Evaluation of weather-related freeway car-following behavior using the SHRP2 naturalistic driving study database

Hammit, Britton E.; Ghasemzadeh, Ali; James, Rachel; Ahmed, Mohamed M.; Young, Rhonda

doi:10.1016/j.trf.2018.08.023

Cited by 43 publications

(17 citation statements)

References 24 publications

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“…Car-following behavior is an extensive area of research due to the high frequency of this particular behavior. Previous studies have presented insights into this behavior that were beneficial for various purposes, such as car-following models' development and calibration (Treiber et al 2000;Rakha 2009;Tang et al 2012;Hammit et al 2018), driver assistance system development (Kiefer et al 1999;Bella and Russo 2011;Mohammed et al 2016;Tawfeek and El-Basyouny 2018b), and rear-end near-crash and crash investigation (Carney et al 2016). Moreover, car-following modeling for autonomous vehicles has been discussed in various studies.…”

Section: Related Workmentioning

confidence: 99%

Human-like speed modeling for autonomous vehicles during car-following at intersections

Tawfeek

2022

Can. J. Civ. Eng.

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This study aims at modelling drivers’ speed in car-following during braking situations at intersections to estimate a safe comfortable human-like speed at the minimum car-following distance for Autonomous Vehicles (AV). Several car-following behavioral measures at different times before reaching the minimum following distance and the intersection control type (signalized or unsignalized) were extracted to train the model using three machine learning techniques. The results showed that the XGBoost model is superior when compared to other techniques with R-squared values of 0.99 and 0.97 for training and testing datasets. The results also indicated that the control type impact driver speed at the minimum following distance. The modelled speed will provide more comfortable speed to the AV riders and will not violate the expectations of the surrounding traditional vehicle drivers. Also, the proposed model can be adopted to enhance current car-following models by considering the effect of intersections and its control type.

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Section: Related Workmentioning

confidence: 99%

Human-like speed modeling for autonomous vehicles during car-following at intersections

Tawfeek

2022

Can. J. Civ. Eng.

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“…Additionally, another complementary Roadway Information Database (RID) was utilized that contains dataset from the SHRP2 mobile data collection project and other existing dataset from public and private sources ( 23 ). Note that these unprecedented datasets have been used in many studies to investigate driver behavior in addition to traffic safety and operations ( 24 – 30 ).…”

Section: Data Acquisition and Preparationmentioning

confidence: 99%

Nonparametric Multivariate Adaptive Regression Splines Models for Investigating Lane-Changing Gap Acceptance Behavior Utilizing Strategic Highway Research Program 2 Naturalistic Driving Data

Das

Khan

Ahmed

2020

Transportation Research Record: Journal of the Transportation R

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Gap acceptance is one of the crucial components of lane-changing analysis and an important parameter in microsimulation modeling. Drivers’ poor gap judgment, and failure to accept a necessary safety gap, make it one of the major causes of lane-changing crashes on roadways. Several studies have been conducted to investigate lane-changing gap acceptance behavior; however, very few studies examined the behavior in complex real-world situations, such as in naturalistic settings. This study examined lane-changing gap acceptance behavior from the big Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study (NDS) datasets using a nonparametric multivariate adaptive regression splines (MARS) approach to better understand the complex effects of different factors in gap acceptance behavior. The study developed a unique methodology to identify lane-changing events of the non-NDS-vehicles using the front-mounted radar data from NDS vehicles and extract necessary parameters for analyzing gap acceptance behavior. In addition, surrogate measures of safety, that is, time-to-collision (TTC), was utilized to understand the impact of lane-changing on the NDS following vehicle safety. Moreover, different distributions of gap acceptance were fitted to identify the trend of gap acceptance behavior. The results from the MARS model revealed that different factors including relative speed between lane-changing vehicle (LCV) and lead vehicle (LV)/following vehicle (FV), traffic conditions, acceleration of LCV and FV, and roadway geometric characteristics have significant effects on gap acceptance behavior. The results of this study have significant implications, which could be used in microsimulation model calibration and safety improvements in connected and autonomous vehicles (CAV).

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“…These new datasets would offer new opportunities to understand human driving behavior. Collecting real-time vehicle trajectory data, however, is costly and may infringe privacy, as it involves placing sensors inside individual vehicles (e.g., naturalistic driving devices continuously collecting vehicle movement information in the real traffic environment (Hecker et al, 2018a,b;Hammit et al, 2018;Flores et al, 2018;Zhang et al, 2018a;Zhu et al, 2018a)). Albeit lower cost, laboratory driving simulators (Sadigh et al, 2016b;Abbeel and Ng, 2011;Ziebart et al, 2008) allow only one driver to test at a time, unable to offer realistic experience of interacting with other vehicles on roads.…”

Section: Datasetmentioning

confidence: 99%

A Survey on Autonomous Vehicle Control in the Era of Mixed-Autonomy: From Physics-Based to AI-Guided Driving Policy Learning

Shi

2020

Preprint

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This paper serves as an introduction and overview of the potentially useful models and methodologies from artificial intelligence (AI) into the field of transportation engineering for autonomous vehicle (AV) control in the era of mixed autonomy. It is the first-of-its-kind survey paper to comprehensively review literature in both transportation engineering and AI for mixed traffic modeling. We will discuss state-ofthe-art applications of AI-guided methods, identify opportunities and obstacles, raise open questions, and help suggest the building blocks and areas where AI could play a role in mixed autonomy. We divide the stage of autonomous vehicle (AV) deployment into four phases: the pure HVs, the HV-dominated, the AV-dominated, and the pure AVs. This paper is primarily focused on the latter three phases. Models used for each phase are summarized, encompassing game theory, deep (reinforcement) learning, and imitation learning. While reviewing the methodologies, we primarily focus on the following research questions: (1) What scalable driving policies are to control a large number of AVs in mixed traffic comprised of human drivers and uncontrollable AVs? (2) How do we estimate human driver behaviors? (3) How should the driving behavior of uncontrollable AVs be modeled in the environment? (4) How are the interactions between human drivers and autonomous vehicles characterized? Hopefully this paper will not only inspire our transportation community to rethink the conventional models that are developed in the data-shortage era, but also reach out to other disciplines, in particular robotics and machine learning, to join forces towards creating a safe and efficient mixed traffic ecosystem.

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Evaluation of weather-related freeway car-following behavior using the SHRP2 naturalistic driving study database

Cited by 43 publications

References 24 publications

Human-like speed modeling for autonomous vehicles during car-following at intersections

Human-like speed modeling for autonomous vehicles during car-following at intersections

Nonparametric Multivariate Adaptive Regression Splines Models for Investigating Lane-Changing Gap Acceptance Behavior Utilizing Strategic Highway Research Program 2 Naturalistic Driving Data

A Survey on Autonomous Vehicle Control in the Era of Mixed-Autonomy: From Physics-Based to AI-Guided Driving Policy Learning

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