Planning and decision-making for connected autonomous vehicles at road intersections: A review
Planning and decision-making technology at intersections is a comprehensive research
problem in intelligent transportation systems due to the uncertainties caused by a variety of …
problem in intelligent transportation systems due to the uncertainties caused by a variety of …
Global lessons learned from naturalistic driving studies to advance traffic safety and operation research: A systematic review
The state of practice of investigating traffic safety and operation is primarily based on
traditional data sources, such as spot sensors, loop detectors, and historical crash data …
traditional data sources, such as spot sensors, loop detectors, and historical crash data …
Bayesian additive regression trees and the General BART model
Bayesian additive regression trees (BART) is a flexible prediction model/machine learning
approach that has gained widespread popularity in recent years. As BART becomes more …
approach that has gained widespread popularity in recent years. As BART becomes more …
Hourly streamflow forecasting using a Bayesian additive regression tree model hybridized with a genetic algorithm
Urban flooding is a global metropolitan problem; therefore, establishing reliable streamflow
forecasting models is critical for flood control and planning in urban areas. Furthermore …
forecasting models is critical for flood control and planning in urban areas. Furthermore …
Adaptive decision-making for automated vehicles under roundabout scenarios using optimization embedded reinforcement learning
The roundabout is a typical changeable, interactive scenario in which automated vehicles
should make adaptive and safe decisions. In this article, an optimization embedded …
should make adaptive and safe decisions. In this article, an optimization embedded …
An inverse reinforcement learning approach for customizing automated lane change systems
Vehicle automation seeks to enhance road safety and improve the driving experience.
However, a standard system does not account for variations in users and driving conditions …
However, a standard system does not account for variations in users and driving conditions …
Big data for finite population inference: Applying quasi-random approaches to naturalistic driving data using Bayesian additive regression trees
A Rafei, CAC Flannagan… - Journal of Survey Statistics …, 2020 - academic.oup.com
Big Data are a “big challenge” for finite population inference. Lack of control over data-
generating processes by researchers in the absence of a known random selection …
generating processes by researchers in the absence of a known random selection …
A hybrid approach for turning intention prediction based on time series forecasting and deep learning
H Zhang, R Fu - Sensors, 2020 - mdpi.com
At an intersection with complex traffic flow, the early detection of the intention of drivers in
surrounding vehicles can enable advanced driver assistance systems (ADAS) to warn the …
surrounding vehicles can enable advanced driver assistance systems (ADAS) to warn the …
Target vehicle lane-change intention detection: An approach based on online transfer learning
H Zhang, R Fu - Computer Communications, 2021 - Elsevier
The lack of visual information from the driver of the target vehicle makes it difficult to detect
the intention of the target vehicle early before the start of the lane-change maneuver …
the intention of the target vehicle early before the start of the lane-change maneuver …
Predicting human-driving behavior to help driverless vehicles drive: random intercept Bayesian additive regression trees
YV Tan, CAC Flannagan, MR Elliott - arXiv preprint arXiv:1609.07464, 2016 - arxiv.org
The development of driverless vehicles has spurred the need to predict human driving
behavior to facilitate interaction between driverless and human-driven vehicles. Predicting …
behavior to facilitate interaction between driverless and human-driven vehicles. Predicting …