A new psychological methodology for modeling real-time car following maneuvers

Naseri, H.; Nahvi, Ali; Karan, Farshad Salimi Naneh

doi:10.1016/j.tbs.2014.12.002

Cited by 15 publications

(3 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In most CF models, velocity, distance headway and relative velocity are the primary variables for explaining the following vehicle's acceleration. Time-to-collision has also been incorporated in some CF models [19,20]. However, few studies have investigated these variables' significance to drivers' decision making, especially in a specific time window.…”

Section: Extracting Features Using Autoencodermentioning

confidence: 99%

Car-Following Modeling Incorporating Driving Memory Based on Autoencoder and Long Short-Term Memory Neural Networks

et al. 2019

View full text Add to dashboard Cite

Although a lot of work has been conducted on car-following modeling, model calibration and validation are still a great challenge, especially in the era of autonomous driving. Most challengingly, besides the immediate benefit incurred with a car-following action, a smart vehicle needs to learn to evaluate the long-term benefits and become foresighted in conducting car-following behaviors. Driving memory, which plays a significant role in car-following, has seldom been considered in current models. This paper focuses on the impact of driving memory on car-following behavior, particularly, historical driving memory represents certain types of driving regimes and drivers’ maneuver in coordination with the variety of driving regimes. An autoencoder was used to extract the main features underlying the time-series data in historical driving memory. Long short-term memory (LSTM) neural network model has been employed to investigate the relationship between driving memory and car-following behavior. The results show that velocity, relative velocity, instant perception time (IPT), and time gap are the most relevant parameters, while distance gap is insignificant. Furthermore, we compared the accuracy and robustness of three patterns including various driving memory information and span levels. This study contributes to bridging the gap between historical driving memory and car-following behavior modeling. The developed LSTM methodology has the potential to provide personalized warnings of dangerous car-following distance over the next second.

show abstract

Section: Extracting Features Using Autoencodermentioning

confidence: 99%

Car-Following Modeling Incorporating Driving Memory Based on Autoencoder and Long Short-Term Memory Neural Networks

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Connected and automated vehicle solutions can potentially help in addressing transportation challenges by primarily targeting the human factor involved in surface transportation. In special relevance to transportation safety solutions, several studies have focused on monitoring driving behavior to develop cooperative collision warning systems (Sengupta et al, 2007, Yang et al, 2000, Chrysler et al, 2015, Goodall et al, 2016, Osman et al, 2015, Doecke et al, 2015, Lee et al, 2002, Lee et al, 2004, Abe and Richardson, 2006, Naseri et al, 2015. By carefully characterizing driving behavior, the afore-mentioned studies contributed by developing effective collision warning systems and documented the potential of connected vehicle technologies in addressing major transportation safety challenges (Chrysler et al, 2015, Goodall et al, 2016, Osman et al, 2015, Doecke et al, 2015.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles

Khattak

Wali

2017

Transportation Research Part C: Emerging Technologies

View full text Add to dashboard Cite

This is the pre-print version of the accepted article. For citation, please use:Khattak, Asad, and Behram Wali. "Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles.Abstract -Driving volatility captures the extent of speed variations when a vehicle is being driven. Extreme longitudinal variations signify hard acceleration or braking.Warnings and alerts given to drivers can reduce such volatility potentially improving safety, energy use, and emissions. This study develops a fundamental understanding of instantaneous driving decisions, needed for hazard anticipation and notification systems, and distinguishes normal from anomalous driving. In this study, driving task is divided into distinct yet unobserved regimes. The research issue is to characterize and quantify these regimes in typical driving cycles and the associated volatility of each regime, explore when the regimes change and the key correlates associated with each regime. Using Basic Safety Message (BSM) data from the Safety Pilot Model Deployment in Ann Arbor, Michigan, two-and three-regime Dynamic Markov switching models are estimated for several trips undertaken on various roadway types. While thousands of instrumented vehicles with vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication systems are being tested, nearly 1.4 million records of BSMs, from 184 trips undertaken by 71 instrumented vehicles are analyzed in this study. Then even more detailed analysis of 43 randomly chosen trips (N = 714,340 BSM records) that were undertaken on various roadway types is conducted. The results indicate that acceleration and deceleration are two distinct regimes, and as compared to acceleration, drivers decelerate at higher rates, and braking is significantly more volatile than acceleration. Different correlations of the two regimes with instantaneous driving contexts are explored. With a more generic threeregime model specification, the results reveal high-rate acceleration, high-rate deceleration, and cruise/constant as the three distinct regimes that characterize a typical driving cycle. Moreover, given in a high-rate regime, drivers' on-average tend to decelerate Keywords: connected vehicle, basic safety messages, instantaneous driving decisions, driving regimes, Markov-switching dynamic regressions.As a part of U.S. Department of Transportation's (USDOT) Real-Time Data Capture and Management Program, Safety Pilot Model Deployment (SPMD) in Ann Arbor, Michigan features real-world demonstration of connected vehicle safety applications, technologies, and systems by hosting approximately 3,000 vehicles instrumented with V2V and V2I communication systems (Henclewood, 2014). Altogether, 75 miles of roadway in Ann Arbor, Michigan are instrumented with roadside equipment (RSE) that are capable of communicating with appropriately instrumented vehicles, and devices via advanced communication and sensor technologies such as dedicated short-range communications (DSRC) (Henclewood, 2014...

show abstract

“…It is worth reminding that statistical physics considers each particle of the matter, and uses simple conservation laws [2] for them (by some simple assumptions). Next, the scientist tries to understand and formulate the behavior [3], [4] of the whole complex based on these assumptions and formulations. This point of view with which the properties of the whole structure or complex are obtained by considering particles (as individual) is the most powerful aspect of statistical physics which is used in chemistry (crystallization behavior of some materials [5]), material sciences (time dependent growth of lattices [6]), and even in social sciences (social dynamics).…”

Section: Introductionmentioning

confidence: 99%

Controllability of a Nonlinear Voter Model

Shiran¹

2018

MJMIE

View full text Add to dashboard Cite

Converging and emerging behavior of different phenomena call for a strong tool to describe the behavior of mutli-agent systems. Economics and quantitative sociology are two of the most important areas where these behavioral patterns are observed. In this paper, a brief introduction to statistical physics is provided as a tool to understand and investigate the upper-mentioned phenomena. Some of the available interaction models used in quantitative sociology have been presented to get an idea of how they work. Using one of these models, a discrete nonlinear dynamic system is presented. Main focus of this paper is providing necessary tools and solutions for discrete nonlinear dynamical systems.

show abstract

A new psychological methodology for modeling real-time car following maneuvers

Cited by 15 publications

References 44 publications

Car-Following Modeling Incorporating Driving Memory Based on Autoencoder and Long Short-Term Memory Neural Networks

Car-Following Modeling Incorporating Driving Memory Based on Autoencoder and Long Short-Term Memory Neural Networks

Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles

Controllability of a Nonlinear Voter Model

Contact Info

Product

Resources

About