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OKI Project – Phase 2 Project Progress Summary Department of Electrical and Computer Engineering The Ohio State Universi

OKI Project – Phase 2 Project Progress Summary Department of Electrical and Computer Engineering The Ohio State University May 2004. Key Accomplishments. Physical Layer Channel Modeling:

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OKI Project – Phase 2 Project Progress Summary Department of Electrical and Computer Engineering The Ohio State Universi

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  1. OKI Project – Phase 2 Project Progress Summary Department of Electrical and Computer Engineering The Ohio State University May 2004

  2. Key Accomplishments • Physical Layer Channel Modeling: • Investigated a modified two-ray model for line-of-sight condition, comparing vehicle rooftop vs. ground reflections [1] • Investigated a model for no line-of-sight condition [2] • Determined appropriate reflection coefficients [15] • Wireless Simulators: • Improving original simulator to better represent wireless transmission behavior, including additional packet updates, packet retransmission, and a repeater at the intersection • Enhanced data update logic • Transmission every 10 meters, within 50 meters from intersection • Packet retransmission for configurable number of attempts • Given real-time limitations on simulator performance, developing a statistical wireless simulator for packet behavior under a variety of vehicle conditions • Vehicle Traffic Simulator: • Improving simulator to allow user to select different simulation input parameters, such as vehicle density and throughput • Developed graphical user interface to perform and monitor simulation OKI Project - Phase 2

  3. Direct path: rt Reflected path: ht hr h0 Distance: r Path Loss s: visibility factor, set to 0 when the transmitter is lower than the surroundings. rt: (direct distance from the tip of transmitting antenna to the tip of receiving antenna) = : (ith distance from the tip of transmitting antenna to the tip of receiving antenna via reflection) = Ri: reflection coefficient. k: wave number. ht: height of transmitting antenna to the ground. hr: height of receiving antenna to the ground. h0: height of the reflection surface to the ground. It is set to zero when we only consider the reflection from road. Physical Layer Channel Modeling – Line-Of-Sight Path [1] Considering a 2-path model, the received power is mainly contributed by the direct path and single reflection from a surface. OKI Project - Phase 2

  4. Physical Layer Channel Modeling – Line-Of-Sight Path [1] Simulation Results • At short distances, the direct path and road reflection path have rapid phase shift, yielding the oscillations noted in the right-hand chart. The rooftop reflection, for the antenna height selected, yields a phase shift that smooths out the path loss. Further investigation is necessary to determine appropriate parameters (i.e., antenna height, reflection coefficient, etc.) • There is a significant difference (~40 dB) between the traditional model and the modified two-ray model; further investigation is in progress to determine if the vehicle density can be leveraged to provide a more accurate approximation OKI Project - Phase 2

  5. x TX • α is the street parameter to specify the street characteristics. • The receiver is out-of-sight but close to the intersection (RX2), the reflection mechanism dominates [4]. We do not use a VS. • The receiver is out-of-sight and far away from the intersection (RX1), the diffraction mechanism dominates [4][5]. Use a VS. • According to [3], this model is only considered for distances up to 300m from the LoS street. Beyond that, it loses the consistency. • The term inside {} was originally represented as the free space model: • But it is suggested in [3] that it should be replaced with the 2-path model for a better LoS street model. rs RX2 ws r RX1 VS Physical Layer Channel Modeling – No Line-Of-Sight Path [2] We use a “Virtual Source (VS)” to model diffraction. (RX1 only) Path loss (between RX1 and VS) OKI Project - Phase 2

  6. Statistical information: • Packet success probability • Packet collision probability Non Real-Time Wireless Simulator Input Parameters: - Vehicle density - Vehicle throughput Wireless Simulator Vehicle Traffic Simulator • Trace files: • Vehicle information • Vehicle position • - Vehicle velocity Physical layer model Multiple scenarios with different input parameters • Information collected from the multiple scenario executions will be the basis for Statistical Wireless Simulator OKI Project - Phase 2

  7. Statistical Wireless Simulator Statistical Wireless Simulator Vehicle density Vehicle positions Communication protocol Protocol parameters Packet Generator Vehicle Traffic Simulator Driver Behavior Nodes that received packet Packet reception time = (request time + packet length) • The statistical wireless network simulator will allow real-time feedback to the vehicle traffic simulator. This, in turn, enables driver behavior to be modified by information from the wireless simulator. OKI Project - Phase 2

  8. Vehicle Traffic Simulator - Overview • Warning System and Driver Behavior Model: • Human factors must be taken into consideration for developing an “intelligent” collision warning system ([6],[7],[8],[9]). • A viable collision warning system should satisfy the following: • 1. Reduce the collisions • 2. Minimize the driver’s attention load • 3. Not to give out excessive warning signal • 4. Not to distract the driver OKI Project - Phase 2

  9. R: relative distance : : relative velocity Li : Vehicle i’s length in angle TTA: Time to Avoidance : Speed Reduction Parameter. If 1, then full stop. μ: Friction Coefficient. Vehicle Traffic Simulator – Warning System Algorithm • Three Level Warning System: • Get Communication Data • Compute Route Contention • If no Route Contention • No Warning • Else • Compute TTC and TTA • If TTC >= TTA+ Driver’s Response Time (1.93 s -2.53 s) • If Deceleration>=TTA Deceleration • No Warning • Else If Deceleration < TTA Deceleration • Warning Level 1 • Else If no acceleration • Warning Level 2 • Else (acceleration) • Warning Level 3 • Else • No Warning • Notes: • Driver’s Response Time: Initial Driver action was defined as the first action the subject performed after the incurring vehicle initiated movement. (Either begin to release the accelerator pedal or begin to steer as part of this measure) Data comes from actual experiments. [10] • TTC: In research on Traffic Conflicts Techniques, Time-To-Collision (TTC) has proven to be an effective measure for rating the severity of conflicts. [11] TTC is defined as: "The time required for two vehicles to collide if they continue at their present speed and on the same path". In principle, the lower the TTC, the higher the risk of a collision has been ([12],[13]). OKI Project - Phase 2

  10. Vehicle Traffic Simulator – Driver Behavior Model [6], [10], [14] • Aggressive Driver: • Only response to Warning Level 3 • Initial accelerator release only • Normal Driver: • Response to Both Warning Level 3 and Level 2 • Braking to Warning Level 3 • Decelerate slowly to Warning Level 2 • Conservative Driver: • Response to all the Warnings • Braking to Warning Level 3 and Warning Level 2 • Decelerate quickly to Warning Level 1 OKI Project - Phase 2

  11. Vehicle Traffic Simulator • Simulation snapshot for left-turn signal scenario • Indicates signal status • Indicates last collision event Parameter specification during simulation startup OKI Project - Phase 2

  12. Next Steps • Physical Layer Modeling: • Develop received path loss simulation module for multiple input conditions (i.e. vehicle density and velocity) • Wireless Network Simulator: • Integrate improved physical layer simulation module • Generate statistical wireless simulator for multiple input conditions (i.e. vehicle density and velocity) • Vehicle Traffic Simulator: • Develop Warning System using statistical data to interact with received packet information • Develop driver behavior module to react warnings • Determine impact of received information on driver behavior and collision avoidance • Integrate statistical wireless network simulator for real-time feedback OKI Project - Phase 2

  13. References [1] Y. Oda, K. Tsunekawa and M. Hata, “Advanced LOS path-loss model in microcellular mobile communications”, IEEE Trans. Vehicular Techn., vol. 49, (6), Nov. 2000, pp. 2121 – 2125. [2] H.M. El-Sallabi, “Fast path loss prediction by using virtual source technique for urban microcell”, IEEE VTC 2000-Spring Tokyo, pp. 2183 – 2187. [3] T.C.W. Schenk, R.J.C. Bultitude, L.M. Augustin, R.H. van Poppel and G. Brussaard, “Analysis of propagation loss in urban microcells at 1.9GHz and 5.8GHz” [4] V. Erceg, A.J. Rustako Jr. and R.S. Roman, “Diffraction around corners and its effects on the microcell coverage area in urban and suburban environments at 900MHz, 2GHz, and 6GHz”, IEEE Transactions on Vehicular Technology, vol. 43, no. 3 pp. 762 – 766, Aug 1994. [5] H.L. Bertoni, W. Honcharenko, L.R. Maciel, and H.H. Xia, “UHF propagation prediction for wireless personal communications”, Proc. IEEE, vol. 82, pp. 1333 – 1359, Sept. 1994. [6] Ronald Miller and Qingfeng Huang, ‘An Adaptive Peer-to-Peer Collision Warning System’ IEEE VTC2002 [7] John D. Lee, Michelle L. Ries, Dannile V.McGehee, and Timothy L. Brown, ‘Can Collision Warning Systems Mitigate Distraction Due to In-Vehicle Devices?’, May 2000, NHTSA http://www-nrd.nhtsa.dot.gov/departments/nrd-13/driver-distraction/PDF/31.PDF [8] Mark Vollrath and Ingo Totzke, ‘In-Vehicle Communication and Driving: An attempt to Overcome their Interference,’ June 2000 NHTSA http://www.psychologie.uni-wuerzburg.de/methoden/forschung/vortraege/vollrath&totzke_NHTSA_artikel.pdf [9] Louis Tijerina, ‘Issues in the Evaluation of Drive Distraction Associated with In-vehicle Information and Telecommunication Systems,’ May 2000,NHTSA http://www-nrd.nhtsa.dot.gov/departments/nrd-13/driver-distraction/PDF/3.PDF [10] Daniel V. McGehee, Timothy L. Brown “Effect of Warning Timing on Collision Avoidance Behavior in a Stationary Lead Vehicle Scenario” Transportation Research Record 1803 paper No. 02-3746 [11] Richard van der Horst & Jeroen Hogema “TIME-TO-COLLISION AND COLLISION AVOIDANCE SYSTEMS” http://www.ictct.org/workshops/93-Salzburg/Horst.pdf [12] Delphi-Delco Electronic Systems “Automotive Collision Avoidance Systems (ACAS) Program Final Report” DOT HS 809 080 August 2000 http://www.nhtsa.dot.gov/people/injury/research/pub/ACAS/TOC.htm [13] Hayward, J.Ch. (1972). “Near miss determination through use of a scale of danger.” Report no.TTSC 7115, The Pennsylvania State University, Pennsylvania. [14] Dariush, B.Fujimura, K.   “A framework for driver specific inference of danger at signalized intersections” Intelligent Transportation Systems, 1999. Proceedings. 1999 IEEE/IEEJ/JSAI International Conference 5-8 Oct. 1999 Tokyo Japan On page(s): 195 – 200 [15] W.C. Jakes Jr., Microwave Mobile Communication. New York: Wiley, 1974 OKI Project - Phase 2

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