prasad-kumkar/ai-driver-safety

Intelligent Driver Monitoring system for Autonomous Vehicles

Deep Learning based driver monitoring system (activity & object recognition)

Problem

In recent years there has been a lot of focus on developing driver monitoring software for integration in passenger cars and other vehicles to facilitate better safety and other functions that improve the user experience. By studying a person’s posture and body movements, intelligent interior vehicle algorithms can draw conclusions about a person’s alertness, attention and focus. Tomorrow’s cabin sensing features will include detection of passenger position, safety belt status and forgotten objects, as well as enabling multimodal functionality such as deeper AI and mood recognition.So the car is able to seamlessly transfer control of the vehicle to an awake and able driver, call for help in a medical emergency, or offer to play the perfect song for the moment.

Solution Approach:

By Computer Vision

1.Identification of the driver in order to allow the vehicle to automatically restore its preferences and settings.

2.Activity recognition:
2.1 Deep learning model for recognition of continuous driver’s activity.
2.2 This includes activities such as driver talking on the phone, eating while driving the vehicle. These activities will alert the system and make the driver more aware of the dangerous situation.

3.Detecting levels of driver impairment:
3.1 Using a camera and microphone for detecting drowsiness, distraction, yawn, eye closure, and joy in real-time.
3.2 Monitor driver fatigue and alert him when potential drowsiness situation is detected.
3.3 Monitor driver attentiveness by ensuring he’s keeping his eyes on the road and that he is aware of any dangerous situation.
3.4 Pilot a user interface thanks to the eyes by automatically selecting HMI areas.

4.Hand gesture control:
A trained neural network to detect hand gestures for volume/ channel control or any other in built functionality of the car.

5.An intelligent steering wheel that monitors the heart rate to detect potential drowsiness of the driver. This is made by embedding the hand-grip heart rate monitor into the steering wheel of the vehicle.

6.After detection of the driver’s fatigue while driving, the driver can be alerted with special sensors or an electric impulse bracelet.

Driving Style Classifier-AI:

The Driving style is simply analyzed by computational methodologies (Artificial Intelligence) and applied computing of transportation.

Implementations of all Classifications Using Fuzzy Logic model

Fuzzy Logic Model-A branch of Artificial Intelligence (AI), which will characterize the uncertainty in the data by adding truth and false concepts from common logic to a machine-generated model.

Aggressive Driving Style Criteria: (Input Variables)

1.Sudden Accelerations or Decelerations
2.Sudden Braking
3.Sharp Turns
4.Set of events like start, stop, speed and turns
5.Maximum and minimum rpm of the engine
6.Number of Red light Jumps
7.Number of Tailgating cases
8.Number of Aggressive Honking
9.Number of Wrong side Overtaking

Steps Involved

1.Fuzzification:
This stage defines the membership functions and linguistic variables of the inputs.

2.Rules Evaluation: In this stage, we will apply the fuzzy logic rules to calculate the output.

3.Defuzzification:
The final conversion of the inputs to crisp results.

Threshold Criteria:

(to be set for all classifications) Example- the Threshold for harsh accelerations and decelerations has to be decided in by the System In accordance with the type of Road it is running on For example a city road or a state Highway or a National Highway. Because the speed limits will be different.

Novelty:

Our solution is a combination of three different approaches, which increases accuracy. This is a more intuitive use of the new generation of driver assistance functions. Most solutions being tested are based on just image processing. Combining computer vision, driving style, and heartbeat analysis and testing them has not been tried before.

Implementation Plan:

Implementation is divided into the following parts:

1.Data acquisition : A camera module is attached in front of the user which is continuously monitoring the activity of the driver. The hand-grip heart rate sensor embedded onto the steering wheel gives us the bpm of the driver.

2.Pre-processing : Filtering on sensor data.

3.Data processing/ Feature Extraction: AI/ Deep learning-based image processing for detecting the activity of the driver.

4.Classification: Fuzzy classifier to classify the driver’s state by scaling drowsiness, distraction, yawn, eye closure, and joy in real-time based on the threshold values.

Usage:

git clone https://github.com/prasad-kumkar/ai-driver-safety.git

Download models, haarcascade files and shape predictor file and put it in main folder. Install required libraries:

• opencv-python
• dlib
• keras
• imutils
• tensorflow
• keras
• numpy
• pygame

Face landmarks:

Use of deep learning library dlib's facial landmark predictor

Eye blinking counter

Detection of eye blinks and if more frequent eye blinks are detected, then play an alarm.

Drowsiness Detection

Use of opencv and dlib library to detect and Deep learning algorithm to detect the driver's drowsiness in real time and play alarm sound if eyes are being closed for more than given frames.

python drowsiness_detection.py

Activity Recognition : Yawning

Detection of yawn by analysing facial landmarks of mouth and eyes.

python yawn.py

Mood/Emotion recognition

Use of tensorflow library and training model to classify mood/emotion in real time.

python mood_recognition.py

ai-driver-safety's Issues

HeartBeat code not added

Need to start working on analysis on heartbeat when the driver is feeling drowsy:

• Read accurate heartbeats : Use of Arduino/ESP or any other alternatives and HB sensor
• Plot them
• Analysis for drowsiness

Implementation of Measuring Head Rotation

Measuring head-rotation for potential distraction detection:

• Pitch, Yaw and Role measurements
  Maybe use of dlib for detecting facial landmarks and their relative positions to measure

Need suggestions for activity recognition.

Getting started for implementing activity recognition of driver while driving. Few of the potentially dangerous activities could be:
Talking on the phone, Eating or Looking off-road.

Incorrect predictions in yawn detection algorithm

Lawn detection in yawn.py is calculated based on the ratio of mouth closure, which is calculated using the feature-points we get using the dlib library.
However, not every time the user is open mouth-wide he is yawning. This will result in the wrong assumption of the user being dizzy.
There needs to be a better alternative to this algorithm to detect yawns.
You are open to going through various research papers and coming up with a different approach. Comment your ideas below and let's see if we could implement them.