hardware in the loop (HiL) simulator with real DSRC modems is used for extensive testing of the algorithms and the low level longitudinal and lateral controllers. Real-world experiments that are reported here have been conducted in a small test area close to the Ohio State University AV pilot test route. Model-in-the-loop simulation, HiL simulation and experimental testing are used for demonstrating the feasibility and robustness of this approach to developing and evaluating low speed autonomous shuttle localization and perception algorithms for control and decision making.
CitationWen, B., Gelbal, S., Aksun Guvenc, B., and Guvenc, L., "Localization and Perception for Control and Decision Making of a Low Speed Autonomous Shuttle in a Campus Pilot Deployment," SAE Technical Paper 2018-01-1182, 2018, https://doi.org/10.4271/2018-01-1182.
Data Sets - Support Documents
|Unnamed Dataset 1|
- Leonard , John J. , and Hugh F. Durrant-Whyte Simultaneous Map Building and Localization for an Autonomous Mobile Robot In Intelligent Robots and Systems’ 91.’ Intelligence for Mechanical Systems, Proceedings IROS’91 1442 1447 1991
- Pritsker , A. Alan B. Introduction to Stimulation and Slam II 1986
- Dissanayake , M.W.M.G. , Newman , P. , Clark , S. , Durrant-Whyte , H.F. , and Csorba , M. A Solution to the Simultaneous Localization and Map Building (SLAM) Problem IEEE Transactions on Robotics and Automation 17 3 229 241 2001
- Grisettiyz , Giorgio , Cyrill Stachniss , and Wolfram Burgard Improving Grid-based Slam with Rao-blackwellized Particle Filters by Adaptive Proposals and Selective Resampling Robotics and Automation, 2005. ICRA 2005. Proceedings of the 2005 IEEE International Conference on 2432 2437 2005
- Kohlbrecher , Stefan , Oskar Von Stryk , Johannes Meyer , and Uwe Klingauf A flexible and scalable slam system with full 3d motion estimation In Safety, Security, and Rescue Robotics (SSRR), 2011 IEEE International Symposium on 155 160 2011
- Newman , Paul , David Cole , and Kin Ho Outdoor SLAM using Visual Appearance and Laser Ranging Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on, pp. 1180-1187. IEEE 2006
- Cole , David M. , and Paul M. Newman Using Laser Range Data for 3D SLAM in Outdoor Environments Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on 1556 1563 2006
- Levinson , Jesse , and Sebastian Thrun Robust Vehicle Localization in Urban Environments using Probabilistic Maps Robotics and Automation (ICRA), 2010 IEEE International Conference on, pp. 4372-4378. IEEE 2010
- Vu , Trung-Dung , Julien Burlet , and Olivier Aycard Grid-based localization and online mapping with moving objects detection and tracking: new results In Intelligent Vehicles Symposium, 2008 IEEE 684 689 2008
- Gelbal , S. Y. , Wang , H. , Chandramouli , N. , Guvenc , L. et al. A Connected and Autonomous Vehicle Hardware-in-the-loop Simulator for Developing Automated Driving Algorithms IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2017
- Emirler , Mümin Tolga , Haoan Wang , B. Aksun Güvenç , and Levent Güvenç Automated robust path following control based on calculation of lateral deviation and yaw angle error ASME Dynamic Systems and Control Conference (DSCC) Columbus, OH, USA October 28 2015
- Moré , J.J. The Levenberg-Marquardt Algorithm: Implementation and Theory Numerical Analysis Berlin, Heidelberg Springer 1978 105 116
- Lucas , Bruce D. , and Takeo Kanade An Iterative Image Registration Technique with an Application to Stereo Vision 1981 674 679
- Meer , P. et al. Robust Regression Methods for Computer Vision: A Review International Journal of Computer Vision 6 1 59 70 1991
- Quigley , Morgan , Ken Conley , Brian Gerkey , Josh Faust , Tully Foote , Jeremy Leibs , Rob Wheeler , and Andrew Y. Ng. ROS: An Open-Source Robot Operating System In ICRA Workshop on Open Source Software 3 3.2 5 2009