首页|University of Texas Tyler Researchers Publish Findings in Machine Learning (Appl ication of machine learning models to predict driver left turn destination lane choice behavior at urban intersections)
University of Texas Tyler Researchers Publish Findings in Machine Learning (Appl ication of machine learning models to predict driver left turn destination lane choice behavior at urban intersections)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on artificial intelligence is now available. According to news reporting from Tyler, Texas, by NewsRx jour nalists, research stated, "When there are multiple lanes to choose from downstre am of a turning movement, drivers should choose the innermost lane so that drive rs at other approaches of the intersection may make concurrent turning movements in the outermost lane(s)." Our news editors obtained a quote from the research from University of Texas Tyl er: "However, human drivers do not always choose the innermost lane, which could lead to crashes with other vehicles. Therefore, predicting human driver behavio rs is vital in reducing crashes, as the need to share the roadways with automate d vehicles (AVs) continues to grow. In this research, various machine learning m odels have been used to predict the left turn destination lane choice of human-d riven vehicles (HDVs) at urban intersections based on several quantifiable param eters. A total of 174 subject vehicles were extracted and analyzed in Los Angele s, California, and Atlanta, Georgia, using HDV trajectory data from the Next Gen eration SIMulation (NGSIM) database. Five machine learning techniques, namely bi nary logistic regression, k nearest neighbors, support vector machines, random f orest, and adaptive neuro-fuzzy inference system, were applied to the extracted data to predict the lane choice behavior of drivers. The k nearest neighbors mod el showed the most promising results for the evaluated data with a correct decis ion score of over 93 % for the unseen test data."
University of Texas TylerTylerTexasUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesK-nea rest NeighborMachine Learning
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