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CSE 535 autonomous braking system
创建日期：2024-08-15 17:36:34
标签： CSE 535
autonomous braking system

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

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

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

model.

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