Design of Adaptive Cruise Control System - A Time-critical Data Driven Approach by Neera Sharma...

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

by

Neera Sharma

(03305402)

under the guidance of

Prof. Krithi Ramamritham

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.

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

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.

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.

Radar SensorUnit

ControllerUnit

User Interface

Distance & velocity of obstacle

Accelerate/decelerate

Preset speed

Cruise controlInterface / braking

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

Human interaction timeFor automatic system delay of sensors

Max deceleration

J_max

T

T+t_1

T+t_1+t_f

t_0

- A_max

Velocity is zero here

Current acceleration

a

State Diagram of the System

OFF ONno vehicle ahead

/resume

deceleratingwith feedback

vehicle_sensed() = false

vehicl

e_sen

s`ed()

= true

vehicle_sensd() = true

vehicle_sensed() = true / get_val()vehicle_sensed() = true

/get_val()

/get_val(

)

/get_val()

speed>30

/switch on

a = 0

cur_

sp <

cru

ise_

sp

acceleratingwith feedback

cur_

sp =

cru

ise_

sp

a > 0a < 0

max

_brk

&

AC

C o

ff

ACC off

vehicle_sensed() = falseemergency state

vehicle ahead

cal a

Real-Time Data Repository Design for ACC

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.

Hierarchical ACC Controller

Fromspeed

sensors

Fromradar

sensor

Upper-level controller

Lower-level controller

R1 R2

Current status

Stable store

Log currentstatus

Raw data-items

Base data-items

safe_dist

host_v

lead_v

separation

Derived data-items

Sensor parameters

Controller constants

To actuators

Update R2

store desired velocity

Read sensor values

`

calculate safe_dist

Read R2

Real-Time Data Repository for ACC

On demand update

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

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 ||

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.

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)

αdes αv0LLC A P

v

-

From ULC

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.

Hierarchical ACC Controller

Fromspeed

sensors

Fromradar

sensor

Upper-level controller

Lower-level controller

R1 R2

Current status

Stable store

Log currentstatus

Raw data-items

Base data-items

safe_dist

host_v

lead_v

separation

Derived data-items

Sensor parameters

Controller constants

To actuators

Update R2

store desired velocity

Read sensor values

`

calculate safe_dist

Read R2

Real-Time Data Repository for ACC

On demand update

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.

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

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.

Simulations Results

Reserved Bandwidth CBS Scheduling Error

•Reserved bandwidth converges to value 0.001•Corresponding CBS scheduling error reduces to 0 after few steps.

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.

Preliminary experiments for Mode Change

• We assume that controller operates with frequency 50Hz : in active mode

70Hz : non-critical mode100Hz : 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.

Simulations : Without Mode Change

Sudden decrease in separation, because the host vehicle decreases slowly.

Simulations : With Mode Change

Host velocity fluctuates due to frequent mode change.

r1

r2

r3r4

Simulation : Avoiding Frequent Mode Change

Frequent mode change is avoided by forcing the system to stay in one mode for a minimum time

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.

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.

References1. 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.

2. 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.

3. 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.

4. Zhou J.; Peng H. String Stability Conditions of Adaptive Cruise Control Algorithms. 1st IFAC Symposium on Advances in Automotive Control, April Italy, 2004.

5. C. C. Chien; P. A. Ioannou. Autonomous Intelligent Cruise Control. IEEE Trans. On Vehicular Technology, 42(4):657-672, Nov. 1993.

References

1. 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.

2. 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

3. 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.

4. 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.