scholarly journals Testing the Robustness of Commercial Lane Departure Warning Systems

Author(s):  
Fabrizio Re ◽  
Akos Kriston ◽  
Dalia Broggi ◽  
Fabrizio Minarini

Assessment methods are needed to rate the performances of advanced driver assistance systems in a range of real-world conditions, enabling the possibility of mandating minimum performance requirements beyond standardized, regulatory pass-or-fail tests, and ultimately ensuring a real and objectively measurable safety benefit. To bridge the gap between regulatory and real-world performance, this work presents a novel robustness assessment methodology and defines a robustness index determined from regulatory tests to analyze the real-world performance of lane departure warning (LDW) systems. In this context, a robust system means that it is insensitive to changes in driving conditions or environmental conditions. Distance to line (DTL) and time to line crossing (TTLC) were calculated for a light truck and a passenger car, and a black box model of the LDW systems was developed to predict their performance over different lane markings, drifting directions, and vehicle lateral and longitudinal speeds. During the test, neither of the vehicles triggered warning in around 10% of the trials despite the perfect condition of the markings painted on the proving ground. The type of lane marking significantly influenced DTL for both vehicles. For the light truck, the drifting direction, marking type, and their interaction were found to be statistically significant, which resulted in a lower robustness index than that of the passenger car. For both vehicles, TTLC was inversely proportional to the lateral speed, which greatly influences crash avoidance.

2017 ◽  
Vol 18 (2) ◽  
pp. 225-229 ◽  
Author(s):  
Simon Sternlund ◽  
Johan Strandroth ◽  
Matteo Rizzi ◽  
Anders Lie ◽  
Claes Tingvall

2015 ◽  
Vol 764-765 ◽  
pp. 1361-1365
Author(s):  
Cheng Yu Chiu ◽  
Chih Han Chang ◽  
Hsin Jung Lin ◽  
Tsong Liang Huang

This paper addressed a new lane departure warning system (LDWS). We used the side-view cameras to promote Advanced Driver Assistance Systems (ADAS). A left side-view camera detected the right lane next to vehicle, and a right side-view camera detected the right lane. Two cameras processed in their algorithm and gave warning message, independently and separately. Our algorithm combined those warning messages to analyze environment situations. At the end, we used the LUXGEN MPV to test and showed results of verifications and tests.


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