M. Tech. Project Presentation

Automatic Cruise Control System

By: Rupesh Sonu Kakade05323014

Under the guidance of

Prof. Kannan Moudgalya and

Prof. Krithi RamamrithamIndian Institute of Technology, Bombay

10 July 2007

Overview Introduction Objectives Automatic Cruise Control (ACC) Control in stop-and-go traffic Results Conclusion Future Improvements

Introduction

Conventional Cruise Control

Difficulties:1. Useful only in sparsely populated roads

2. Disengagement may result in driver

loosing control of a car.

Velocitycontrol

Driver Set

Speed

Introduction

Automatic Cruise Control (ACC) System

Control Objectives:

1. Follow-the-leader car

2. Adapt to leader velocity

Introduction - ACC

Introduction - ACC

Safe Inter-vehicle distance Rule:1. Constant spacing policy – Safe distance is independent

of vehicle parameters such as maximal velocity, deceleration, etc.

Introduction - ACC

2. Constant time-gap policy:

Difficulties with ACC:

1. Federal and State laws prohibits the use of ACC system below

certain speed value.

2. Human driving often results in

excessive accelerations and

decelerations. Thus violating

comfort specifications.

Introduction

Stop-and-go scenario demands a different behavior from vehicles.

Control in stop-and-go scenarioControl Objectives:

1. Safety Constraint: Stop the vehicle before it reaches a critical distance, .

2. Comfort specification: Keep the

deceleration and jerk bounded.

Overview Introduction Objectives of project Automatic Cruise Control (ACC) Control in stop-and-go traffic Results Conclusion Future Improvements

Objectives of Project

Design control systems for1. Speed control - in conventional cruise control

2. ACC controller

3. Controller for stop-and-go traffic

and4. Integrate controllers on

low-cost platform

Approach used

Zones:

1. Blue Zone: Cruise control

2. Green Zone: Automatic cruise control

3. Orange Zone: Stop-and-go traffic control

4. Red Zone: Safety critical zone

Overview Introduction Objectives of project Automatic Cruise Control (ACC) Control in stop-and-go traffic Results Conclusion Future Improvements

Automatic Cruise Control

Control Objectives:

1. Follow-the-leader car, i.e., distance error should be minimal. Distance error is computed from

where,

2. Adapt to leader velocity, i.e., relative velocity between two vehicles should be minimal.

ACC Control Law: The first time-derivative of distance error is

computed and solved the following equation

which ensures the distance error reduces to zero. We have

ACC

The control structure is similar to PD controller with,

1. Proportional gain

2. Derivative gain

p

kk

h

1dk h

ACC Control Scheme

Overview Introduction Objectives of project Automatic Cruise Control (ACC) Control in stop-and-go traffic Results Conclusion Future Improvements

Control during stop-and-go scenario

Control Objectives:1. Safety Constraint: Stop the vehicle before it reaches a

critical distance, . 2. Comfort specification: Keep the deceleration and jerk

values bounded for all t.

Reference model:Input: Lead vehicle velocity and

Output: Reference distance andreference acceleration

Control during stop-and-go scenario

Control during stop-and-go scenario

Reference model has twofold objectives:

1. Reference distance computation:

2. Reference acceleration computation:

Safety and comfort constraints

rd

Control during stop-and-go scenario

Objectives: To find constraints on c and so that safety and comfort specifications are satisfied for all initial conditions and .

Initial conditions are defined as

where t = 0 s, is the time when

Orange Zone is reached.

Solving and

Control during stop-and-go scenario

where =rfx

Control during stop-and-go scenario

Solving the previous expression, we have

The maximum penetration distance is

This gives us a lower bound on c

Control during stop-and-go scenario

Next we find upper bound on c. Substitute in expression for reference acceleration, i.e.,

The maximum value of reference

breaking is computed from

Control during stop-and-go scenario

Substitute in , we have

Control during stop-and-go scenario

Now we consider comfort specification, i.e., jerk values must also be bounded. This gives us another upper limit on value for c.

The maximum value of jerk is

believed to depend on extremes of

Control during stop-and-go scenario

The expression has two solutions.

i.e., estimated lead velocity assumed

to be zero. Therefore maximum value

of jerk could be computed from

Control during stop-and-go scenario

To proceed we assume

i. e., negative acceleration is always greater than positive acceleration.

The maximum jerk will be

bounded as

Control during stop-and-go scenario

Assuming sufficiently large for The previous expression

yields another upper bound on value for c.

C1 and c2 are associated with safety

Whereas c3 is associated with comfort

Control during stop-and-go scenario

In the Orange Zone, priority is given to safety, i.e.,

Next we determine the lower bound on the value of .We use the above expression together with

If takes the smallest value thenc takes on the largest value.

Control during stop-and-go scenario

Overview Introduction Objectives of project Automatic Cruise Control (ACC) Control in stop-and-go traffic Results Conclusion Future Improvements

Results

We implemented ACC controller on Dexter-6C. This platform is relatively reach in a sense that it has

1. Independent steering controller

2. Independent drive controller

3. Independent controller for white line sensing Our objective was to implement control system on a

low cost platform, such as CDBOT.

The experimental results on CDBOT are also presented.

Results

Figure: Dexter-6C, a test car

Results - On Dexter-6C

Fig.: Speed control loop performance Fig.: Car-following (ACC) results

Results - On Dexter-6C

Fig.: Time-gap results

Results – On CDBOT Inner speed control loop performance test

ACC Results – On CDBOT

Results – On CDBOTControl in stop-and-go scenario

Overview Introduction Objectives of project Automatic Cruise Control (ACC) Control in stop-and-go traffic Results Conclusion Future Improvements

Conclusion Different traffic densities is found to demand different

behavior from vehicles.

Controllers for longitudinal speed control of cars during sparsely populated road, moderate traffic, and stop-and-go scenarios are designed.

Controllers were integrated on robotic platform, CDBOT. Also ACC controller was implemented on Dexter-6C.

Overview Introduction Objectives of project Automatic Cruise Control (ACC) Control in stop-and-go traffic Results Conclusion Future Improvements

Future Improvements

1. ACC controller used PD structure. Due to its non

perfect tracking, jerk values are some times higher.

This aspect could be improved by using advanced

controller such as controller based on adaptive control

theory.

2. String (or platoon) stability problem is not analyzed

here.