首页|Findings from McMaster University Update Understanding of Self-Driving Cars (Enh ancing Autonomous Vehicle Hyperawareness In Busy Traffic Environments: a Machine Learning Approach)
Findings from McMaster University Update Understanding of Self-Driving Cars (Enh ancing Autonomous Vehicle Hyperawareness In Busy Traffic Environments: a Machine Learning Approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Fresh data on Transportation - Self-Driving Cars are presented in a new report. According to news reporting out of Hamilton, Cana da, by NewsRx editors, research stated, "As autonomous vehicles (AVs) advance fr om theory into practice, their safety and operational impacts are being more clo sely studied. This study aims to contribute to the ever-evolving algorithms used by AVs during travel in busy urban districts, as well as explore the potential utilization of AV sensor data to identify safety hazards to surrounding road use rs in real time." Our news journalists obtained a quote from the research from McMaster University , "Accordingly, the study incorporates AV data collected from multiple cities in the United States to detect and categorize traffic conflicts that involve the s ource AVs, as well as conflicts that involve other surrounding road users. Then, a machine learning conflict prediction model is trained with Isolation Forest - Convolutional Neural Network - Long Short-Term Memory (IF-CNN-LSTM) layers. The model receives data in real time in the form of road user trajectories and head ings to make an informed prediction of the potential frequency and severity of c onflicts three seconds into the future. In addition, the transferability of the trained model to new data and locations is explored to understand the potential compromise in accuracy compared to the effort and cost of retraining. The result s show that the proposed model is capable of predicting the possibility of confl ict occurrence and conflict severity with high accuracy (sensitivity = 83.5 % and fallout = 11 %). The reported sensitivity of AV conflict predic tion ranged between 89 % and 95 %, depending on confl ict type, which outperforms most of the existing conflict prediction models. The model is also capable of predicting hazardous conflicts of surrounding road use rs in real time, with sensitivity values ranging between 82 % and 87 %, affirming the promising capabilities of onboard vehicle senso rs in undertaking real-time safety applications."
HamiltonCanadaNorth and Central Amer icaCyborgsEmerging TechnologiesMachine LearningSelf-Driving CarsTransp ortationMcMaster University
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