According to statistics more than 1.2 million people die each year on the road accidents. 20 to 50 millions of people suffer from non-fatal injuries due to these road accidents across the world. According to the NHTSA (National Highway Traffic Safety Administration) in the year 2008, it was estimated that 5,870 deaths and 350,000 injuries were due to lack of driver concentration on the road. Drivers stress behavior may be the reason for lack of inattention, which causes the accidents. Drivers under the influence of alcohol causes severe damage of life and property as well as the loss of the economy. Alcohol and drugs are the major factor for road accidents (Ministry of Road Transport & Highways, 2015).

Due to drunken driving, drivers loose their control and accidents occurs. In recent years the number of accidents in vehicle has been increased manifold due to aggressive driving, sleeping disorders and fatigue, as they cause lack of concentration among drivers while driving. The statistics in Figure 1 shows a linear relationship of accidents. Day by day the growth of vehicles on road increases and along with that the number of accidents also increases (Mukhtar et al., 2015).

Figure 1. Number and Percent of Alcohol Related vs. Nonalcohol Related Highway Vehicle Fatalities: 2001-2010

Bhumkar et al. (2012) illustrated how to prevent road accidents using automated signal processing systems. Kasde and Sugapriya (2016) provided an eye and head location visual examination for continuous monitoring of the alertness vehicle drivers. To collect critical information on the driver's non-alertness, the proposed scheme used visual features such as Eye Index (EI), Pupil Activity (PA), and Head Position (HP). Some authors created an automatic system using computer vision. It detects yawning using an embedded smart camera platform (John et al., 2008). A survey has been done that explores into the wellestablished strategies for tracking driver inattention and exploring the use of cutting-edge and futuristic methods that take advantage of mobile technologies. The studies were divided into two categories: drowsy driving and distracted driving (Priyanga et al., 2014). Few authors used an MCU electronic circuit board in the device along with an alcohol sensor MQ303A to detect the alcohol concentration, which is a serious problem in modern society (Ali & Alwan, 2015). They created a real-time monitoring anti-drunk driving device for automobiles. By forwarding the configuration of a combination of the alcohol detection and also the image processing surveillance, the driver's uniqueness is secured by combining the feature of alcohol detection with the face recognition device (Chaudhari & Naik, 2016). Raya and Hubaux (2005) demonstrated how to use an eye tracking device, to detect drowsiness in a driver. It has to do with the Viola-Jones algorithm and the Percentage of Eyelid Closure (PERCLOS). If the drowsiness index reaches a predetermined threshold, the device will notify the driver (Lee et al., 2014). Some authors suggested a secure real-time embedded system for tracking the driver's loss of attention during the day and night driving conditions. The alertness level has been measured using the percentage of closed eyes. The eye condition has been labelled as open or closed using support vector machines after the face has been identified using HAAR-like features.

There are both transmitter and receiver section for the system. Figure 2 and 3 shows the system setup inside the vehicle. In the transmitted section with the help of LDR, the system automatically controls the headlight of both vehicles (Sivaraman & Trivedi, 2013). An RF transmitter is kept on the transmitter block and a value is set. If the value of the light exceeds the constant value the headlight is automatically dimmed in the opposite vehicle. The headlight is controlled automatically at a particular distance.

Figure 2. The System Setup of Transmitter

Figure 3. The System Setup for Receiver

The alcohol sensor will detect the alcohol content from human (driver) breath and send its value to microcontroller. Alcohol sensor is suitable for detecting alcohol concentration just like common Breathalyzer. It has a high sensitivity to small value of BAC and fast response time, providing an analog resistive output based on alcohol. The LCD display fitted inside the car act as an indicator to the driver and other passengers inside the car. This display gives indication of alcohol detection level by alcohol sensor. This provide warning message to the driver to stop the vehicle within particular time. Afterwards car will automatically stop, indicating smoke/gas detected in car. In this we use GSM that can accept any GSM network operator SIM card as like a mobile phone with its own unique phone number. Applications such as SMS control, remote control and logging can be developed easily. The modem can be directly connected to PC serial port or to any microcontroller.

Brightness control (dimming the headlight) will be controlled when wireless is available on vehicles. An RF transmitter and receiver is kept on both the vehicles. There is no need for the driver to dim and brighten the beam. When there is a brake failure or fuel dryness detected, anti-lock system in the engine is activated. The brake failure sensor and fuel dry sensors are activated (Srijayathi & Vedachary, 2013). Figure 4 shows the transmitter kit where you can see a light dependent resistor. A value of 200 is set to analyze the control. Driver's drunkenness is detected by the alcohol sensor. If the alcohol is detected, sensor gives alert to the driver and a message will be passed to the corresponding authority. If sensed, the engine will automatically stop and it is displayed in the LCD display (Varma et al., 2012). The eye blink sensor gets activated when the driver is drowsy. The eye blink time is set for 10 seconds. If the driver closes the eye for more than 10 seconds, automatically the engine will stop. The fuel dry sensor is in floating state. When the fuel is nearing the empty condition, the system stops and the output is shown in the LCD display.

Figure 4. Code Segment (a) Alcohol Detection (b) Headlight Brightness Control

Figure 5. Transmitter Kit

It has become a daily occurence for us to read accident reports involving driver inattention, such as drowsiness, excessive speed, and drunken driving. In this project, we propose protocols for the identification of excessive alcohol consumption and various sensors to perform certain task in the driving system. This project uses the Arduino UNO controller to detect consumption of alcohol, brake failure, fuel emptying, the driver's drowsiness, and vehicle speed. In addition to these parameters, the headlight intensity of the vehicle is altered on the basis of data received by the RF transmitter located in opposite vehicle. Future Enhancement The image processing technique can be added to this project along with head movement detection. ECG signals of the heart and EEG signals of the brain may also be used to detect alcohol and drowsiness. For automatic headlight control, the government should take the initiative to raise public awareness about the importance of keeping the proposed device inside the car. Ackonowledgement We are in deep gratitude to Mr. Prakash, Technical Director (Nanotronics) for helping us throughout in this project.