Driver Behavior Models

2015 ◽  
pp. 1-12 ◽  
Author(s):  
Edmund Donges
Keyword(s):  
Driver Behavior ◽  
Behavior Models

Evaluation of driver-behavior models in real-world car-following task

2009 ◽  
Author(s):  
Pongtep Angkititrakul ◽  
Terashima Ryuta ◽  
Toshihiro Wakita ◽  
Kazuya Takeda ◽  
Chiyomi Miyajima ◽  
...  
Keyword(s):  
Real World ◽  
Driver Behavior ◽  
Car Following ◽  
Behavior Models

scholarly journals Driver Behavior Modeling: Developments and Future Directions

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Najah AbuAli ◽  
Hatem Abou-zeid
Keyword(s):  
Fog Computing ◽  
Driver Behavior ◽  
Behavior Modeling ◽  
Mobile Cloud ◽  
Context Aware ◽  
Design Development ◽  
Future Directions ◽  
Research Challenges ◽  
Recent Developments ◽  
Behavior Models

The advances in wireless communication schemes, mobile cloud and fog computing, and context-aware services boost a growing interest in the design, development, and deployment of driver behavior models for emerging applications. Despite the progressive advancements in various aspects of driver behavior modeling (DBM), only limited work can be found that reviews the growing body of literature, which only targets a subset of DBM processes. Thus a more general review of the diverse aspects of DBM, with an emphasis on the most recent developments, is needed. In this paper, we provide an overview of advances of in-vehicle and smartphone sensing capabilities and communication and recent applications and services of DBM and emphasize research challenges and key future directions.

scholarly journals A Review of Near-Collision Driver Behavior Models

2012 ◽  
Vol 54 (6) ◽  
pp. 1117-1143 ◽  
Author(s):  
Gustav Markkula ◽  
Ola Benderius ◽  
Krister Wolff ◽  
Mattias Wahde
Keyword(s):  
Computer Simulation ◽  
Traffic Safety ◽  
Systematic Approach ◽  
Driver Behavior ◽  
Research Field ◽  
Main Emphasis ◽  
Simulation Based ◽  
Model Equations ◽  
Behavior Models ◽  
Real Traffic

Objective: This article provides a review of recent models of driver behavior in on-road collision situations. Background: In efforts to improve traffic safety, computer simulation of accident situations holds promise as a valuable tool, for both academia and industry. However, to ensure the validity of simulations, models are needed that accurately capture near-crash driver behavior, as observed in real traffic or driving experiments. Method: Scientific articles were identified by a systematic approach, including extensive database searches. Criteria for inclusion were defined and applied, including the requirement that models should have been previously applied to simulate on-road collision avoidance behavior. Several selected models were implemented and tested in selected scenarios. Results: The reviewed articles were grouped according to a rough taxonomy based on main emphasis, namely avoidance by braking, avoidance by steering, avoidance by a combination of braking and steering, effects of driver states and characteristics on avoidance, and simulation platforms. Conclusion: A large number of near-collision driver behavior models have been proposed. Validation using human driving data has often been limited, but exceptions exist. The research field appears fragmented, but simulation-based comparison indicates that there may be more similarity between models than what is apparent from the model equations. Further comparison of models is recommended. Application: This review provides traffic safety researchers with an overview of the field of driver models for collision situations. Specifically, researchers aiming to develop simulations of on-road collision accident situations can use this review to find suitable starting points for their work.

Learning Driver Behavior Models from Traffic Observations for Decision Making and Planning

2015 ◽  
Vol 7 (1) ◽  
pp. 69-79 ◽  
Author(s):  
Tobias Gindele ◽  
Sebastian Brechtel ◽  
Rudiger Dillmann
Keyword(s):  
Decision Making ◽  
Driver Behavior ◽  
Behavior Models

scholarly journals Driver Behavior Models for Heavy Vehicles and Passenger Cars at a Work Zone

2019 ◽  
Vol 11 (21) ◽  
pp. 6007
Author(s):  
Bawan Mahmood ◽  
Jalil Kianfar
Keyword(s):  
Traffic Simulation ◽  
Driver Behavior ◽  
Simulation Models ◽  
Work Zone ◽  
Validation Dataset ◽  
Heavy Vehicles ◽  
Passenger Cars ◽  
Planning And Scheduling ◽  
Behavior Models ◽  
Transportation Agencies

Traffic impact assessment is a key step in the process of work zone planning and scheduling for transportation agencies. Microscopic traffic simulation models enable transportation agencies to conduct detailed analyses of work zone mobility performance measures during the planning and scheduling process. However, traffic simulation results are valid only when the simulation model is calibrated to replicate driver behavior that is observed in the field. Few studies have provided guidance on the calibration of traffic simulation models at work zones and have offered driver behavior parameters that reproduce capacity values that are observed in the field. This paper contributes to existing knowledge of work zone simulation by providing separate driver behavior model parameters for heavy vehicles and passenger vehicles. The driver behavior parameters replicate the flow and speed at the work zone taper and at roadway segments upstream of the work zone. A particle swarm optimization framework is proposed to improve the efficiency of the calibration process. The desired time headway was found to be 2.31 seconds for heavy vehicles and 1.53 seconds for passenger cars. The longitudinal following threshold was found to be 17.64 meters for heavy vehicles and 11.70 meters for passenger cars. The proposed parameters were tested against field data that had not previously been used in the calibration of driver behavior models. The average absolute relative error for flow rate at the taper was 10% and the mean absolute error was 54 veh/h/ln. The GEH statistic for the validation dataset was 1.48.

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