Driver Assistance System Research Papers (original) (raw)

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- Cognitive Science, Human Computer Interaction, Computer Vision, Modal Analysis
- by Marinus Van Paassen
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- Algorithms, Expert Systems, Human Factors, Statistical Analysis
- by Mohan Trivedi
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- Augmented Reality, Automotive Engineering, Motion estimation, Graphics
- by J. Armingol and +1
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- Computer Vision, Image Processing, Image Analysis, Genetic Algorithm
- by Ján Turán
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- Feature Extraction, Driver Assistance System, Color Space
- by Mohan Trivedi
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- Sensors, Real Time Systems, Knowledge Based Systems, Bit Error Rate
- by Tamas Daboczi
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- Sensitivity Analysis, Stereo Vision, Driver Assistance System
- by Pedro Gil Jiménez
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- Civil Engineering, Psychology, Computer Vision, Image Processing

This paper describes a novel Driver Assistant System based on real-time video to track lanes and road signs with minimal hardware and software requirements. The proposed system was designed to use low cost cameras and processing power of an on-board commodity laptop. The system model was developed using MATLAB®/Simulink blocksets. This was later converted into an optimized compiled code using the built-in code generation features for the Pentium and AMD processors. The lane tracking algorithm based on Hough Transform was implemented as embedded MATLAB® code. The signboards were detected by Blob Analysis Based Template Matching. Sum of Absolute Differences (SAD) as well as Scale Invariant Feature Transform (SIFT) followed by RANdom SAmple Consensus (RANSAC) based template matching methods. The above were implemented and compared for their performance. The developed system was tested on different drives varying from a high speed drive on a high way to a low speed drive on the city roa...

- by Prof. N D Gangadhar
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- Video Processing, Matlab, Simulink, RANSAC
- by Pedro Gomes
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- Civil Engineering, Augmented Reality, Video Streaming, Human Machine Interface
- by Dario Petri and +1
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- Levels of Abstraction, Data Fusion, Sensor Fusion, Low Power
- by Caio Mendes
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- Embedded Systems, Software Architecture, Embedded Software, Control Systems

Lane detection and tracking is one of the key features of advanced driver assistance system. Lane detection is finding the white markings on a dark road. Lane tracking use the previously detected lane markers and adjusts itself according to the motion model. In this paper, review of lane detection and tracking algorithms developed in the last decade is discussed. Several modalities are considered for lane detection which include vision, LIDAR, vehicle odometry information, information from global positioning system and digital maps. The lane detection and tracking is one of the challenging problems in computer vision. Different vision based lane detection techniques are explained in the paper. The performance of different lane detection and tracking algorithms is also compared and studied.

- by Minija Tamosiunaite
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- Civil Engineering, Human Factors, Real Time Systems, Intelligent

Road traffic is known to have its own complex dynamics. One implication of complexity is that road traffic collisions have become an unwelcome but unavoidable part of human life. One of the major causes of collisions is the human factor. While car manufacturers have been focusing on developing feasible solutions for autonomous and semi-autonomous vehicles to replace or assist human drivers, the proposed solutions have been designed only for individual vehicles. The road traffic, however, is an interaction-oriented system including complex flows. Such a system requires a complex systems approach to solving this problem as it involves considering not only pedestrians, road environment, but also road traffic which can include multiple vehicles. Recent research has demonstrated that large-scale autonomous vehicular traffic can be better modeled using a collective approach as proposed in the form of vehicular cyber-physical systems (VCPS) such as given by Li et al. (IEEE Trans Parallel Distrib Syst 23(9):1775–1789, 2012) or Work et al. (Automotive cyber physical systems in the context of human mobility. In: National workshop on high-confidence automotive cyber-physical systems, Troy, MI, 2008). To the best of our knowledge, there is currently no comprehensive review of collision avoidance in the VCPS. In this paper, we present a comprehensive literature review of VCPS from the collision-avoidance perspective. The review includes a careful selection of articles from highly cited sources presented in the form of taxonomy. We also highlight open research problems in this domain.

- by Ronald Miller
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- Information Systems, Algorithms, Artificial Intelligence, Computer Vision
- by Mark Vollrath
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- Psychology, Causality, Germany, Humans
- by Mojtaba Mirsalim
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- Fuzzy Logic, Controller Design, Fuzzy Control, Anti lock braking system

Traffic sign recognition is one of the main components of a Driver Assistance System (DAS). This paper presents a real-time traffic sign recognition system. It consists of three stages: 1) an image segmentation using red color enhancement to reduce the search space, 2) a HOG-based Support Vector Machine (SVM) detection to extract the traffic signs, and 3) a tree classifier

- by Fatin Zaklouta and +1
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- Image segmentation, Random Forest, Real Time Systems, Support vector machine
- by Christian Kelber
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- Machine Vision, Linear Model, Driver Assistance System, Hough Transform
- by Johann Edelbrunner and +2
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- Engineering, Image Processing, Real Time Computing, Soft Computing
- by Yiannis Papadopoulos
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- Safety, Conceptual Design, Software Implementation, Collision Avoidance

The scene interpretation and the behavior planning of a vehicle in real world traffic is a difficult problem to be solved. If different hierarchies of tasks and purposes are built to structure the behavior of a driver, complex systems can be designed. But finally behavior planning in vehicles can only influence the controlled variables: steering angle and velocity. In this paper a scene interpretation and a behavior planning for a driver assistance system aiming on cruise control is proposed. In this system the controlled variables are determined by an evaluation of the dynamics of a two-dimensional neural field for scene interpretation and two one-dimensional neural fields controlling steering angle and velocity. The stimuli of the fields are determined according to the sensor information.

- by Uwe Handmann
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- Complex System, Intelligent Vehicles, Driver Assistance System
- by Philomina Simon
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- Computer Science, Information Technology, Driver Assistance Systems, Tracking

- by Prof. N D Gangadhar
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- Video Processing, Matlab, Simulink, RANSAC
- by Mahdi Rezaei
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- Earth Sciences, Road safety, Image Analysis, Driver Assistance Systems
- by Marc Ritter
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- Computer Vision, Image Processing, Data Fusion, Sensor Fusion

Co-Authors:
S. Azadi, R. Kazemi, A. Nahvi, and A. Eichberger

- by Sajjad Samiee and +1
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- Eye tracking, Vehicle Dynamics, Fuzzy Control, Fuzzy and Neuro-Fuzzy Systems
- by Francesco Bella
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- Risk Perception, Driver Assistance System
- by Mohan Trivedi
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- Visual attention, Support Vector Machines, Near Infrared, Regression Analysis
- by Uwe Handmann
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- Computer Vision, Image Processing, Image Analysis, Machine Vision

- by JEGAN R MANI
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- Video Processing, Matlab, Simulink, RANSAC
- by Md. Rafiqul Islam
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- Computer Vision, Machine Vision, Mobile Robots, Edge Detection

Driving assistance systems (DAS) offer support in potentially dangerous situations, especially for unexperienced drivers. Co-operative systems improve their performance by sharing information with each other. One key-enabler for describing and exchanging context between intelligent vehicles, which use it for reasoning about their environment, is a common context-model. In this paper, we briefly discuss the influence of the driving context on

- by Kyandoghere Kyamakya
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- Decision Making, OPERATING SYSTEM, Optical Flow, Intelligent Vehicles

Since the potential of soft-computing for driver assistance systems has been recognized, much eort has been spent in the development of appropriate techniques for robust lane detection, object classication, tracking, and representation of task relevant objects. For such systems in order to be able to perform their tasks the environment must be sensed by one or more sensors. Usually a

- by Cecil Hilario
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- Mechanical Engineering, Robotics, Computer Vision, Visual perception
- by Prof. N D Gangadhar
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- Video Processing, Matlab, Simulink, RANSAC

Localization of automobiles in road environments is an important task in the field of developing driver assistance systems, focussing on the improvement of safety and comfort. It is not only essential for navigation systems but offers the possibility to create reliable and precise warning systems and vehicle control functions. Besides the internal odometric sensors, especially the GPS and DGPS are

- by Ullrich Scheunert
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- Kalman Filter, Sensor Data Fusion, Vehicle dynamic control, Urban Environment
- by Fadi Dornaika
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- Stereo Vision, Real Time, Pose Estimation, Driver Assistance System
- by Amit Singh Bhati
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- Computer Science, Computer Vision, Image Processing, Driver Assistance System
- by Mohan Trivedi
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- Distributed Computing, Computer Vision, Pattern Recognition, Vehicle Dynamics
- by Marc Van Hulle and +1
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- Civil Engineering, Human Factors, Real Time Systems, Intelligent
- by Juan Collado
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- Mechanical Engineering, Robotics, Computer Vision, Visual perception

Abstract—To offer increased security and comfort, advanced driver-assistance systems (ADASs) should consider individual driving styles. Here, we present a system that learns a human’s basic driving behavior and demonstrate its use as ADAS by issuing alerts when detecting inconsistent driving behavior. In contrast to much other work in this area, which is based on or obtained from simulation, our system is implemented as a multithreaded parallel central processing unit (CPU)/graphics processing unit (GPU) architecture in a real car and trained with real driving data to generate steering and acceleration control for road following. It also implements a method for detecting independently moving objects (IMOs) for spotting obstacles. Both learning and IMO detection algorithms are data driven and thus improve above the limitations of model-based approaches. The system’s ability to imitate the teacher’s behavior is analyzed on known and unknown

- by Aušra Vidugiriene
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- Engineering, Civil Engineering, Computer Science, Human Factors

Abstract Intelligent vehicles are one of the enlightening ideas that will shape our future by providing enhanced safety and improved mobility. Apparently, among the complex and challenging tasks of future road vehicles is road lane detection or road boundaries detection. However, lane detection is a challenging task because of the varying road conditions that one can encounter while driving. In this paper, a vision-based lane detection approach capable of reaching real time operation with robustness to lighting change and ...

- by Aisha Hassan
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- Computer Science, Computer Vision, Edge Detection, Artificial Intelligent