Road geometry (e.g. slope and curvature) has significant impacts on driving behaviours of low-level automated vehicles (AVs), but it has been largely ignored in microscopic traffic models. To capture these effects, this study proposes a generic approach to extend any (free-flow or car-following) microscopic models characterized by acceleration functions. To this end, three model extensions are developed, each of which can use different submodels for comparison. Their effectiveness is demonstrated with a microscopic free-flow model that represents the AVs’ control logic. Finally, all possible combinations of the microscopic model and the model extensions are calibrated and cross-validated against data sets, which contain empirical trajectories of commercial adaptive cruise control (ACC) systems on different test tracks. Results suggest that two submodels, i.e., the nonlinear vehicle dynamics (NVD) and the radius difference method (RDM), can extend the microscopic model to effectively capture the effects of road slope and road curvature, respectively, on automated driving. Specifically, the NVD is the dominant factor contributing to increasing (by 34.9% on average) model accuracy. In addition, when simulating reckless turning behaviours (i.e. the vehicle turns at high speeds), the inclusion of the developed RDM is significant for model performance, and the models extended with both the NVD and the RDM can achieve the largest accuracy gains (39.6%).

In the scientific literature, the ratio between the total travel cost under a user equilibrium assignment and the total travel cost under a system optimum assignment is typically referred to as the Price of Anarchy (PoA), i.e., the level of inefficiency that can be eliminated by centralized control in routing. Recently, this concept has been attracting renovated attention due to the new opportunities offered by vehicles’ connectivity and automation. The new technologies could allow individual prescriptions centrally managed to achieve benefits in network performance. However, considering the infrastructure that such a system would need and the ethical implications it could have (related to privacy, equity, etc.), it is necessary to carefully quantify its actual benefits. Existing PoA related studies do not fully capture the essential realism of traffic dynamics and show contradictory results. Moreover, there are no studies that explore the relation of the PoA and driving behavior (e.g., reaction time, acceleration, deceleration, aggressiveness, etc.), which is very important for designing the algorithms of automated driving. In this light, the present paper investigates the PoA over a Braess-like network using the most detailed way to model traffic—the microscopic simulation with parameters defining the driving behavior. The magnitude of the PoA has been studied over a wide range of combinations of the model input parameters. Results show that the PoA can be much higher than that obtained in theoretical studies. In addition, results are used to reveal some PoA features of real traffic networks and propose further research directions.

Multiple sensing technologies exist for advanced driver support and driving automation systems. Car manufacturers combine different solutions to increase the robustness and operational capabilities of driving automation technology perception systems. One sensing method that did not achieve high market penetration with respect to other solutions is the thermal camera. Thermal cameras can operate in low-light and high-contrast conditions as they do not need external sources of illumination. Instead, they are sensitive to the infrared radiation emitted by any surface above 0 K. Thus, thermal cameras can be a suitable candidate to compensate for the weaknesses of visible cameras. To establish the potential benefit of thermal cameras for pedestrian protection, this paper reports on an experiment using state-of-the-art thermal imaging sensors. The authors’ scientific effort includes a characterization of the infrared sensor capability to identify pedestrians when coupled with a detection algorithm and provides a simulation-based investigation of the potential safety benefits of thermal camera adoption. The analysis additionally includes a heuristic characterization of the detection capabilities of a passenger car equipped with visible cameras to provide a baseline comparison. Ultimately, the thermal sensors return noteworthy detection capability that could lead to a potentially significant improvement in road safety on rural roads provided that the sensor has sufficient resolution.