代做CSE 535、MATLAB编程设计代写

日期：2023-10-03 08:59

CSE 535 Project 2: Context-Aware Adaptation

**Note: The provided content, along with any associated project materials, is exclusively intended for

classroom use. Any form of sharing, posting, or redistribution, even after the completion of this course, is

strictly prohibited in accordance with ASU regulations. Such actions are considered violations of the FSE

code of conduct and may result in severe academic consequences.

In Project 1 you learnt about developing a monitoring application using on-board sensors in a smart

phone. In this project, we will design a context aware adaptation strategy for the development of

autonomous braking system for Level 3 autonomous cars which assumes an underlying distributed

monitoring and actuation cyber-physical system specifically human-in-the-loop and human-in-the plant.

Before we delve into the details let us understand some basic definitions.

Level 3 autonomy: The vehicle operates autonomously, however, requires the driver to be

attentive at all times for potential switch to manual mode.

Controller: It is a software that takes sensor data from the vehicle such as speed, distance

between cars and acceleration or deceleration and outputs the braking pressure to apply

deceleration to stop the car.

Controller gain: It is a property of the controller that determines how aggressively a controller

will apply brakes and stop the car.

Deceleration Limit: This is the maximum limit on the deceleration applied by the controller.

High deceleration can lead to unwanted harm to the driver behind the wheel. The controller can

never apply deceleration that is higher than the maximum limit.

Controller frequency: It is the number of times the controller needs to compute a new braking

input per second.

Reaction time: It is the time taken for the human to decide on an action after a switch to

manual mode

Action time: Time taken to execute the action decided by the human and stop the car.

Autonomous vehicle braking system example

Figure 1: Autonomous Driving example

Consider a user driving a Level 3 autonomous car (Fig. 1). We are specifically focusing on the

autonomous braking system. As a part of the autonomous braking system, there is an advisory control

that:

a) processes a driving scenario, by sensing environmental conditions

b) Predicts if the autonomous braking system will be able to stop the car

c) Determines if the control should be transferred to the human behind the wheel

Autonomous braking system has a feed-back loop controller that senses the distance between the

autonomous car   and the car in front  , and computes a braking force as a function of the  ’s initial

speed  !, and distance between   and  . The braking force applied is also a function of the controller

gain  , which is a design parameter that you will have to tune.

The braking force is constrained by the maximum deceleration limit  "#$, which is dependent on the

road condition. For example, on dry road you can apply more deceleration than on wet road in rainy

season. The braking force is thus given by the following equation:

               = min ( ( !,  !%,  ),  "#$).

Once the braking force   is applied, the vehicle kinematics show how the vehicle behaves in the real

world. Given the initial speed  ! and initial distance between   and  ,  !%, the vehicle kinematics gives

the stopping distance  &'(), and stopping time  &'() for the vehicle. This you can obtain using the

vehicle kinematics Simulink model ( *+,-./+) provided to you.

If  &'() ≥  !% then the autonomous vehicle fails, and the cars collide, else there is no collision. If cars

collide, then stopping time  &'() is same as collision time  ..

In this example, we will fix the distance between cars   and  ,  !%. However, changing initial velocity  !,

controller gain  , and deceleration limit  "#$, can change the stopping distance and stopping time

resulting in potential collisions.

Human driver model

The human driver is assumed to be alert all the time to take over control. However, we have to account

for the reaction time  0 of the human driver after a switching signal is sent through the dashboard

controls. In addition to the reaction time the human action also takes some time to execute.

In this example, we assume that the human provides a deceleration that is 10% higher than the

deceleration limit  "#$, i.e. ℎ# = 1.1 "#$. The action time  # can be obtained by removing the braking

controller from the Simulink model and applying a static deceleration of ℎ# into the vehicle kinematics

model. After simulation the action time can be obtained as the stopping time reported in the Simulink

model.

The total time taken by the human to completely stop the car is ℎ&'() =  0 +  #.

We are now in a position to define a basic logic for the advisory control. The advisory control uses the

following algorithm to decide if it wants to switch:

SWITCH = ADVISORY CONTROL ( !, , "#$, 0)

Step 1: Predict if autonomous braking control collides or not using  !,  ,      "#$.

Step 2: If no collision then

Step 3: Do not switch to manual control

Step 4: Else

Step 5: Collision time  .= predicted stopping time from Step 1

Step 6: Predict action time  #, using  !, remove controller, and constant

deceleration 1.1 "#$.

Step 7: Predict reaction time  0

Step 8: Compute ℎ&'() =  0 +  #

Step 9: if       <  

Step 10: Switch to human

Step 11: Else

Step 12: Do not switch.

Driving Context Changes

Road conditions depend on several factors including:

a) User mobility patterns

b) Traffic conditions

c) Weather patterns

Each context can be modeled using stochastic modeling techniques such as Markov chains. In this

assignment you are given a composite model of driving context that has two states: a) normal with

deceleration limit -200 "

&+.! and b) poor road conditions with deceleration limit -150 "

678! .

The prior probabilities of each state  (      ) = 0.6,      (    ) = 0.4. The transition probabilities

are given by the matrix:

 =

      0.6 0.4

    0.85 0.15

Human Reaction time model

The cognitive workload of a human changes based on driving context. We will assume that normal

driving conditions have low workload, and poor driving conditions require high workload.

Consider Table 1 in https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6338053/pdf/fnhum-12-00525.pdf

The heart rate and respiratory rate is provided for single task under low cognitive workload (LCW) and

high cognitive workload (HCW) tasks. Use the mean and standard deviation to create a gaussian model

of heart rate and respiratory rate. For each driving condition, the heart rate    and respiratory rate  

is sampled from the gaussian model. The respiratory quotient can be computed as    = 90

:0

. The human

reaction time  0 can be computed using the following equation:

0 = 0.01 ∗  

Project Tasks

The project has three specific tasks as shown in Fig 2.

Fig 2: Project tasks

In this project, we will first attempt to design the advisory control for an average user. Then choose a

specific user and modify the advisory control design at deployment. Finally, after deployment revert

back to the design phase to change the advisory control and personalize it for the specific user.

This mimics the standard context aware application development workflow. Here the manufacturer

initially designs a system to be used for a large set of users. After deployment, a specific user can make

some limited configuration changes to the system. The data is collected during deployment. This data is

then used to redesign the system to personalize for specific user profiles.

Task 1: Controller design

Given:

1. A set of initial speeds, 20 kmph to 40 kmph

2. Deceleration limits of normal (-200  /  ) and poor (-150  /  )

3. User profiles

User LCW HCW

HR RR HR RR

1 80 (±  ) 16 (±6) 95 (±  ) 21 (±  )

2 65 (±  ) 13 (± ) 71 (±  ) 14 (±5)

3 61 (±  ) 17 (± ) 92 (±  ) 26 (±  )

To do:

1: Create an average user profile

2. Determine reaction time model

3. Determine controller gain for each road condition such that collision is minimized for the given

initial speed range

4. Determine the number of switches necessary to avoid collision for your design

Task 2: Reaction time update in advisory control

Given:

1. User 3 as the actual user of the system

2. Initial speed range 20 kmph to 60 kmph

3. Fix the gains for Normal and Poor road condition from Task 1.

4. Markov chain model for road condition generation

To do:

1. Using the monte Carlo Markov chain simulation method generate a sequence of length

100 of normal and poor road conditions

2. For each road condition in the sequence, sample the initial speed range uniformly to get

one initial speed

3. For each road condition in the sequence, sample the HR and RR models of User 3 to obtain

one HR, one RR and consequently one reaction time for the user.

4. Evaluate collision status for each road condition in the sequence.

5. Evaluate the number of switches in each road condition in the sequence.

6. Determine the reaction time setting in the advisory control that gives the least number of

collisions with the least number of switches.

Note here that the reaction time of the human user is independent of the reaction time setting of

the controller.

So there will be scenarios where even if you decide to switch according to the current reaction

time of the human driver it is not feasible for the user to stop the car and as a result there is a

collision.

Task 3: Gain personalization

Given:

1. User 3 as the actual user of the system

2. Fixed reaction time setting of advisory control

3. Initial speed range of 20 to 60 kmph

To do:

1. Update the gains for each road condition such that the number of collisions and number

of switches are both reduced.

DETAILS FOR PROJECT SUBMISSION:

Software required: MATLAB

To get Started with MATLAB:

Refer to the documentation uploaded on Canvas – ‘Software Requirements and Guidelines for Project 2’

Provided Files:

You have been provided with some files in the Github repository:

https://github.com/ImpactLabASU/MC535Project2

These files contain components related to the project or task you are working on.

Deliverables:

1.Kindly submit three separate MATLAB files for Task 1, 2, and 3, along with an output CSV file.

2.The MATLAB submission files should follow the naming convention:

<FirstName_LastName_TaskNo>.m

3.All output data should be saved in a CSV file containing two columns: 'Tasks' and 'Output.' The task

name should adhere to this format: Task<TaskNo>.<ToDoNo>: <yourOutput>. For instance, if Task 1

involves 2 switches, it should be displayed as follows in the CSV:

Tasks Output

Task1.4 2

Append all outputs of Task 1, 2 and 3 to the same csv file. The naming convention for the CSV file should

be <FirstName_LastName>.csv