Modeling Lane-Changing Behavior in a Connected Environment: A Game Theory Approach

AbstractVehicle-to-Vehicle communications provide the opportunity to create an internet of cars through the recent advances in communication technologies, processing power, and sensing technologies. Aconnected vehicle receives real-time information from surrounding vehicles; such information can improve drivers’ awareness about their surrounding traffic condition and lead to safer and more efficient driving maneuvers. Lane-changing behavior,as one of the most challenging driving maneuvers to understand and to predict, and a major source of congestion and collisions, can benefit from this additional information.This paper presents a lane-changing model based on a game-theoretical approach that endogenously accounts for the flow of information in a connected vehicular environment.A calibration approach based on the method of simulated moments is presented and a simplified version of the proposed framework is calibrated against NGSIM data. The prediction capability of the simplified model is validated. It is concluded the presented framework is capable of predicting lane-changing behavior with limitations that still need to be addressed.Finally, a simulation framework based on the fictitious play is proposed. The simulation results revealed that the presented lane-changing model provides a greater level of realism than a basic gap-acceptance model

A Time Efficient Approach for Decision-Making Style Recognition in Lane-Change Behavior

Fast recognizing driver's decision-making style of changing lanes plays a pivotal role in safety-oriented and personalized vehicle control system design. This paper presents a time-efficient recognition method by integrating k-means clustering (k-MC) with K-nearest neighbor (KNN), called kMC-KNN. The mathematical morphology is implemented to automatically label the decision-making data into three styles (moderate, vague, and aggressive), while the integration of kMC and KNN helps to improve the recognition speed and accuracy. Our developed mathematical morphology-based clustering algorithm is then validated by comparing to agglomerative hierarchical clustering. Experimental results demonstrate that the developed kMC-KNN method, in comparison to the traditional KNN, can shorten the recognition time by over 72.67% with recognition accuracy of 90%-98%. In addition, our developed kMC-KNN method also outperforms the support vector machine (SVM) in recognition accuracy and stability. The developed time-efficient recognition approach would have great application potential to the in-vehicle embedded solutions with restricted design specifications

Modeling Discretionary Lane Changing Decisions for Connected Vehicles Based on Fuzzy Logic

Lane changing is one of the most complex tasks during driving. Advances in vehicle technology seek to help drivers during the lane change maneuver. Researchers have conducted many attempts to address this issue. However, most of these attempts have not focused on actual driver behavior using advanced vehicle technologies. Among those advances is the vehicle-to-vehicle (V2V) communication which promises safer and more efficient driving operations. This research seeks to fill in this gap by conducting an experiment in a driving simulator environment simulating V2V communication during a lane change maneuver. The experiments allow a better understanding of driver behavior during lane changing maneuvers. First, a literature review was completed to assess studies that focused on understanding and modeling discretionary lane changes. Then a pilot study was conducted with a small sample on a driving simulator to obtain a fuzzy logic membership function. Then a large sample was tested for the study. Adjustments were made to the model and performance measures were analyzed. A t-test was conducted to evaluate any significant differences between the two conditions with and without V2V communication. The results showed that drivers were more willing to accept smaller gaps under connected environment conditions than without V2Vcommunication. Also, the implementation of V2V communication was found to help drivers make the lane changing decision faster. The overall initial speed was reduced under the connected environment

SUMOPy

INSTALL INSTRUCTIONS _______________________ WINDOWS ========= For SUMOPy, download and install: - Anaconda-2.3.0-Windows-x86.exe - wxPython2.9-win32-2.9.5.0-py27.exe - unzip folder "sumopy" and run file "sumopy_gui.py" with python (double click or right-click and select app) To use the Plugin "traces" you need additionally: - download basemap-1.0.8-cp27-none-win32.whl and Shapely-1.5.13-cp27-none-win32.whl - use Anaconda shell to install packages: cd in folder where whl packages are downloaded and execute: pip install basemap-1.0.8-cp27-none-win32.whl pip install Shapely-1.5.13-cp27-none-win32.whl LINUX ===== Open shell, copy and execute this line: sudo apt-get install python-numpy python-wxgtk2.9 python-matplotlib python-mpltoolkits.basemap python-shapel

Lane change decision prediction:an efficient BO-XGB modelling approach with SHAP analysis

The lane-change decision (LCD) is a critical aspect of driving behaviour. This study proposes an LCD model based on a Bayesian optimization (BO) framework and extreme gradient boosting (XGBoost) to predict whether a vehicle should change lanes. First, an LCD point extraction method is proposed to refine the exact LCD points with a highD dataset to increase model learning accuracy. Subsequently, an efficient XGBoost with BO (BO-XGB) was used to learn the LCD principles. The prediction accuracy on the highD dataset was 99.14% with a computation time of 66.837s. The accuracy on the CQSkyEyeX dataset was 99.45%. Model explanation using the shapley additive explanation (SHAP) method was developed to analyse the mechanism of the BO-XGB’s LCD prediction results, including global and sample explanations. The former indicates the particular contribution of each feature to the model prediction throughout the entire dataset. The latter denotes each feature's contribution to a single sample

APPLICATION OF PARAMETER ESTIMATION AND CALIBRATION METHOD FOR CAR-FOLLOWING MODELS

Both safety and the capacity of the roadway system are highly dependent on the car-following characteristics of drivers. Car-following theory describes the driver behavior of vehicles following other vehicles in a traffic stream. In the last few decades, many car-following models have been developed; however, studies are still needed to improve their accuracy and reliability. Car-following models are a vital component of traffic simulation tools that attempt to mimic driver behavior in the real world. Microscopic traffic simulators, particularly car-following models, have been extensively used in current traffic engineering studies and safety research. These models are a vital component of traffic simulation tools that attempt to mimic real-world driver behaviors. The accuracy and reliability of microscopic traffic simulation models are greatly dependent on the calibration of car-following models, which requires a large amount of real world vehicle trajectory data. In this study, the author developed a process to apply a stochastic calibration method with appropriate regularization to estimate the distribution of parameters for car-following models. The calibration method is based on the Markov Chain Monte Carlo (MCMC) simulation using the Bayesian estimation theory that has been recently investigated for use in inverse problems. This dissertation research includes a case study, which is based on the Linear (Helly) model with a different number of vehicle trajectories in a highway network. The stochastic approach facilitated the calibration of car-following models more realistically than the deterministic method, as the deterministic algorithm can easily get stuck at a local minimum. This study also demonstrates that the calibrated model yields smaller errors with large sample sizes. Furthermore, the results from the Linear model validation effort suggest that the performance of the calibration method is dependent upon size of the vehicle trajectory