Managing Transition to Autonomous Vehicles Using Bayesian Fuzzy Logic

Author(s):  
Milan Todorovic ◽  
Milan Simic
Author(s):  
Annalisa Milella ◽  
Giulio Reina

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.


2015 ◽  
Vol 35 ◽  
pp. 662-669 ◽  
Author(s):  
Joshué Pérez Rastelli ◽  
Matilde Santos Peñas

2021 ◽  
Vol 11 (24) ◽  
pp. 11940
Author(s):  
Allisa J. Dalpe ◽  
May-Win L. Thein ◽  
Martin Renken

Trust and confidence in autonomous behavior is required to send autonomous vehicles into operational missions. The authors introduce the Performance Evaluation and Review Framework Of Robotic Missions (PERFORM), a framework to enable a rigorous and replicable autonomy test environment, thereby filling the void between that of merely simulating autonomy and that of completing true field missions. A generic architecture for defining the missions under test is proposed and a unique Interval Type-2 Fuzzy Logic approach is used as the foundation for the mathematically rigorous autonomy evaluation framework. The test environment is designed to aid in (1) new technology development (i.e., providing direct comparisons and quantitative evaluations between autonomy algorithms), (2) the validation of the performance of specific autonomous platforms, and (3) the selection of the appropriate robotic platform(s) for a given mission type (e.g., for surveying, surveillance, search and rescue). Three case studies are presented to apply the metric to various test scenarios. Results demonstrate the flexibility of the technique with the ability to tailor tests to the user’s design requirements accounting for different priorities related to acceptable risks and goals of a given mission.


1995 ◽  
Vol 69 (1) ◽  
pp. 15-27 ◽  
Author(s):  
M Poloni ◽  
G Ulivi ◽  
M Vendittelli

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