Driver behaviours
Photo by iDLABThis project uses empirical data, either from naturalistic driving or simulator experiments, to understand driver behaviours and apply the obtained knowledge to enhance driving safety, comfort, and intelligence.
It covers the studies that I was involved since the beginning of my master’s study in 2014 - 2016 at the iDLAB, Tsinghua University, as well as the followed efforts in 2016 - 2019 in the free time of my PhD study.
Yuan Liao
Postdoctoral Research Fellow in Mobility
My research interests include mobility data science, urban big data, GIS, sustainable transport.
Publications
Traffic Crash Characteristics in Shenzhen, China from 2014 to 2016
Crash characteristics in Shenzhen, one of the biggest four cities in China, based on the police-reported crashes from 2014 to 2016.
Detection of road traffic participants using cost-effective arrayed ultrasonic sensors in low-speed traffic situations
Detect traffic participants using cost-effective arrayed ultrasonic sensors.
Understanding driver response patterns to mental workload increase in typical driving scenarios
Driver response patterns, assessed by driving performance and eye fixations, to mental workload increases, as measured by the detection response task.
Decision tree-based maneuver prediction for driver rear-end risk-avoidance behaviors in cut-in scenarios
Driver rear-end risk-avoidance behaviors in cut-in scenarios on a straight three-lane highway. Data from 24 participants in 1326 valid trials were collected using a motion-based driving simulator.
Detection of driver cognitive distraction: an SVM based real-time algorithm and its comparison study in typical driving scenarios
A real-time detection algorithm for driver cognitive distraction by using support vector machine (SVM). Data are collected from 26 subjects, driving in typical urban and highway scenarios in a simulator.
Detection of Driver Cognitive Distraction: A Comparison Study of Stop-Controlled Intersection and Speed-Limited Highway
A method for the detection of driver cognitive distraction at stop-controlled intersections and compares its feature subsets and classification accuracy with that on a speed-limited highway.
