Intelligent Agent for Driving Safety

This article discusses the design and implementation of an intelligent agent for driving safety. It explores the use of a convolutional neural network (CNN) algorithm to analyze and predict driving postures. The agent aims to help drivers drive safely by monitoring and preventing distracted driving, such as using a mobile phone while driving. The article also highlights the importance of driving laws and provides statistical evidence on the dangers of distracted driving. The prototype design and steps of the intelligent agent are explained, along with the pseudocode algorithm and code implementation using the CNN algorithm. Overall, the article emphasizes the need for intelligent systems to improve road safety.

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INTELLIGENT AGENT
ARTIFICIAL INTELLIGENCE
5/31/2019

Abstract
Driver’s basic perceptive procedure has long been documented because of the primary
conducive factors in guests’ injuries. It takes a glance at offerings singular device that applies
the convolution neural community (CNN) to automatically analyse and predict predefined
riding postures. The essential plan is to help the drivers to drive properly. In distinction to
older techniques, CNN will habitually know the way to differentiate functions directly from
raw photos. Within the authors' research, a CNN version becomes 1st retrained by the manner
of associate unattended characteristic gaining data of method known as distributed filtering
and later fine-tuned with a category. The approach becomes incontestable usage of the
Southeast University mistreatment posture info set that comprised videos covering four riding
postures, which are normal riding, responding to a cellular Smartphone decision, smoking
and eating. Compared to alternative noted processes with special photograph descriptors and
kind methods, the author’s scheme achieves nice performance with associate usual accuracy
of 99.78%. To gauge the generalization performance and effectiveness in additional wise
conditions, the approach equally examines the employment of alternative specifically
designed information units that recall of terrible illuminations and distinctive road conditions,
reaching associate traditional accuracy of ninety-nine, three and ninety-five, seventy-seven
percent, sequentially.

Contents
INTRODUCTION.....................................................................................................................................3
Driving...............................................................................................................................................3
Cell Phone Distraction to Driving.......................................................................................................3
Statistical Evidences...........................................................................................................................3
Driving Laws.......................................................................................................................................4
DESIGN..................................................................................................................................................4
Prototype Design...............................................................................................................................4
Intelligent Agent................................................................................................................................5
Steps..............................................................................................................................................6
Pseudocode Algorithm......................................................................................................................6
Code Implementation Using the CNN Algorithm...............................................................................7
CNN Algorithm...............................................................................................................................7
Purpose and Functionality of the Code..............................................................................................7
Components (Environment, Perceptron’s Actions, Functions, Attributes)........................................7
Agent Type.....................................................................................................................................8
Percepts.........................................................................................................................................8
Actions...........................................................................................................................................8
Goals..............................................................................................................................................8
Environment..................................................................................................................................8
Search Based Technique Used...........................................................................................................8
Decision Tree.....................................................................................................................................9
CONCLUSION.........................................................................................................................................9
REFERENCES........................................................................................................................................10
INTRODUCTION
Life is precious and invaluable than anything in the universe. Having said this the risks that
threaten sustainability of life must be minimized to the best possible extent, by maintaining
the lifestyle and manners accordingly.
Driving
Driving is movement of a motor vehicle, such as buses, trucks, cars and motorcycles, while
controlling the operation. The aspect of driving, in relation with safety of life should be
explored and best driving methods and habits must be determined, in order to minimize
accidents.

Accidents are possible with the following safety issues, while driving.
1. Texting and talking over phone, while driving
2. Sleep deprived driving
3. Speeding
4. Driving impaired by drugs directly or with influence
5. Reckless driving
6. Distracted driving
7. Street racing
All the above 7 factors have negative influence on driveability, which is the smooth delivery
of power.
Cell Phone Distraction to Driving
At any instance of time, there are about 660,000 drivers attempt using phones in hand, while
driving. Eventually, distraction rates for driving by cell phones are huge and alarmingly high.
Statistical Evidences
 According to the reports of National Safety Council usage of cell, while driving result
more than 1.5 million crashes, every year.
 Texting while driving cause injuries up to 390,000, from accidents, every year.
 1 car accident out of 4 is caused by simultaneous driving and texting, in the United
States.
 Cause of accident, while texting and driving is 6 times more likely, compared driving
while drunk.
 The driver’s attention is taken away for 5 seconds, while answering a text and this
time is enough to travel the football field length, while travelling at the speed of 55
mph.
 Compared to all other tasks related to cell phone, the most dangerous activity is
texting, by far.
 74% of drivers wish and support ban over usage of hand-held cell phone.
 94% of drivers wish and support ban over texting in the cell phone, while driving.
Driving Laws
To minimize the accidents, driving laws have been are defined and imposed, in almost all the
countries. Every driver, who comes on the road, is subject to the respective laws of the
jurisdiction, where the driver is driving.
And Australia government has imposed the law related to driving and using the mobile phone
and stated as follows.
“A driver of a vehicle can only touch a mobile phone to receive and terminate a phone call if
the phone is secured in a mounting affixed to the vehicle. If the phone is not secured in a
mounting, it can only be used to receive or terminate a phone call without touching it (e.g.
using voice activation, a Bluetooth hands-free car kit, ear piece or headset)”.
The law also indicates that…

It is illegal for the driver of a vehicle to create, send or look at a text message, video
message, email or similar communication, even when the phone is secured in a mounting or
can be operated without touching it.
GPS may be used by a driver whilst driving if no touch of the keypad or screen is required.
Unlike NSW, in WA the same rules apply to all drivers, including L and P plate drivers.
… according to safety commission, 2019
These laws are the same for all the territories and states in entire Australia. The vehicle driver
mustn’t make use of the mobile phone no matter vehicle is stationary or moving, except while
the vehicle is parked. Sometimes the driver may be exempted from this law, because of
another law applied in the same jurisdiction. This law is not applicable in case the cell phone
gets mounted and securely fixed or positioned in a way, if a hands free device is used by the
driver. Learner drivers or provisional drivers are banned from using the mobile phone, in
some locations, while they attempt to control the vehicle.
Hence, there is a requirement to design a new detecting system that can monitor the activities
of the driver, especially, when using the mobile phone. Various activities performed with the
driver must be monitored closely and then the respective actions must be performed
accordingly. This intelligent system should be able to alert the driver with the warning
messages, when he or she attempts to operate the mobile, in restricted ways. The same
respective agent should also release penalty after throwing warning message to the driver.
This system can help the driver to drive the vehicle without operating the mobile, except
connecting the call or disconnecting the call, while the mobile phone is mounted to the stand
near the driving seat in the vehicle. Other operations, like texting the messages, gendering at
the instant messages and video messages are considered as unlawful actions.
DESIGN
Prototype Design
A prototype is a model, release or sample of a new product, built for testing a determined
process or concept to be learned or replicated from. A prototype helps in a new design
evaluation that helps to enhance precision by users and system analysts (Blackwell & Manar,
2015).
Intelligent Agent
The intelligent agent is designed to allow only two operations, connecting and disconnecting
the calls. This intelligent agent receives the inputs, processes the input and releases the
output. There are different ways input is accessed by the agent and different ways that each of
its output operation is performed.
A vehicle driver can use the cell phone and contact it only for two operations, accepting call
and ending calls. Initially, intelligent agent verifies whether the handset is verified in
mounting attached to the vehicle or not. It is important that the handset is mounted to the
vehicle, as it can eliminate the need for holding the cell phone with the hand, since hands of
driver are always busy holding the steering and gears. Attending and speaking over the phone

can be conducted by utilizing headset, Bluetooth or just without the need for holding the cell
phone.
In intelligent agent product, key persistence of prototyping is to include users, drivers, in this
context in taxing and gather opinions and reviews from clients, during initial levels so that the
period back is scaled and scale the value. The prototype allows the designer to be economical
and experimental with the same design and review the results. The changes that can be made
possible with the prototype do not need more money and resources, except the time.
Figure 1: Intelligent Agent Prototype
The prototype has been designed to take the inputs from different sources, called sensors and
then makes the decisions according to the inputs and finally the outputs are delivered (Harper
& Douglas, N.D).
Steps
1. Initially the process starts with monitoring the basic state and activity of the driver,
driving the vehicle.
2. The intelligent agent initiates its functionality, when the driver starts using the mobile
phone. After the driver initiates to use the mobile phone for any kind of operation, it
starts the process by monitoring other inputs about the status and state of the phone.
3. As the third step, after the driver initiates to use the mobile phone, the intelligent
agent starts checking the state of the mobile phone. It checks two conditions, whether
the phone is secured and fixed in mounting or phone is not secured in mounting. In

either cases, the intelligent agent takes two different processes, on the basis of
condition fulfilled.
4. As the fourth stage or fourth step, the agent checks whether the driver is using hands
free, Bluetooth or headset, it takes the output to the following stage. On the other
hand, if the phone is not secured in mounting it still checks whether the driver is using
handsfree, Bluetooth or headset, it passes the its results and outputs to the next stage.
And in case the phone is not secured in mounting and the driver does not use any
handsfree, Bluetooth or headset, the agent performs any one of the two actions, either
accepting the call or create, send or look for the messages.
5. As the final or fifth step, the agent outputs its results as ‘safe’ otherwise, it sends a
‘warning message’.
The entire functionality of the intelligent agent is performed in these three steps or stages.
PSEUDOCODE ALGORITHM
Driver of a vehical;
Using Mobile Phone;
if(During Driving){
Phone is not secure in mounting
While(Warning)
}
function driver-Agent([location,status]) returns an action
if status
= Using mobile phone during driving then return Warning
else if location
=
A then return Right
else if location
=
B then return Left
def driver-Agent(sensors):
if sensors[’status’] == ’Using mobile phone during driving’:
return ’Warning’

elif sensors[’location’] == ’A’:
return ’Right’
elif sensors[’location’] == ’B’:
return ’Left’
Agent Programs
Function driver(percept)returns action
Static driver, the driver drive a vehicle
memory ← UPDATE-MEMORY(memory, percept)
action ← CHOOSE-BEST-ACTION(memory)
memory ← UPDATE-MEMORY(memory, action)
return action
The pseudocode algorithm for intelligent agent performs its designed functions, in the
following ways.
Driver will need to understand and interpret where the vehicle is and is there anything else
standing on the road and the speed of the vehicle, with which it is running then. The driver
has to focus on driving in order to have good control over the vehicle. The driver should
ensure that the mobile phone is secured to the mounting, before even starting the vehicle.
Driver should only attempt to press a button or touch a button, to connect to answer the call
and the same to disconnect the call and these actions are accepted only when the driver makes
use of Bluetooth, hands-free, headset etc., to attend the call.
The algorithm ensures that each step of the prototype design is implemented using the
specified programming language.
CODE IMPLEMENTATION USING THE CNN ALGORITHM
CNN Algorithm
Convolutional Neural Network is a deep neural networks class that is applied for visual
image analysis. CNNs are multilayer perceptron’s regularized versions.
The difference between image classification algorithms and CNN algorithms is that the CNN
algorithms are pre-processed. Another major advantage of the CNN algorithm is that it is
independent from human effort and prior knowledge in the design feature. These algorithms
have various applications, including image classification, image and video recognition,
natural language processing, recommender systems and medical image analysis.
Here CNN algorithm has been adapted and implemented to image classification of the driver
and identify what kind of operation that the driver is performing. It detects the image and the
activities of the driver are interpreted. The activity of the driver can be driving, mobile

operation, eating or talking, usually. Based on the image it detected, further algorithm is
processed.
The code is implemented with the help of CNN algorithm of driver deployed using
TensorFlow and Python.
Train The Network:
import numpy as np
import tensorflow as tf
from time import time
import math
from include.data import get_data_set
from include.model import model, lr
train_x, train_y = get_data_set("train")
test_x, test_y = get_data_set("test")
tf.set_random_seed(21)
x, y, output, y_pred_cls, global_step, learning_rate = model()
global_accuracy = 0
epoch_start = 0
# PARAMS
_BATCH_SIZE = 128
_EPOCH = 60
_SAVE_PATH = "./tensorboard/cifar-10-v1.0.0/"

# LOSS AND OPTIMIZER
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=output, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=0.9,
beta2=0.999,
epsilon=1e-08).minimize(loss, global_step=global_step)
# PREDICTION AND ACCURACY CALCULATION
correct_prediction = tf.equal(y_pred_cls, tf.argmax(y, axis=1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# SAVER
merged = tf.summary.merge_all()
saver = tf.train.Saver()
sess = tf.Session()
train_writer = tf.summary.FileWriter(_SAVE_PATH, sess.graph)
try:
print("
Trying to restore last checkpoint ...")
last_chk_path = tf.train.latest_checkpoint(checkpoint_dir=_SAVE_PATH)
saver.restore(sess, save_path=last_chk_path)
print("Restored checkpoint from:", last_chk_path)
except ValueError:
print("
Failed to restore checkpoint. Initializing variables instead.")
sess.run(tf.global_variables_initializer())

def train(epoch):
global epoch_start
epoch_start = time()
batch_size = int(math.ceil(len(train_x) / _BATCH_SIZE))
i_global = 0
for s in range(batch_size):
batch_xs = train_x[s*_BATCH_SIZE: (s+1)*_BATCH_SIZE]
batch_ys = train_y[s*_BATCH_SIZE: (s+1)*_BATCH_SIZE]
start_time = time()
i_global, _, batch_loss, batch_acc = sess.run(
[global_step, optimizer, loss, accuracy],
feed_dict={x: batch_xs, y: batch_ys, learning_rate: lr(epoch)})
duration = time() - start_time
if s % 10 == 0:
percentage = int(round((s/batch_size)*100))
bar_len = 29
filled_len = int((bar_len*int(percentage))/100)
bar = '=' * filled_len + '>' + '-' * (bar_len - filled_len)
msg = "Global step: {:>5} - [{}] {:>3}% - acc: {:.4f} - loss: {:.4f} - {:.1f} sample/sec"
print(msg.format(i_global, bar, percentage, batch_acc, batch_loss, _BATCH_SIZE / duration))
test_and_save(i_global, epoch)

def test_and_save(_global_step, epoch):
global global_accuracy
global epoch_start
i = 0
predicted_class = np.zeros(shape=len(test_x), dtype=np.int)
while i<len(test_x): j = min(i + _BATCH_SIZE, len(test_x)) batch_xs = test_x[i:j, :] batch_ys = test_y[i:j, :] predicted_class[i:j] =
sess.run( y_pred_cls, feed_dict={x: batch_xs, y: batch_ys, learning_rate: lr(epoch)} ) i = j correct = (np.argmax(test_y, axis=1) ==
predicted_class) acc = correct.mean()*100 correct_numbers = correct.sum() hours, rem = divmod(time() - epoch_start, 3600) minutes,
seconds = divmod(rem, 60) mes = "
Epoch {} - accuracy: {:.2f}% ({}/{}) - time: {:0>2}:{:0>2}:{:05.2f}"
print(mes.format((epoch+1), acc, correct_numbers, len(test_x), int(hours), int(minutes), seconds))
if global_accuracy != 0 and global_accuracy< acc: summary = tf.Summary(value=[ tf.Summary.Value(tag="Accuracy/test",
simple_value=acc), ]) train_writer.add_summary(summary, _global_step) saver.save(sess, save_path=_SAVE_PATH,
global_step=_global_step) mes = "This epoch receive better accuracy: {:.2f} > {:.2f}. Saving session..."
print(mes.format(acc, global_accuracy))
global_accuracy = acc
elifglobal_accuracy == 0:
global_accuracy = acc
print("#########################################################################################################
##")
def main():
train_start = time()
for i in range(_EPOCH):
print("
Epoch: {}/{}
".format((i+1), _EPOCH))

train(i)
hours, rem = divmod(time() - train_start, 3600)
minutes, seconds = divmod(rem, 60)
mes = "Best accuracy pre session: {:.2f}, time: {:0>2}:{:0>2}:{:05.2f}"
print(mes.format(global_accuracy, int(hours), int(minutes), seconds))
if __name__ == "__main__":
main()
sess.close()
Purpose and Functionality of the Code
Each part of the code performs the following functions.
The code is written to implement the above five steps, specified in the prototype design.
Components (Environment, Perceptron’s Actions, Functions,
Attributes)
The intelligent agent system has the following components.
Agent Type Percepts Actions Goals Environment
Driver Cameras,
speedometer,
microphone,
mobile phone
GPS, Drive
vehicle, don’t
use mobile
phone while
driving, only
phone calls
accept if (using
hands-free,
Bluetooth, or
headset) else
(Phone is
secured in
mounting
affixed)
Safe, Fast,
Legal, Follow
rules
Roads, other
traffic, safe area,
dangerous area
Agent Type
Driver – Driver is considered as the agent, who performs the actions that are to be monitored
and respective stages of processing are performed. The function of the agent is to map from
percept histories to actions. Here, agent is considered as something that can be viewed as
perceiving its environment through sensors and acting upon that environment through
actuators.

Percepts
Percepts are the robotic agents, through which the behaviour of the environment is accessed
and taken as inputs to the intelligent agent system. Human agents that input the surrounding
environment status to the brain of human are ears, eyes and other sense organs. Actuators are
the hands, legs mouth and other human body parts to perform the output actions. The
following are the percepts of the intelligent agent systems.
a. Cameras to access the image of the driver to interpret and perceive the action
b. Speedometer to record the present speed of the vehicle
c. Microphone to access the audio input from the driver’s speaking
d. Mobile phone to see how the mobile phone is operated by the driver
Actions
e. GPS, to restrict the use of it
f. Drive vehicle, action to indicate the driver to proceed
g. Don’t use mobile phone while driving, warning action to the driver
h. Only phone calls accept if (using hands-free, Bluetooth or headset) else
(Phone is secured in mounting affixed), to inform the driver to connect or
disconnect the call
Goals
i. Safe, indicating safe to use to attend, disconnect call
j. Fast, indicating faster driving of the vehicle
k. Legal, indicating the action to be legal
l. Follow Rules, informing the driver to follow the rules
Environment
m. Roads, the real picture of road and objects on it
n. Other traffic, indicating surrounding traffic
o. Safe area, relevantly safer area for parking or stopping vehicle
p. Dangerous area, relevantly dangerous area to inform to driver
Search Based Technique Used
The intelligent agent system makes use of the model, based on reflex agents search base
technique. It can sense the environment and grip perceptible surroundings moderately. The
system preserves these sensed inputs and performs the actions accordingly.
It should keep some kind of inner prototype that depends on the history of percepts and so it
imitates minimum some of the characteristics unseen of present states. History of percept and
the effect of an act on the atmosphere can resolute by using an inner model. Then the act is
chosen accordingly, in the alike way as a reflux agent.

Decision Tree
CONCLUSION
Life is the most important one than anything else in the universe and this life has to be saved
very preciously. Unfortunately vehicle accidents end up millions of lives in the world every
year and these accidents are majorly because of drunk and drive and most importantly
because of mobile operation and texting operation. So, this mobile operation is regulated by
many countries and in Australia.
To regulate the operation of the mobile by the vehicle driver, intelligent agent system is
designed and developed. This intelligent system understands the environment and the
activities of the driver at any instance, with the help of sensors. Later based on the inputs
provided by the sensors, the internal processing is performed and based on the conditions,
respective actions are performed. The outputs and related actions are performed with the help
of actuators. The intelligent agent system is designed to ensure that the driver makes use of
the mobile limitedly and only in lawful ways. Hence, there are two important conditions set
to fulfil for safer vehicle driving. Firstly, the mobile has to be fixed to a mounting and
secured without any movement. Another condition to be fulfilled is that the driver has to
make use of the Bluetooth, headset or hands-free to attend and answer the call. When these
two conditions are fulfilled, then only intelligent agent system allows the driver to operate the
mobile. The mobile operation is also limited to press a button or touch the screen to answer or
to disconnect the call. If the driver attempts to interact with the messages, there will be a
warning message and penalty system activated.
The intelligent system has been designed starting with a prototype and then the coding is
done accordingly, by implementing CNN algorithm, and TensorFlow and Python
programming languages.

REFERENCES
1. Blackwell, A. H., Manar, E., eds. (2015). "Prototype". UXL Encyclopedia of
Science (3rd ed.).
2. Harper, Douglas. "prototype (n.)". Online Etymology Dictionary.