Journal ArticleDOI
A model for the structure of lane-changing decisions
TLDR
A structure is proposed to connect the decisions which a driver has to make before changing lanes to ensure that the vehicles in traffic simulations behave logically when confronted with situations commonly encountered in real traffic.Abstract:
A structure is proposed to connect the decisions which a driver has to make before changing lanes. The model is intended to cover the urban driving situation, where traffic signals, obstructions and heavy vehicles all exert an influence. The structure is designed to ensure that the vehicles in traffic simulations behave logically when confronted with situations commonly encountered in real traffic. The specific mathematical expression of the questions embedded in the decision process and employed in the present implementation of the model are not critical and can be replaced by alternatives, but the heirarchy of the decisions is crucial. On the basis of experience to date, the lane changing model produces a realistic simulation of driver behaviour and has proved very robust under a wide range of conditions.read more
Citations
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Journal ArticleDOI
Learning From Naturalistic Driving Data for Human-Like Autonomous Highway Driving
TL;DR: In this study, a method of learning cost parameters of a motion planner from naturalistic driving data is proposed, and the learning is achieved by encouraging the selected trajectory to approximate the human driving trajectory under the same traffic situation.
Journal ArticleDOI
Discretionary lane-changing behavior: empirical validation for one realistic rule-based model
TL;DR: It is found that the classical lane-changing rules of rule-based model cannot explain many cases in the empirical dataset, so one new decision rule is proposed, comparing the position after a time horizon of several seconds without a lane-change.
Proceedings ArticleDOI
Modeling tactical lane-change behavior for automated vehicles: A supervised machine learning approach
TL;DR: This paper compares the results of different supervised machine learning classifiers to better understand the lane change decision of drivers using the NGSIM database and indicates that extra trees classifier, decision trees and random forest had the best performance in predicting the lane changing decisions of human drivers.
Journal ArticleDOI
Socio-demographic impacts on lane-changing response time and distance in work zone with Drivers' Smart Advisory System
Qing Li,Fengxiang Qiao,Lei Yu +2 more
TL;DR: In this article, fuzzy logic-based lane-changing models that consider drivers' socio-demographics were developed to improve the realism of lane changing maneuvers in work zones, where drivers' Smart Advisory System (DSAS) messages were provided as one of the scenarios.
Dissertation
Modeling gap acceptance at freeway merges
TL;DR: Ben-Akiva et al. as mentioned in this paper developed a merging model that captures the gap acceptance behavior of drivers that merge from a ramp into a congested freeway, which can be classified into three types: normal, forced and cooperative lane changing.
References
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Journal ArticleDOI
A behavioural car-following model for computer simulation
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Journal ArticleDOI
Nonlinear Follow-the-Leader Models of Traffic Flow
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Journal ArticleDOI
Nonlinear Effects in the Dynamics of Car Following
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Journal ArticleDOI
A Generalization of Linear Car-Following Theory
TL;DR: The linear theory of single-lane traffic flow is generalized by using an integral transform technique well-known in other branches of applied science that introduces the idea of a memory function that describes the way in which a driver processes the information he receives from a lead vehicle.
Journal ArticleDOI
Fuel saving and other benefits of dynamic advisory speeds on a multi-lane arterial road
TL;DR: In this paper, a discrete vehicle simulation using program MULTSIM has been run on a 6 km idealized road and also on 2.3 km of the multi-lane arterial, Military Road, Sydney.
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