1 / 29

Design of Adaptive Cruise Control System - A Time-critical Data Driven Approach

by Neera Sharma (03305402) under the guidance of Prof. Krithi Ramamritham. Design of Adaptive Cruise Control System - A Time-critical Data Driven Approach. Motivation. Intelligent automotive applications require efficient management of time-sensitive data.

felicia-armstrong
Download Presentation

Design of Adaptive Cruise Control System - A Time-critical Data Driven Approach

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. by Neera Sharma (03305402) under the guidance of Prof. Krithi Ramamritham Design of Adaptive Cruise Control System - A Time-critical Data Driven Approach

  2. Motivation • Intelligent automotive applications require efficient management of time-sensitive data. • Existing approaches for ACC design • Control theory based • Ad hoc data management • Systematic data management could improve the efficiency of control theory driven approaches. • We propose a model for designing a real-time data repository for ACC.

  3. Outline • Introduction to ACC • Functional Model • Data Management in ACC • Real-time repository model • Task scheduling in the model • Used techniques and performance results • Mode Change Behavior of ACC • Issues in mode change design

  4. Adaptive Cruise Control • ACC • Controls vehicle speed to maintain a safe distance from leading vehicle, automatically. • Detects lead vehicle using sensors. • Adjusts speed based on the velocity and distance from detected vehicle. • Increases safety and driver comfort. • Next step towards fully autonomous vehicles.

  5. Distance & velocity of obstacle Accelerate/ decelerate Cruise control Interface / braking Radar Sensor Unit Controller Unit Preset speed User Interface How ACC works? • Radar sensor detects lead obstacle and returns its velocity and separation from ACC host. • Controller unit calculates required safe-distance and desired velocity. • Cruise controller regulates the host speed to the desired speed using throttling and braking.

  6. Human interaction time For automatic system delay of sensors Current acceleration a Velocity is zero here J_max T+t_1 T+t_1+t_f t_0 T - A_max Max deceleration Calculating Safe-Distance • Two kinds of policies: static & dynamic • We calculate safe distance as a function of relative velocity (dynamic)[5]. Sd= Sm + Sa Sm = minimum separation , Sa = additional gap for safety

  7. vehicle_sensed() = false speed>30 OFF ON /switch on no vehicle ahead /resume /get_val() vehicle_sensd() = true cur_sp = cruise_sp cur_sp < cruise_sp a = 0 vehicle_sens`ed() = true decelerating with feedback /get_val() accelerating with feedback cal a a < 0 a > 0 vehicle_sensed() = true / get_val() vehicle_sensed() = true /get_val() vehicle ahead max_brk & ACC off ACC off emergency state vehicle_sensed() = false State Diagram of the System

  8. Real-Time Data Repository Design for ACC

  9. Design Concerns • Data Freshness • Values in repository • obtained from sensors • reflect the latest values of vehicle parameters. • Freshness of a data is defined • In time domain : update periodically current time – TS(d) <= VI(d) • In value domain : update if |d(t) – d(t’) | <= δd • Temporal characteristics of tasks are derived from the properties of data.

  10. Real-Time Data Repository for ACC Sensor parameters Controller constants Hierarchical ACC Controller R1 R2 Read sensor values ` On demand update host_v Read R2 Upper-level controller From speed sensors Update R2 calculate safe_dist Current status store desired velocity safe_dist Lower-level controller Derived data-items From radar sensor lead_v Log current status separation Raw data-items Base data-items To actuators Stable store

  11. Why Two-level Data Store? • Controller decisions change when there are significant changes repository1. • Repository2 is updated only when difference in the values crosses a threshold value (on-demand update). • Two level data store minimizes contention. • OD updates reduces unnecessary updates in the system

  12. Design for a Chain of Vehicles • A chain of ACC vehicles should be stable. • spacing errors does not increase from head to tail traversal of the chain : String Stability • for chain of vehicles εi = (xi-1 - xi) – Di(Range Error ) Ri = vi-1 – vi(Range Rate Error ) Di : is desired separation, vi : velocity of ith vehicle and, (xi-1 – xi ) : current inter-vehicle separation • A uniform vehicle string is string stable if - ||εi+1|| <= ||εi ||

  13. Upper-level Controller • Calculates desired speed . • We use UTMRI algorithm[4] to determine the desired speed Vi,des = vi-1 + εi /T0 + c. Ri Vi,des : desired velocity, vi-1 : velocity of leading vehicle T0 and c are constants.

  14. αdes α From ULC v0 v LLC A P - Lower Level Controller • Modelled as first order linear system. • Determines the throttle & brake actuator commands to track the desired velocity using [4]. τ . vcurr + vcurr = vdes • Using proportional control law desired velocity is mapped to required throttle position using- α(t) = Kp (vcurr - vdes)

  15. Tasks in the Model • Sensor reading tasks : • periodic with known computation time. • On-demand update tasks (Update R2 + Read R2): • aperiodic with known minimum inter-arrival time and worst case computation time. • Low-level Controller Tasks : • periodic with known computation time. • Other Tasks : (logging, lane monitoring, road condition etc): • Periodic with known computation time. • Periodic tasks are scheduled using EDF.

  16. Real-Time Data Repository for ACC Sensor parameters Controller constants Hierarchical ACC Controller R1 R2 Read sensor values ` On demand update host_v Read R2 Upper-level controller From speed sensors Update R2 calculate safe_dist Current status store desired velocity safe_dist Lower-level controller Derived data-items From radar sensor lead_v Log current status separation Raw data-items Base data-items To actuators Stable store

  17. How to Schedule Aperiodic OD tasks? • OD tasks need predictable service guarantees. • Bandwidth Reservation Techniques: • Reserve a share of CPU bandwidth. • Constant Bandwidth Server(CBS): • S = (C, T, B), characterized by maximum capacity(C), period(T) and bandwidth(B=C/T). • Task can execute for time C within period T. • Provides hard-real time guarantees if task’s worst case parameters are known.

  18. Adaptive CBS Technique • Adapts required bandwidth for a task using error correction mechanism. • T is equal to the period of sensor reading task. • CBS scheduling error is calculated as: • CBS bandwidth is adjusted using capacity correction: ε = CBS deadline – task deadline δC = (ε / Ts )* cs

  19. Simulator Setup • We simulate the model on RTLinux kernel. • Threads communicate using shared memory. • For CBS : • We use application level CBS patch on RTLinux. • Modify it for automatic bandwidth adaptation.

  20. Simulations Results Reserved Bandwidth CBS Scheduling Error • Reserved bandwidth converges to value 0.001 • Corresponding CBS scheduling error reduces to 0 after few steps.

  21. Mode Change Behavior of the System • Response requirement of ACC vehicle change with the change in relative velocity and separation between host and ACC vehicle. • We design ACC with three modes of operations : active, non-critical and critical. • In mode change task set and frequencies of tasks change. • We assume that a task set is known for each of the three modes.

  22. Preliminary experiments for Mode Change • We assume that controller operates with frequency 50Hz : in active mode 70Hz : non-critical mode 100Hz : critical mode and choose corresponding values for sampling time(T) and time constant(τ) for each mode. • Conditions for mode change: • From active to non-critical • an obstacle is detected within a predefined range. • From non-critical to critical • the difference between desired speed and current speed is greater than a threshold.

  23. Simulations : Without Mode Change Sudden decrease in separation, because the host vehicle decreases slowly.

  24. Simulations : With Mode Change r2 r1 r3 r4 Host velocity fluctuates due to frequent mode change.

  25. Simulation : Avoiding Frequent Mode Change Frequent mode change is avoided by forcing the system to stay in one mode for a minimum time

  26. Enhancement to the Mode Change Scheme • Choose task set for each mode at run time using service level controller, admission controller and feedback. • Service level controller : controls workload inside the system. • Admission controller : admits new tasks in the system.

  27. Conclusions and Future Work • Systematic approach for handling time-sensitive ACC data improves the performance. • Use of on-demand update scheme reduces the no of updates. • Adaptive bandwidth server technique provide service guarantees to aperiodic tasks. • There is a need for ACC design with multiple operational modes. • A principled approach for choosing no. of modes and deriving conditions for mode change is required.

  28. References • Thomas Gustafsson and Jorgen Hansson. Dynamic On-Demand Updating of Data in Real- Time Database Systems. In SAC'04: Proceedings of the 2004 ACM symposium on Applied computing, pages 846-853. ACM Press, 2004. • K. Ramamritham; Sang H. Son; L.C. DiPippo. Real-Time Database and Data Services. In Real Time Systems: p.179-216. Kluwer Academic Publishers, 2004. • D. Nystrom, A. Tesanovic, C. Norstrom, J. Hansson, and N-E. Bankestad. Data Management Issues in Vehicle Control Systems: a Case Study. In Proceedings of the 14th Euromicro International Conference on Real-Time Systems, pages 249-256, Vienna, Austria, June 2002. • Zhou J.; Peng H. String Stability Conditions of Adaptive Cruise Control Algorithms. 1st IFAC Symposium on Advances in Automotive Control, April Italy, 2004. • C. C. Chien; P. A. Ioannou. Autonomous Intelligent Cruise Control. IEEE Trans. On Vehicular Technology, 42(4):657-672, Nov. 1993.

  29. References • T.W. Kuo; A. K. Mok. Real-time Data Semantics and Similarity-Based Concurrency Control. IEEE Trans. on Computers, 49(11):1241-1254, Nov. 2000. • Thomas Gustafsson; Jorgen Hansson. Data Management in Real-Time Systems: a Case of On-Demand Updates in Vehicle Control Systems. 10th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS'04), page 182, May 25-28, 2004 • Jing Zhou; Huei Peng. Range Policy of Adaptive Cruise Control for Improved Flow Stability and String Stability. IEEE International Conference on IEEE Trans. on Networking, Sensing and Control, 4:595-600, March, 21-23 2004. • C. L u; J. Stankovic; G. Tao; S. Son. Feedback Control Real-Time Scheduling: Framework, Modeling and Algorithms. special issue of Real-Time Systems Journal on Control-Theoretic Approaches to Real-Time Computing,, 23(1/2):85-126, July/September 2002.

More Related