Microscopic modeling of driving behavior is the basis for traffic design and traffic simulation studies and can be applied to automated driving systems to provide human-like decision making. Previous modeling methods can be mainly divided into scenario-based modeling methods and field theory-based modeling methods. Scenario-based models are based on behavior theories that can explain behavioral mechanisms and field theory-based models are convenient for application to different scenarios. Combining two behavior theories and field theory, this paper aims to present a novel method to uniformly model the driving behavior in different scenarios. Risk homeostasis theory and preview-follower theory are used as the theoretical foundation, and field theory is utilized to connect the two behavior theories. A new risk field model is constructed for better coupling these behavior theories. Integrating these theories, this study then develops a subjectively perceived risk quantification method and a trajectory and motion planning model, which are validated using naturalistic data in car-following scenarios. Results show the effectiveness of this method and this model with reference to an effective risk quantification index (safety margin) and in comparison with the classical models (desired safety margin model and intelligent driver model) using naturalistic data in car-following scenarios.

The modern world is built on the robustness of interdependent infrastructures, which can be characterized as complex networks. Recently, a framework for the analysis of interdependent networks has been developed to explain the mechanism of robustness in interdependent networks. Here, we extend this interdependent network model by considering flows in the networks, and we study the system's robustness under different attack strategies. In our model, nodes may fail because of either overload or loss of interdependency. Considering the interaction between these two failure mechanisms, it is shown that interdependent scale-free networks show extreme vulnerability. The robustness of interdependent scale-free networks is found in our simulations to be much smaller than that of the single scale-free networks or the interdependent scale-free networks without flows.

In the present studies of autonomous driving systems, motion planning of automated vehicles in complex traffic scenarios is a tough issue. The problem of adjusting automated vehicles driving style is a difficult one. To address the above issues, this paper focuses on the spatio-temporal motion planning problem of automated vehicles in multi-vehicle conflict scenarios. A risk-field based motion planning method is proposed. By predicting the state of the vehicles in the traffic scenario, the risk field matrix is built for motion planning. The idea of path-velocity separation planning is adopted to divide the motion planning into two layers as path planning and trajectory planning. In path planning, static risk matrices serve as constraints. Considering the driving safety and comfort, the path cost function is designed to evaluate the candidate path group, obtained by longitudinal sampling and lateral sampling, to determine the target path. The dynamic risk matrices are used to construct the spatio-temporal graph corresponding to the target path, based on which dynamic planning and quadratic planning are introduced to solve the trajectory planning problem. Simulations were built to prove the practicability of the method. Results show that by adjusting risk threshold can change the aggressiveness of autonomous vehicles driving style.

This study attempts to elucidate individual car-following behavior using risk homeostasis theory (RHT). On the basis of this theory and the stimulus-response concept, we develop a desired safety margin (DSM) model. Safety margin, defined as the level of perceived risk in car-following processes, is proposed and considered to be a stimulus parameter. Acceleration is assessed in accordance with the difference between the perceived safety margin (perceived level of risk) and desired safety margin (acceptable level of risk) of a driver in a car-following situation. Sixty-three cases selected from Next Generation Simulation (NGSIM) are used to calibrate the parameters of the proposed model for general car-following behavior. Other eight cases with two following cars taken from NGSIM are used to validate the model. A car-following case with stop-and-go processes is also used to demonstrate the performance of the proposed model. The simulation results are then compared with the calculations derived using the Gazis-Herman-Rothery (GHR) model. As a result, the DSM and GHR models yield similar results and the proposed model is effective for simulation of car following. By adjusting model parameters, the proposed model can simulate different driving behaviors. The proposed model gives a new way to explain car-following process by RHT.