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Enhanced AHS Safety Through the Integration of Vehicle Control and Communication

Enhanced AHS Safety Through the Integration of Vehicle Control and Communication. October 2002 J. K. Hedrick, R. Sengupta, Q. Xu, C. Lee, Y. Kang. Communication architecture. Goal = wireless network useful for: 1) Cooperative friction estimation 2) Cooperative Emergency Maneuver

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Enhanced AHS Safety Through the Integration of Vehicle Control and Communication

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  1. Enhanced AHS Safety Through the Integration of Vehicle Control and Communication October 2002 J. K. Hedrick, R. Sengupta, Q. Xu, C. Lee, Y. Kang

  2. Communication architecture Goal = wireless network useful for: 1) Cooperative friction estimation 2) Cooperative Emergency Maneuver 3) AHS and cooperative adaptive cruise control Emergency Braking Maneuvers Tire/road friction estimation Goal: Develop a safe control strategy for the emergency braking maneuver of platoons. Goal: Real time estimation of maximum tire-road friction Research Areas

  3. Communication architecture Tire/road friction estimation Road Condition & Maximum Friction Coeff. Information from other vehicles and road side infra structure. Emergency Braking Maneuvers “ SAFETY ” Project Goals

  4. Slip-based Road Condition Estimation

  5. Overview andBenefits of the Research • Overview • Empirical approach • Effect-based method • Real Time work • Benefits • Estimation of max. acceleration limit of the vehicle. • Emergency Braking Control for the Platoon. • Road Condition vs. Position Map.

  6. Slip/Friction Coefficient Calculation • Friction Coefficient • Slip • Max Acceleration Maximum friction coefficients determines maximum acceleration or deceleration

  7. Slip Slope Vs Affecting Factors • Road Condition • Tire Type • Tread Pattern • Tread Depth • Velocity Focus on Linear Region Slip Slope, k

  8. Schematic of the Estimator

  9. Static Normal Force Observer Normal Force friction coefficient calculation effective tire radius estimation Static normal force observer assumes a static model for vehicle3% error comparing to dynamic vehicle model Static Normal Force Model

  10. Effective Tire Radius Observer • Tire radius is required for Slip Calculation Tire Radius Change is a Function of • Normal Force • Tire Pressure • Velocity

  11. Tractive Force Estimation

  12. Brake Gain Estimator • Brake Gain Change • Heat • Water • Wear of Brake Pad, etc. sometimes changes more than 50% • Model : Front Wheel Dynamics • Method : Recursive Least Square Method Using Bounded Forgetting

  13. Slip Slope Vs Each vehicle has its own slip slope under same road Dry road condition set as Reference slip slope Maximum friction coefficient change rate / Slip slope change rate based on reference slip slope Linear Assumption

  14. Slip Slope and Estimation • Example : Wet  Dry Slip Slope Estimation Using RLS method Estimation Based on slip slope

  15. Emergency Braking Maneuvers

  16. Emergency Braking Controller(Longitudinal Control) • Requirements for emergency braking control • Control robustness against the high slip condition between road and tire. • Friction coefficients estimation algorithm. • Control method : Dynamic Surface Control

  17. Experimental Set-up • Experimental Vehicle used : Ford red Lincoln town car • Sensors and actuators : wheel speed sensors brake pressure sensors 5th wheel speed sensor brake and throttle actuators • Computers with real-time OS : QNX operating system

  18. Simulation Results • Simulation with Dynamic Surface Control (6 m/s2 deceleration)

  19. Experimental Results • Experiments in same situation ( 6 m/s2 deceleration)

  20. Analysis of the Result • The performance of the Dynamic Surface Controller is reasonable(1~2m error with –6 m/s2) in an emergency braking situation. • Control performance depends on friction coefficients.  “Friction coefficient estimation” is important.

  21. (((( Emergency Braking Control Strategies for Platoons • Emergency braking for the Platoons : Difference of max. deceleration limit between vehicles can result in collision. • Cooperative Emergency Control. )))) Vehicle with Worst Braking Capacity transmits its info.

  22. Simulation Results • Space & velocity tracking [m/s] [m]

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