A Fuzzy Lane Tracking System for Driver Assistance

2006 ◽  
Author(s):  
Annalisa Milella ◽  
Giulio Reina
Keyword(s):  
Fuzzy Logic ◽  
Autonomous Vehicles ◽  
Tracking System ◽  
Critical Role ◽  
Lane Detection ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Inference System ◽  
Driver Support Systems ◽  
Lane Position

In the last few years, driver-assistance systems are increasingly being investigated in automotive field to provide a higher degree of comfort and safety. Lane position determination plays a critical role toward the development of autonomous and computer-aided driving. This paper presents an accurate and robust method for detecting lateral road marking with applications in autonomous vehicles and driver support systems. Much like other lane detection systems, ours is based on computer vision and Hough transform. Our approach, however, is unique in that it combines geometrical and intensity information of the image, based on a fuzzy logic inference system implementing in-depth understanding of different driving and environmental conditions. We call it Fuzzy Logic lane (FLane) tracking system. Details of the main components of the FLane module are presented along with experimental results obtained under varying lighting and road conditions. It is shown that the proposed method is reliable and effective in detecting road border and can be successfully employed for driver assistance.

FLane: An Adaptive Fuzzy Logic Lane Tracking System for Driver Assistance

2011 ◽  
Vol 133 (2) ◽  
Author(s):  
Giulio Reina ◽  
Annalisa Milella
Keyword(s):  
Fuzzy Logic ◽  
Autonomous Vehicles ◽  
Tracking System ◽  
A Priori ◽  
Critical Role ◽  
Lane Detection ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Lane Tracking ◽  
Lane Position

In the last few years, driver assistance systems are increasingly being investigated in the automotive field to provide a higher degree of safety and comfort. Lane position determination plays a critical role toward the development of autonomous and computer-aided driving. This paper presents an accurate and robust method for detecting road markings with applications to autonomous vehicles and driver support. Much like other lane detection systems, ours is based on computer vision and Hough transform. The proposed approach, however, is unique in that it uses fuzzy reasoning to combine adaptively geometrical and intensity information of the scene in order to handle varying driving and environmental conditions. Since our system uses fuzzy logic operations for lane detection and tracking, we call it “FLane.” This paper also presents a method for building the initial lane model in real time, during vehicle motion, and without any a priori information. Details of the main components of the FLane system are presented along with experimental results obtained in the field under different lighting and road conditions.

scholarly journals Hardware Simulation of Rear-End Collision Avoidance System Based on Fuzzy Logic

2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Noor Cholis Basjaruddin ◽  
Didin Saefudin ◽  
Anggun Pancawati
Keyword(s):  
Fuzzy Logic ◽  
Collision Avoidance ◽  
Fuzzy Inference ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Inference System ◽  
Fuzzy Logic Algorithm ◽  
Pile Up ◽  
Assistance Systems ◽  
Collision Avoidance System

Rear-end collisions are the most common type of traffc accident. On the highway, a real-end collision may involve more than two vehicles and cause a pile-up or chain-reaction crash. Referring to data released by the Australian Capital Territory (ACT), rear-end  collisions which occurred throughout 2010 constituted as much as 43.65% of all collisions. In most cases, these rear-end collisions are caused by inattentive drivers, adverse road conditions and poor following distance. The Rear-end Collision Avoidance System (RCAS) is a device to help drivers to avoid rear-end collisions. The RCAS is a subsystem of Advanced Driver Assistance Systems (ADASs) and became an important part of the driverless car. This paper discusses a hardware simulation of a RCAS based on fuzzy logic using a remote control car. The Mamdani method was used as a fuzzy inference system and realized by using the Arduiono Uno microcontroller system. Simulation results showed that the fuzzy logic algorithm of RCAS can work as designed.

scholarly journals Advanced Driver Assistant Systems Focused on Pedestrians’ Safety: A User Experience Approach

2021 ◽  
Vol 13 (8) ◽  
pp. 4264
Author(s):  
Matúš Šucha ◽  
Ralf Risser ◽  
Kristýna Honzíčková
Keyword(s):  
Autonomous Vehicles ◽  
Qualitative Data ◽  
Expert Interviews ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Assistant Systems ◽  
Vehicle Technology ◽  
Assistance Systems ◽  
Technical Solutions ◽  
Near Future

Globally, pedestrians represent 23% of all road deaths. Many solutions to protect pedestrians are proposed; in this paper, we focus on technical solutions of the ADAS–Advanced Driver Assistance Systems–type. Concerning the interaction between drivers and pedestrians, we want to have a closer look at two aspects: how to protect pedestrians with the help of vehicle technology, and how pedestrians–but also car drivers–perceive and accept such technology. The aim of the present study was to analyze and describe the experiences, needs, and preferences of pedestrians–and drivers–in connection with ADAS, or in other words, how ADAS should work in such a way that it would protect pedestrians and make walking more relaxed. Moreover, we interviewed experts in the field in order to check if, in the near future, the needs and preferences of pedestrians and drivers can be met by new generations of ADAS. A combination of different methods, specifically, an original questionnaire, on-the-spot interviewing, and expert interviews, was used to collect data. The qualitative data was analyzed using qualitative text analysis (clustering and categorization). The questionnaire for drivers was answered by a total of 70 respondents, while a total of 60 pedestrians agreed to complete questionnaires concerning pedestrian safety. Expert interviews (five interviews) were conducted by means of personal interviews, approximately one hour in duration. We conclude that systems to protect pedestrians–to avoid collisions of cars with pedestrians–are considered useful by all groups, though with somewhat different implications. With respect to the features of such systems, the considerations are very heterogeneous, and experimentation is needed in order to develop optimal systems, but a decisive argument put forward by some of the experts is that autonomous vehicles will have to be programmed extremely defensively. Given this argument, we conclude that we will need more discussion concerning typical interaction situations in order to find solutions that allow traffic to work both smoothly and safely.

Advanced Driver-Assistance Systems: A Path Toward Autonomous Vehicles

2018 ◽  
Vol 7 (5) ◽  
pp. 18-25 ◽  
Author(s):  
Vipin Kumar Kukkala ◽  
Jordan Tunnell ◽  
Sudeep Pasricha ◽  
Thomas Bradley
Keyword(s):  
Autonomous Vehicles ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Advanced Driver Assistance Systems ◽  
Assistance Systems

scholarly journals Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS)

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 748 ◽  
Author(s):  
John E. Ball ◽  
Bo Tang
Keyword(s):  
Machine Learning ◽  
Autonomous Vehicles ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Embedded Computing ◽  
Advanced Driver Assistance Systems ◽  
Assistance Systems

Advanced driver assistance systems (ADAS) are rapidly being developed for autonomous vehicles [...]

scholarly journals Multilane Detection Entropy-Based Fusion Model by using Iterative Seed and Optimized Deep Convolutional Neural Network

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1967-1974
Keyword(s):  
Neural Network ◽  
Search Algorithm ◽  
Lane Detection ◽  
Learning Approaches ◽  
Driver Assistance ◽  
Fusion Model ◽  
Driver Assistance Systems ◽  
Proposed Model ◽  
Segmentation Approach ◽  
Deep Convolution Neural Network

In today’s world, the conditions of road is drastically improved as compared with past decade. Most of the express highways are made up of cement concrete and equipped with increased lane size. Apparently speed of the vehicle will increase. Therefore there are more chances for accidents. To avoid the accidents in recent days driver assistance systems are designed to detect the various lane. The detected information of lane path is used for controlling the vehicles and giving alerts to drivers. In this paper the entropy based fusion approach is presents for detecting multi-lanes. The Earth Worm- Crow Search Algorithm (EW-CSA) which is based on Deep Convolution Neural Network(DCNN) is utilized for consolidating the outcomes. At first, the deep learning approaches for path location is prepared using an optimization algorithm and EW-CSA, which focus on characterizing every pixel accurately and require post preparing activities to surmise path data. Correspondingly, the region based segmentation approach is utilizing for the multi-lane detection. An entropy based fusion model is used because this method preserved all the information in the image and reduces the noise effects. The performance of proposed model is analyzed in terms of accuracy, sensitivity, and specificity, providing superior results with values 0.991, 0.992, and 0.887, respectively

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