Impact of combined alignments on lane departure: A simulator study for mountainous freeways

Chen, Yixin; Quddus, Mohammed; Wang, Xuesong

doi:10.1016/j.trc.2017.11.010

Cited by 33 publications

(17 citation statements)

References 16 publications

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“…These studies have been based on the analysis of the horizontal and vertical alignment coordination, intersections, interchanges and acceleration and deceleration lanes, overtaking maneuver, operation speed, cross-section, path on horizontal curves, visual demand, road safety countermeasures, signaling and consistency of the geometric design. However, the most recent research linked to driving simulators focuses on aspects closely related to road geometric design and road safety assessment [22][23][24][25].…”

Section: Literature Review On the Application Of Driving Simulators Imentioning

confidence: 99%

Development of Driving Simulation Scenarios Based on Building Information Modeling (BIM) for Road Safety Analysis

et al. 2021

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The analysis of road safety is critical in road design. Complying to guidelines is not enough to ensure the highest safety levels, so many of them encourage designers to virtually recreate and test their roads, benefitting from the evolution of driving simulators in recent years. However, an accurate recreation of the road and its environment represents a real bottleneck in the process. A very important limitation lies in the diversity of input data, from different sources and requiring specific adaptations for every single simulator. This paper aims at showing a framework for recreating faster virtual scenarios by using an Industry Foundation Classes (IFC)-based file. This methodology was compared to two other conventional methods for developing driving scenarios. The main outcome of this study has demonstrated that with a data exchange file in IFC format, virtual scenarios can be faster designed to carry out safety audits with driving simulators. As a result, the editing, programming, and processing times were substantially reduced using the proposed IFC exchange file format through a BIM (Building Information Modeling) model. This methodology facilitates cost-savings, execution, and optimization resources in road safety analysis.

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Section: Literature Review On the Application Of Driving Simulators Imentioning

confidence: 99%

Development of Driving Simulation Scenarios Based on Building Information Modeling (BIM) for Road Safety Analysis

et al. 2021

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“…In the past two decades, a series of ADAS products has been implemented on commercialized vehicles. Currently, most of the successful ADAS products, such as the lane departure warning system (LDW) [7] [8], adaptive cruise control (ACC) [9], and side warning assist system (SWA), [10] are mainly designed to provide additional context information to assist the drivers. Although these products can be viewed as active safety systems, they still interact with the human drivers passively since most of these systems are unable to monitor and understand driver behaviors in real-time.…”

Section: A Motivationmentioning

confidence: 99%

An ensemble deep learning approach for driver lane change intention inference

Xing

Wang

Cao

et al. 2020

Transportation Research Part C: Emerging Technologies

144

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With the rapid development of intelligent vehicles, drivers are increasingly likely to share their control authorities with the intelligent control unit. For building an efficient Advanced Driver Assistance Systems (ADAS) and shared-control systems, the vehicle needs to understand the drivers' intent and their activities to generate assistant and collaborative control strategies. In this study, a driver intention inference system that focuses on the highway lane change maneuvers is proposed. First, a high-level driver intention mechanism and framework are introduced. Then, a vision-based intention inference system is proposed, which captures the multi-modal signals based on multiple low-cost cameras and the VBOX vehicle data acquisition system. A novel ensemble bi-directional recurrent neural network (RNN) model with Long Short-Term Memory (LSTM) units is proposed to deal with the time-series driving sequence and the temporal behavioral patterns. Naturalistic highway driving data that consists of lane-keeping, left and right lane change maneuvers are collected and used for model construction and evaluation. Furthermore, the driver's pre-maneuver activities are statistically analyzed. It is found that for situation-aware, drivers usually check the mirrors for more than six seconds before they initiate the lane change maneuver, and the time interval between steering the handwheel and crossing the lane is about 2 s on average. Finally, hypothesis testing is conducted to show the significant improvement of the proposed algorithm over existing ones. With five-fold cross-validation, the EBiLSTM model achieves an average accuracy of 96.1% for the intention that is inferred 0.5 s before the maneuver starts.

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“…Continuous operating speed profiles are good measures of the general interaction between drivers and the roadway design (8). Lane departure event, identified as extremely large lateral lane departures, is another critical safety surrogate, which has been used as a predictive variable in many studies of single vehicle run-off-road (ROR) crashes (16,42,49). Torbic et al, for instance, found that approximately 76% of curve-related fatal crashes were single-vehicle ROR crashes (50).…”

Section: Indices Adopted In Driving Simulator Application On Roadway ...mentioning

confidence: 99%

Sample Size Study of Driving Simulator Experiment for Freeway Design Safety Evaluations

Wang

Liu

Cai

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Driving Simulator, a powerful simulation tool, has already been used in safety evaluation of roadway geometric design during the pre-construction design stage. Conventional ways of estimating proper sample size include the empirical method, the resource equation, power analysis, and the Bayesian method. However, significant boundaries and prior distributions of operational indices are hard to identify in simulator studies, which makes it difficult to use conventional ways in choosing the acceptable sample size. This study proposes an empirical method to infer proper sample size. The Tongji University eight-degree-of-freedom driving simulator was utilized to collect continuous driving behavior data from a simulated mountainous freeway. Vehicle speed and lane departure events were selected as the indices to measure the influence of geometric design features on operational efficiency and safety. A mixed linear model and a mixed logistic regression model were used to assess the relationships between geometric design features and vehicle speed and lane departure. Random sampling was used to choose 10 samples of 5 to 50 drivers from a total of 55 drivers. Acceptable sample size was determined based on the parameter coefficient convergence elbow points of the mean squared error (MSE) curves of significant variables. The clear elbow points of the MSE curves indicate that 30 is an acceptable sample size.

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Impact of combined alignments on lane departure: A simulator study for mountainous freeways

Cited by 33 publications

References 16 publications

Development of Driving Simulation Scenarios Based on Building Information Modeling (BIM) for Road Safety Analysis

Development of Driving Simulation Scenarios Based on Building Information Modeling (BIM) for Road Safety Analysis

An ensemble deep learning approach for driver lane change intention inference

Sample Size Study of Driving Simulator Experiment for Freeway Design Safety Evaluations

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