Advanced Driver Assistance System

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Advanced Driver Assistance System using Image Processing Techniques

Advanced Driver Assistance System using Image Processing Techniques

Abstract: Driverless car technology is being extensively researched in both academia and industry. But it will take a while until they are sustainable in the present conditions and commercially viable. Until then driving will be done by humans and has room for error like all other humane activities. The driver analysis system tries to gauge the ability of a driver and soundness of their skill. The proposed Advanced Driver Assistance System (ADAS) authenticates a driver by recognizing their face, leaving no room for misuse by unauthorized personnel as well as prevents theft. It keeps track of the driver's activity by continuously monitoring the driver and detects drowsiness of the driver. When the trip ends, the system produces a summary of the trip with the details.
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Advanced Driver Assistance System using Vehicle to Vehicle Communication

Advanced Driver Assistance System using Vehicle to Vehicle Communication

adding to the problems associated with road traffic. Efficient monitoring of vehicles is need of time for smooth traffic flow. Safety system for Vehicle collision avoidance is prime challenge to be met. Many technologies are in action for collision free traffic. Pertaining to this, Intelligent Collision Avoidance (ICWA) system based on V2V (Vehicle to vehicle) Communication is proposed which addresses the issue of collision avoidance. ICWA is one of the leading research feature of advanced driver assistance system (ADAS). In this paper optimized algorithm is developed for collision avoidance, safety zones are created for each vehicle. Overlapping of these safety zones found by this algorithm. It establishes Vehicle to Vehicle communication through wireless protocol Wi-Fi. In this frames Contain vehicular parameters (speed, location of car, turn signal etc.). Each car can transmit and receive the frames from other cars which are within the communication range. This information is takes as input to collision avoidance warning system algorithm. If there is accidental situation, system gives warning to the driver. Apart from this, Android application is developed to make the system more secure and user friendly. The system is implemented on Virtual car environment with street map and gives the warning based on the accidental situation predicted by the algorithm using vehicular information of all cars within the range of communication.
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Effectiveness Estimation Method for Advanced Driver Assistance System and its Application to Collision Mitigation Brake System

Effectiveness Estimation Method for Advanced Driver Assistance System and its Application to Collision Mitigation Brake System

The driving simulator study was performed to come up with a set of reaction times that constitute a representative sample of driver reaction time to the initial CMBS warning. After the screening of the data, 73 test results were acquired. The corresponding response times ranged from 0.32 seconds to 1.64 seconds. The 33rd, 50th, and 67th percentile values were selected from this distribution for purposes of simulation. These three values are 0.52, 0.82, and 1.10 seconds, respectively.

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Advanced Driver Assistance System with Accident Detection

Advanced Driver Assistance System with Accident Detection

Modern vehicles equipped with driver assistance system can “feel” (by sensors), “see” (by cameras) and in future – “speak” (by communication systems). Services subjected to improved road safety by avoiding accidents and reducing injury severity. The efficiency of our project is to support a foresighted driving and enhanced driving comfort. The primary objective of this project is to provide innovative services relating to transport which enables users to be better informed and make safer and ‘smarter’ use of transport system. Road side unit provide information to the vehicle unit which helps the driver to control the vehicle.
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RECOGNITION OF TRAFFIC SIGN FOR DRIVER ASSISTANCE SYSTEMS

RECOGNITION OF TRAFFIC SIGN FOR DRIVER ASSISTANCE SYSTEMS

Traffic sign detection is an consequential step in providing advanced driver assistance system inside the car, mapping data for GIS (geographical information systems) and also developing autonomous (self-driving) cars. At times, the warning signs on the sides of the road become difficult to notice and the driver may sometimes miss important warning notes. These warning notes may be speed breaker ahead or narrow bridge or even accident zone etc. This issue becomes critical at night time. Sometimes because of the traffic or the road condition, the driver may not be able to read the sign and if he tries to read it with a wide eye there may be a chance for the driver to lose concentration on the road.
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Automatic Speed Limit Sign Detection and Recognition for Advanced Driver Assistance Systems

Automatic Speed Limit Sign Detection and Recognition for Advanced Driver Assistance Systems

Abstract -In recent years, traffic accidents have become the major cause to injuries, deaths and property damages. One of the main reasons to such accidents is due to high speed of vehicles. In order to maintain proper speed limit and thus provide significant contribution to improve safety, we propose Speed Limit sign detection and recognition method which is one of the features of Advanced Driver Assistance System (ADAS). In this paper we propose two approaches, i.e., histogram oriented gradient feature with SVM classifier namely HOG-SVM and CNN based approach. In these approaches we first pre-process the image using red color enhancement method and then we detect the Region of Interest using Maximally Stable Extremal Regions (MSER). Later, we classify the image by using different classifiers. In the HOG-SVM method, we are using HOG for feature extraction and Support Vector Machine (SVM) classifier for classification. In the CNN approach, we are using Convolutional Neural Networks (CNN) both for feature extraction and classification. Performance analysis of SVM classifier and CNN classifier are first evaluated on simple German Traffic Sign Recognition Benchmark (GTSRB) dataset using 5 fold classification, we got accuracy 100% for SVM classifier and 98.5% for CNN classifier. Also Further evaluated on German Traffic Sign Detection and Recognition Benchmark datasets and the experimental results show detection accuracy upto 93.6% for SVM classifier and 85.8% for CNN classifier.
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Video-based Pedestrian Intention Recognition and Path Prediction for Advanced Driver Assistance Systems

Video-based Pedestrian Intention Recognition and Path Prediction for Advanced Driver Assistance Systems

In the second major building block of this thesis, a method was presented to estimate the intention of lateral approaching pedestrians in the domain of intelligent vehicles in Chap- ter 5. Multiple features capturing the pedestrian dynamics and the awareness of the nearby traffic situation were used to learn a highly performant Latent-dynamic Conditional Random Field model. The proposed model has the advantage to automatically learn intrinsic structure and feature dependencies as well as temporal dynamics between different intention classes. Evaluation of the trained models showed stable intention estimates for different scenarios compared to other machine learning approaches and state-of-the-art methods. The model provides evidence for potential risky situations and therefore can serve for better pedestrian path prediction or be directly integrated into a system implementing a pedestrian forward collision warning or emergency braking function in order to reduce system false alarms. As the last major contribution, a method for combined intention recognition and path pre- diction of lateral approaching pedestrians was investigated in Chapter 6. The core algorithm consisted of an Interacting Multiple Model filter for pedestrian tracking and path prediction controlled by the intention estimates from a LDCRF model learned for different scenarios. For a prediction horizon of 1 second, evaluation showed a reduction of average path pre- diction error for stopping scenarios (up to 0.17m) compared to a conventional IMM filter and a PHTM-like approach of [Keller and Gavrila, 2014]. In contrast to recent literature, cf. [Keller and Gavrila, 2014, Kooij et al., 2014a], a wider range of scenarios was addressed including pedestrians that initially are walking along the sidewalks but then suddenly bend in towards the road in addition to lateral crossing or stopping pedestrians.
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Real Time Lane Detection for Driver  Assistance System

Real Time Lane Detection for Driver Assistance System

Many of the highway deaths each year were attributed to Lane departure of the vehicle. Many automobile manufacturers are developing advanced driver assistance systems, many of which include subsystems that help prevent unintended Lane departure. Consistent approach among these systems is warning the driver when pre- dicted unintended Lane departure.

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Developing Predictive Models of Driver Behaviour for the Design of Advanced Driving Assistance Systems

Developing Predictive Models of Driver Behaviour for the Design of Advanced Driving Assistance Systems

World-wide injuries in vehicle accidents have been on the rise in recent years, mainly due to driver error. The main objective of this research is to develop a predictive system for driving maneuvers by analyzing the cognitive behavior (cephalo-ocular) and the driving behavior of the driver (how the ve- hicle is being driven). Advanced Driving Assistance Systems (ADAS) include different driving functions, such as vehicle parking, lane departure warning, blind spot detection, and so on. While much research has been performed on developing automated co-driver systems, little attention has been paid to the fact that the driver plays an important role in driving events. Therefore, it is crucial to monitor events and factors that directly concern the driver. As a goal, we perform a quantitative and qualitative analysis of driver behavior to find its relationship with driver intentionality and driving-related actions. We have designed and developed an instrumented vehicle (RoadLAB) that is able to record several synchronized streams of data, including the surrounding environment of the driver, vehicle functions and driver cephalo-ocular behav- ior, such as gaze/head information. We subsequently analyze and study the behavior of several drivers to find out if there is a meaningful relation between driver behavior and the next driving maneuver.
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Traffic Sign Recognition for Advanced Driver Assistance Using Matlab and Embedded Systems
Pola Naveen Kumar & Mr B Nanda Kumar

Traffic Sign Recognition for Advanced Driver Assistance Using Matlab and Embedded Systems Pola Naveen Kumar & Mr B Nanda Kumar

Many a times the warning sign on the road sides be- comes difficult to watch for the drivers and the driver may sometimes miss the warning notes. These warning notes may be speed breaker ahead or narrow bridge or even accident zone etc. This becomes tedious dur- ing many times and at nights. Sometimes because of the traffic or the road condition driver may not read anything and even if he tries to read it with a wide eye there is a chance for the drive to lose concentration on the road.This project aims at developing a solution for this problem using image processing technique. By placing a camera in front of the vehicle, it can pick road signs and give it to a system that processes the image. The image is de-noised and edge detection and shape parameters are used to identify the nature of the signs displayed. The MATLAB program identifies the signs and informs about the signs to the hardware below. Microcontroller based hardware is placed inside the vehicle. The microcontroller at all times receives the information and displays the information using the dedicated LCD display. Further the same is used to an- nounce to the driver about the hurdles such as speed breakers. This voice alerting system helps the drivers to concentrate on the road without even worrying about the sign boards near the road. The sign recognition is done using image processing tools on a MATLB. The re- sult of the recognition can be used for the application. Thus the project can be highly helpful to drivers and the voice announcement can be in any language includ- ing Tamil, English or Hindi. As this project uses image processing no additional components are necessary to be placed on the sign boards and the existing sign boards can be kept as it is. Only the vehicle need to be fitted with this system but this can be left to the vehicle manufacturers and owners and they can use it as an extra feature for safety and to prevent accidents.
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Traffic Sign Recognition for Advanced Driver Assistance Using MatlabAnd Embedded Systems
Ch Pooja & K Niranjan

Traffic Sign Recognition for Advanced Driver Assistance Using MatlabAnd Embedded Systems Ch Pooja & K Niranjan

At the present time, many studies are being conducted working toward the implementation of an Intelligent Traf- fic System (ITS). One field of this research is driving sup- port systems, and many studies are being conducted to develop systems which identify and recognize road signs in front of the vehicle, and then use this information to notify the driver or to control the vehicle [1-9]. Develop- ment of a system which can provide road information to the driver at any time is already underway. This system uses wireless communication with special narrowband signal transmitters installed on the roadside, a technol- ogy which has already been commercialized with ETC. With the construction of this type of infrastructure, it is believed that there will be a change in the method of pro- viding road sign information from the current method of providing visual information. However, much time will be required before this infrastructure covers all roads in local areas, and it is likely that as long as vehicles are driven by human drivers, road signs will never disappear as a means of providing traffic information.
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Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey

Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey

Larger sets of variables generate rules unnecessarily complex that can be replaced by fuzzy logic (FL) maps. These are also based on prefixed thresholds but are able to include more parameters whilst keeping its simplicity, robustness, easy understanding and low computational order. Dörr et al. developed a real-time algorithm that also considered other variables such as road type and gap between vehicles by implementing FL. This system was verified in simulation environment in urban and rural road without traffic disturbances achieving 68% of correct classification rate and 2% of incorrect classification rate [38]. Gilman et al. also employed a RB and FL based on a total of 17 factors evaluated through a performance indicator [39]. Syed et al. proposed a FL algorithm to evaluate optimal pedals operation in a HEV. This algorithm monitored both throttle and braking pedals operation and was able to calculate an appropriate correction and generate haptic feedback to the driver. The authors claimed a minimum of 3.5% improvement in fuel consumption for mildest driving in simulation environment, without compromising the vehicle performance [47]. Won also used FL to improve HEV fuel consumption using road type and events detection: start-up, acceleration, cruise, deceleration and stationary [52]. A similar approach was taken by Kim et al., who used a FL control with driving mode, driving style and driving conditions recognition capability. This was utilized to adapt the control of a HEV and BEV given pedals operation and external temperature and tune the state of charge window, battery recharge/depletion and engine on/off state thresholds. The state of charge membership functions were hereby adaptive to the driving conditions [53].
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Design and Implementation of Driver Assistance System (Das) using Raspberry Pi to Enhance Driver Safety

Design and Implementation of Driver Assistance System (Das) using Raspberry Pi to Enhance Driver Safety

In this study, finally it conclude that it is very difficult to avoid accidents but some how that accident ratio or percentage can be reduced to some less number by using these type of new technologies, by using this method accurate test results will get and driver get some alert while driving regarding rear end collision as well as drowsiness. The goal at the end of this project is the practical demonstration of an alcohol detection system which is suitable for following installation in a vehicle. The adoption of non-regulatory, voluntary approaches to the implementation of advanced vehicle technology makes it critical that policy and public acceptance issues be addressed concurrent with the technology development. This is particularly important when it comes to the implementation of technologies to prevent alcohol-impair drivers from getting behind the wheel. The universal public fully understand the dangers of drinking and driving, having lack of sleep.
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Traffic Sign Recognition for Advanced Driver Assistance using Digital Image Processing
A Shreya & T Sreedevi

Traffic Sign Recognition for Advanced Driver Assistance using Digital Image Processing A Shreya & T Sreedevi

The result of the recognition can be used for the appli- cation. Thus the project can be highly helpful to drivers and the voice announcement can be in any language including Tamil, English or Hindi. As this project uses image processing no additional components are nec- essary to be placed on the sign boards and the existing sign boards can be kept as it is. Only the vehicle need to be fitted with this system but this can be left to the ve- hicle manufacturers and owners and they can use it as an extra feature for safety and to prevent accidents.

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DRIVERS’ ACCEPTANCE OF ADVANCED DRIVER ASSISTANCE SYSTEMS – WHAT TO CONSIDER?

DRIVERS’ ACCEPTANCE OF ADVANCED DRIVER ASSISTANCE SYSTEMS – WHAT TO CONSIDER?

or not to use an assistance system, the results show it may nevertheless play an important role in the deployment process of ADAS in the car pool. Depending on the specific system, 29–60 % of the car drivers’ are not willing to pay any extra money for such equipment. These proportions are higher than those found by (Kyriakidis et al., (2014) in respect to willingness to pay for partially, highly and fully automated vehicles. Again, the perceived safety benefit might convince the drivers to invest in the systems, but the expectancy of increased comfort (or, maybe, usefulness) may be an argument for paying a higher price as well, as suggested by the facts that a slightly higher proportion of drivers is willing to pay more for ACC (which is purchased rather for comfort) than for Blind Spot Monitoring. This would be in accord with the findings of previous studies (Choi et al., 2016; European Commission – Eurobarometer, 2006; Ghazizadeh & Lee, 2014; Kyriakidis et al., 2014; Trübswetter & Bengler, 2013). Czech drivers, too, stress the importance of trust in the system, ease of use, perceived usefulness and positive references/previous experience with the system, and they express some concerns regarding the amount of control they could keep over the vehicle and driving.
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Wearable Wireless Headset based Driver Assistance System to Ensure Vehicle Safety

Wearable Wireless Headset based Driver Assistance System to Ensure Vehicle Safety

Physical disorder due to consumption of alcohol or drowsiness has become one of the major reasons of traffic accident fatalities in recent years. Alcohol causes temporary impairment of vision and hearing that results in the inability of sensing balance and coordination. As a consequence, the driver feels drowsy and loses control over the vehicle that leads to the occurrence of an accident. It has been found that alcohol consumption exponentially increases the risks of accidents [1]. Driving in drunken condition is considered to be responsible for the death of around ten thousand individuals per year in Europe [2]. On the other hand, multi- tasking such as using cell-phones while driving is another major reason for the driver’s being distracted and causing deadly incidents. It is estimated that, the usage of mobile phones during driving is responsible for 25% of all road-crashes in the USA [3]. But the occurrence of this phenomenon has increased by 1 to 11% in many countries in the past 5-10 years [4]. Therefore, implementation of advanced technologies has become necessary for the prevention of driving with physical disorder and safely using communication devices in vehicles to reduce traffic fatalities.
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Safety Assessment of Driver Assistance Systems

Safety Assessment of Driver Assistance Systems

The term Advanced Driver Assistance Systems (ADAS) can cover a full range of systems varying from systems providing information, advice and warnings, through systems that assist an/or intervene in vehicle control and manoeuvring tasks, all the way to systems that support fully automatic driving (Zwaneveld et al., 1999; Becker et al., 2000). Four broad types of ADAS may be distinguished. First of all there are systems intended to support various aspects of the driving task by providing information, commonly termed In-Vehicle Information Systems (IVIS). Typical examples are navigation systems and systems providing information on traffic and road conditions, such as TrafficMaster and RDS-TMC receivers. Secondly there are systems providing warnings or feedback, usually with the intention of reducing driver errors or violations. The informative (advisory) version of Intelligent Speed Adaptation, longitudinal collision warning systems, lane departure warning systems and lane-change assistant systems are examples of this category. The warnings may be auditory, visual or haptic (by force feedback or vibration). Thirdly, there are systems that intervene in vehicle control but without completely supplanting the driver, and in some cases permitting the driver to overrule system actions. Adaptive Cruise Control, Stop and Go and the various intervening forms of Intelligent Speed Adaptation fall into this category. Finally, there is automated driving, sometimes termed “autonomous driving”, in which the driver is completely out of the loop and cannot overrule system actions. Installing these various systems in vehicles changes the driver’s task, modifying certain components while others are added or removed. With the more intervening systems, the role of the human driver will to a greater or lesser extent be transformed from manual to supervisory control.
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Automatic Driver Assistance System - Glare Free High Beam Control

Automatic Driver Assistance System - Glare Free High Beam Control

Glare Free High Beam is one of the features of the ADAS. Advanced driver assistance systems (ADAS) are one of the fastest-growing application areas in present day vehicle sector. The multiple features of ADAS can warn the drivers by allowing better visibility into what is going on outside the car [1]. The ADAS functions and autonomous driving does require the operation of multiple systems at a time.

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Driver Safety Awareness & Assistance System for Vehicle Control

Driver Safety Awareness & Assistance System for Vehicle Control

1.INTRODUCTION In India automotive population is increases rapidly. Due to increase in traffic number of road accidents is increasing. In our country road accidents are menace. By the research of world health organization (WHO) it is identified the major cause of road accidents are due to the driver error and carelessness also the key players in the accident scenario are driver sleepiness, alcoholism and carelessness. Driver monitoring system can increase the safety of vehicle for passengers and also road user. Drowsiness, drunken behavior of the driver is the major driver errors. Sensors such as alcohol sensor, temperature sensor and heart beat sensor are used to detect the physical condition of the driver. Camera module used for the detection of drowsiness of driver. Regarding to the design of advanced safety system in automobiles this behavior of driver is serious issue.by implementing this kind of project on vehicle we can reduces such kind of road accidents and also improves driver’s safety.
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Vision Based Lane Detection and Departure Warning System for Advanced Driver Assistance

Vision Based Lane Detection and Departure Warning System for Advanced Driver Assistance

Fig 3(a). Shows that application of HT on considered road image gives lines corresponding to lane boundaries as well as some lines which do not correspond to lane boundaries resulting in false detection. Different modules are being applied to minimize such false detection and to increase accuracy of lane detection. Fig. 3(b) detection results after applying HT and angle constraint on detected Hough lines. Lines corresponding to lane boundaries lie in certain range of θ. Hence lines which do not fall in that range are eliminated to minimize false detection. This condition makes system robust against shadowing and road irregularities and results in increasing the accuracy of detection. Even after applying angle condition detection results show multiple lines belonging to lane markings causing ambiguity regarding considering exact line for departure measurement which is subsequent step of lane detection. To solve this problem only the innermost line from left and right sub regions is used for drawing detected boundary line and for departure measurement as shown in fig 3(c).
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