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Advanced Driving Assistance System


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Md Asaduzzaman, Automotive Software Engineering, Computer Science Department Tu Chemnitz, Germany

Abstract— The main idea to develop an advanced driving assistant system is to predict the danger, make comfortable, safe and economic journey. Surely safety will come first. Various kind of sensors, radar and GPS are used. Driver used to get Equisetic sound and display on the monitor from. Here I would like to add a special feature about specially for heavy duty vehicles. Though calculating road grade is not new but we can add grade value in maps which will help to take the decision to a driver which path need to follow. For this, we can use slandered mounted sensor and GPS receiver and satellite data.


Index Terms— ADAS- Advanced Driving Assistance System, GPS- Global Positioning System, ESC- Electrical Stability Control

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1   Introduction

Advanced driving assistance system(ADAS) is the combination of various subsystems. The System will assist a driver in all aspect. We have to use many types of sensors for various type of assistance. For detecting an object in path and distance between cars two types of radars, cameras are used. These sensors will produce massive data. We have to implement suitable algorithm also for the massive amount of data to for insuring normal driving and global security.

2   Existing System

Existing System has many Subsystems. Adaptive cruise control. Adaptive light control, Auto break, Auto Parking, Blind sports detection, Collation detection, Driver drowsing detection, GPS detection, Hill descent detection, Intelligent speed adaptation, Night vision, Tire pressure measure, among these subsystems I will describe few of them.


2.1 Adaptive cruise control

This is the major driving assistance system. A driver has to monitor the cruise control system for safety only. A car will be slow down and speed up based on object detection. If speed is lower than then the threshold, automatically shout off. Two types of radars are used here. One pair short range to detect stortrange objects, lane lines. Another pair for detecting long range objects 1.


2.2 Adaptive light control

 Adaptive light control system is designed for better vision in darkness. It rotates the highlight and adjusts better illuminate on the roadway through the corner 2.


2.3 Auto break

It is designing to reduce the severity of the high-speed collisions. Its prevent collision. It helps the stability control system also. ESC also involves in this system 3.


2.4 Blind Sport Detection

Many types of sensors are used for this system. It provides the information which is difficult or impossible to see. The system makes an alarm and sensor can detect an object or camera provides an image 4.


2.5 Collision avoidance system

Various sensors and cameras are used to develop this type of system. The system detects the danger of collision with other objects. It will have worn the driver. Some system takes the action to reduce casualty 5.


2.6 Lane departure warning system

System worms the driver if the vehicle goes out of the lane, hitting to another car or running off the road. So that driver can take care of the vehicles. It can do some correction also 6.


2.7 Night vision

Using Night vision system, a user can see an object which is difficult or impossible to see. Active light vision uses infarcted light and passive light vision use thermal energy 7.


2.8 GPS Navigation

GPS navigation is widely used technology for maps. Base on the geographic coordinate system base on latitude and longitude. We can find any position on earth. Using this technology our current navigation map is designed. From this kind of navigation system or maps, we use to get information about distance but not the road grade distance 8. But we can get the road grade distance in our new proposed map. We have to create an additional database for storing road profiles. Like Road names, locations, longitude and latitude coordination.

3 Control Roles

The main Idea of ADAS is to predict the situation and take the necessary to action to the power train. chassis, Breaking base, driver condition. Prediction is done base on the all other subsystems in formation. Control technology will play the Main Idia of ADAS is to predict the situation and take the necessary to action to the paower tain. chassis, Breaking base, driver condition. Prediction is done base on the all other subsystem information. Control technology will play the central role in ADAS. 9

Figure: System reaction after detecting a pedestrain

The figure 9 shows a car detect a pedestrain and then its find some other way. System gather information and apply accordingly.

4 Proposed GPS Navigation system

We can improve maps including road grade. Using GPS and standard hill mounted sensor we can calculate the road grade. Like one junction to another junction. We can add this grade value to a road profile which will be stored in the database. GPS coordination values as key and road grade distance as a value. We can develop a web service base on this key-value pair. By calling this service we can find the total road grade and distance. A driver can take proper decision. Specially for heavy duty vehicles.


4.1 How to calculate

We find the road grade distance. Hight and angle and minimum distance and total distance form one point to other point using standard mounted sensor and GPS.









Figure: For calculating distance and grade distance

From figure 10 we can see relies on that its easy to find the total length, l from car meter. We can find the hight    h from GPS. Lets AB=a and     h=b, just subtracting height at position A from hight at position B. So, we can find the d also.

d=). All these values we can find for all positions.

Here l is the distance with road grade. We can find the road grade distance, gd.

gd= l-d.


Now we can come to conclude that using this method, we can find the average road grade distance from each intersection to other intersection of a road. If there is a dangerous slope in between any intersections, that also can be recorded in road profile. This data also can be used on our map to worn the driver in advance. Which will help the driver to follow another way.


Some calculated values are given from early research document 11.   

Figure: Altitude of every 1000

Figure 11. Show the altitude of every 1000 M distance. We can find the altitude of any points of a road.

These values have been found from GPS. we can find slope also.


4.2 Implementation on Map

Our main target to find out road grade distance (Total up and down) and total distance where ?=0. From Jouny start to destination.


We have to calculate total road grade distance from one intersection point to another intersection point and the distance also for every intersection points. We have to add this information to every road profile. If there is a dangerous slope that will also be recorded. If we store these data in a database using longitude and latitude coordination as a key value. So that we can use google maps for visualization. Then whatever road we follow, we can find the total road grade distance and real distance. Just by adding intersection point to other intersections points all the way. For this calculation, we can develop a web service. For finding longitude and latitude, we can use free web service. But for coordination with google maps, we have to use our own database for every road profile.

5 Conclusions

Road grade calculation will help the driver to select correct road and economic journey. By watching dangerous slope driver can follow the alternative. So, in advance he/she can plan in which way need to drive. Specially for the heavy dudy vehicles. Those people do not like to drive in the hilly area they can easily detect from the road grade distance and bypass the road. The transportation cast will be reduced for goods.




1      Rajesh Rajamani and Chunya , Proceedings of the American Contol Conference San Diego.

2      Yuan Wang Arizona State University Tempe, AZ, USA Ema [email protected] Dasgupta Arizona State University Tempe, AZ, USA Email: [email protected] Proceedings of 2015 IEEE 12th International Conference on Networking, Sensing and Control

3      E. A. R I C K E R M E M B E R  A I E E Automatic Switches to Protect Transformers

4      Seunghwan Baek Heungseob Kim and Kwangsuck Boo

High Safety Vehicle Core Technology Research Center Inje Unversity Gimhae-si, Gyeongsangnam-do, Republic of Korea

5      E-Liang Li, Guangquan Lu, Yunpeng Wang, Daxin Tian 2014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC)

6      Jia He, Hui Rong, Jinfeng Gong, Wei Huang Automotive Engineering Research Institute China Automotive Technology and Research Center Tianjin, China [email protected] 2010 International Conference on Optoelectronics and Image Processing

7      DSangyoung Lee, Ssangyong Motor Company, 150-3, Chilgoi-dong, 459-711, Pyeongtack-si Gyeonggi-do, Korea, [email protected]

8      J. GPS Techno Mr. WaheedMir (NESPAK) Mr. rajhat Masood Leica Geo-Systems, Switzerland.

9      M. Rezaei, M. Sarshar, and M.M. Sanaatiyan, Toward next generation of driver assistance systems: A multimodal sensor-based platform

10   Figure road grade from Wiki

11   Per Sahlholm, Henrik Jansson Scania CV AB, SE-151 87 S¨ odert¨alje, Sweden +46-8-55389129, [email protected] Karl Henrik Johansson

Royal Institute of Technology (KTH), SE-100 44, Stockholmi  


















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