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Dynamic Object Map Based Architecture for Robust CVS Systems
Technical Paper
2020-01-0084
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Connected and Autonomous Vehicles (CAV) rely on information obtained from sensors and communication to make decisions. In a Cooperative Vehicle Safety (CVS) system, information from remote vehicles (RV) is available at the host vehicle (HV) through the wireless network. Safety applications such as crash warning algorithms use this information to estimate the RV and HV states. However, this information is uncertain and sparse due to communication losses, limitations of communication protocols in high congestion scenarios, and perception errors caused by sensor limitations. In this paper we present a novel approach to improve the robustness of the CVS systems, by proposing an architecture that divide application and information/perception subsystems and a novel prediction method based on non-parametric Bayesian inference to mitigate the detrimental effect of data loss on the performance of safety applications. The architecture is validated with simulations and in a real environment using a remote vehicle emulator (RVE) based on a Denso OBU, which allows the joint study of the CVS applications and its underlying communication system. We study the impact of using different vehicle tracking (prediction) techniques and demonstrate the performance improvement potential of this approach.
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Citation
Valiente, R., Raftari, A., Zaman, M., Pourmohammadi Fallah, Y. et al., "Dynamic Object Map Based Architecture for Robust CVS Systems," SAE Technical Paper 2020-01-0084, 2020, https://doi.org/10.4271/2020-01-0084.Also In
References
- Sengupta , R. , Rezaei , S. , Shladover , S.E. , Cody , D. et al. Cooperative Collision Warning Systems: Concept Definition and Experimental Implementation J. Intell. Transp. Syst. Technol. Planning, Oper. 2007 10.1080/15472450701410452
- Fallah , Y.P. and Khandani , M.K. Analysis of the Coupling of Communication Network and Safety Application in Cooperative Collision Warning Systems 2015 10.1145/2735960.2735975
- Fallah , Y.P. and Khandani , M.K. Context and Network Aware Communication Strategies for Connected Vehicle Safety Applications IEEE Intell. Transp. Syst. Mag. 2016 10.1109/MITS.2016.2593672
- Rezaei , S. , Sengupta , R. , Krishnan Hariharan , H. , Guan , X. et al. Tracking the Position of Neighboring Vehicles Using Wireless Communications Transp. Res. Part C: Emerg. Technol. 2010 10.1016/j.trc.2009.05.010
- Zhang , R. , Cao , L. , Bao , S. , and Tan , J. A Method for Connected Vehicle Trajectory Prediction and Collision Warning Algorithm Based on V2V Communication Int. J. Crashworthiness 2017 10.1080/13588265.2016.1215584
- Jiang , D. and Delgrossi , L. IEEE 802.11p: Towards an International Standard for Wireless Access in Vehicular Environments IEEE Vehicular Technology Conference 2008 10.1109/VETECS.2008.458
- Lee , K. and Peng , H. Evaluation of Automotive forward Collision Warning and Collision Avoidance Algorithms Veh. Syst. Dyn. 2005 10.1080/00423110412331282850
- Kim , C. , Park , J. , and Huh , K. Target Classification Layer Design via Vehicle-to-Vehicle Communication Proc. Inst. Mech. Eng. Part D: J. Automob. Eng. 2016 10.1177/0954407016633551
- Valiente , R. , Zaman , M. , Ozer , S. , and Fallah , Y.P. Controlling Steering Angle for Cooperative Self-Driving Vehicles Utilizing CNN and LSTM-Based Deep Networks 2019 10.1109/ivs.2019.8814260
- Toghi , B. et al. Multiple Access in Cellular V2X: Performance Analysis in Highly Congested Vehicular Networks IEEE Vehicular Networking Conference, VNC 2019 10.1109/VNC.2018.8628416
- Toghi , B. , Mughal , M.O. , Saifuddin , M. , and Fallah , Y.P. Spatio-Temporal Dynamics of Cellular V2X Communication in Dense Vehicular Networks 2019
- Niu , Y. , Li , Y. , Jin , D. , Su , L. et al. A Survey of Millimeter Wave Communications (mmWave) for 5G: Opportunities and Challenges Wirel. Networks 2015 10.1007/s11276-015-0942-z
- Forkenbrock , G.J. and O’Harra , B.C. A Forward Collision Warning (FCW) Performance Evaluation Enhanc. Saf. Veh. 2009
- Jamson , A.H. , Lai , F.C.H. , and Carsten , O.M.J. Potential Benefits of an Adaptive forward Collision Warning System Transp. Res. Part C: Emerg. Technol. 2008 10.1016/j.trc.2007.09.003
- Kiefer , R.J. et al. Forward Collision Warning Requirements Project: Refining the CAMP Crash Alert Timing Approach by Examining “Last-Second” Braking and Lane Change Maneuvers under Various Kinematic Conditions 2003
- Sakr , A.H. , Bansal , G. , Vladimerou , V. , Kusano , K. et al. V2V and On-board Sensor Fusion for Road Geometry Estimation IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC 2018 10.1109/ITSC.2017.8317876
- Sepulcre , M. , Gozalvez , J. , and Hernandez , J. Cooperative Vehicle-to-Vehicle Active Safety Testing under Challenging Conditions Transp. Res. Part C: Emerg. Technol. 2013 10.1016/j.trc.2012.10.003
- Ahmed-Zaid , F. et al. 2011
- Parker , R. and Valaee , S. Cooperative Vehicle Position Estimation IEEE International Conference on Communications 2007 10.1109/ICC.2007.967
- Fallah , Y.P. A Model-Based Communication Approach for Distributed and Connected Vehicle Safety Systems 2016 Annual IEEE Systems Conference (SysCon) 2016 1 6
- Baek , S. , Liu , C. , Watta , P. , and Murphey , Y.L. Accurate Vehicle Position Estimation Using a Kalman Filter and Neural Network-Based Approach 2017 IEEE Symposium Series on Computational Intelligence (SSCI) 2017 1 8
- Painter , J.H. , Kerstetter , D. , and Jowers , S. Reconciling Steady-State Kalman and Alpha-Beta Filter Design 1990
- Chu , L. , Shi , Y. , Zhang , Y. , Liu , H. et al. Vehicle Lateral and Longitudinal Velocity Estimation Based on Adaptive Kalman Filter ICACTE 2010 - 2010 3rd International Conference on Advanced Computer Theory and Engineering, Proceedings 2010 10.1109/ICACTE.2010.5579565
- Mahjoub , H.N. , Toghi , B. , and Fallah , Y.P. A Driver Behavior Modeling Structure Based on Non-Parametric Bayesian Stochastic Hybrid Architecture IEEE Vehicular Technology Conference 2019 10.1109/VTCFall.2018.8690965
- Mahjoub , H.N. , Toghi , B. , Gani , S.M.O. , and Fallah , Y.P. V2X System Architecture Utilizing Hybrid Gaussian Process-Based Model Structures 2019 10.1109/syscon.2019.8836879
- Rasmussen , C.E. Gaussian Processes in Machine Learning Summer School on Machine Learning 2003 63 71
- Mahjoub , H.N. , Raftari , A. , Valiente , R. , Fallah , Y.P. et al. Representing Realistic Human Driver Behaviors Using a Finite Size Gaussian Process Kernel Bank
- Neale , V.L. , Dingus , T.A. , Klauer , S.G. , Sudweeks , J. et al. An Overview of the 100-Car Naturalistic Study and Findings Natl. Highw. Traffic Saf. Adm. Pap. 5 400 2005
- Shah , G. et al. Real-Time Hardware-in-the-Loop Emulation Framework for DSRC-Based Connected Vehicle Applications 2019
- Yang , Q. , Koutsopoulos , H.N. , and Ben-Akiva , M.E. Simulation Laboratory for Evaluating Dynamic Traffic Management Systems Transp. Res. Rec. J. Transp. Res. Board 2007 10.3141/1710-14
- ITS https://www.its.dot.gov/factsheets/pdf/SafetyPilotModelDeployment.pdf