Traditional uncoordinated traffic flows in a roundabout can lead to severe traffic congestion, travel delay, and the increased fuel consumption of vehicles. An interesting way to mitigate this would be through cooperative control of connected and automated vehicles (CAVs). In this paper, we propose a novel solution, which is a roundabout control system (RCS), for CAVs to attain smooth and safe traffic flows. The RCS is essentially a bi-level framework, consisting of higher and lower levels of control, where in the higher level, vehicles in the entry lane approaching the roundabout will be made to form clusters based on traffic flow volume, and in the lower level, the vehicles' optimal sequences and roundabout merging times are calculated by solving a combinatorial optimization problem using a receding horizon control (RHC) approach. The proposed RCS aims to minimize the total time taken for all approaching vehicles to enter the roundabout, whilst minimally affecting the movement of circulating vehicles. Our developed strategy ensures fast optimization, and can be implemented in real-time. Using microscopic simulations, we demonstrate the effectiveness of the RCS, and compare it to the current traditional roundabout system (TRS) for various traffic flow scenarios. From the results, we can conclude that the proposed RCS produces significant improvement in traffic flow performance, in particular for the average velocity, average fuel consumption, and average travel time in the roundabout.
In this study, we theoretically investigated the effect of step gate work function on the InGaAs p-TFET device, which is formed by dual material gate (DMG). We analyzed the performance parameters of the device for low power digital and analog applications based on the gate work function difference (∆ϕS-D) of the source (ϕS) and drain (ϕD) side gate electrodes. In particular, the work function of the drain (ϕD) side gate electrodes was varied with respect to the high work function of the source side gate electrode (Pt, ϕS = 5.65 eV) to produce the step gate work function. It was found that the device performance varies with the variation of gate work function difference (∆ϕS-D) due to a change in the electric field distribution, which also changes the carrier (hole) distribution of the device. We achieved low subthreshold slope (SS) and off-state current (Ioff) of 30.89 mV/dec and 0.39 pA/µm, respectively, as well as low power dissipation, when the gate work function difference (∆ϕS-D = 1.02 eV) was high. Therefore, the device can be a potential candidate for the future low power digital applications. On the other hand, high transconductance (gm), high cut-off frequency (fT), and low output conductance (gd) of the device at low gate work function difference (∆ϕS-D = 0.61 eV) make it a viable candidate for the future low power analog applications.
Energy consumption and emissions of a vehicle are highly influenced by road contexts and driving behavior. Especially, driving on horizontal curves often necessitates a driver to brake and accelerate, which causes additional fuel consumption and emissions. This paper proposes a novel optimal ecological (eco) driving scheme (EDS) using nonlinear model predictive control (MPC) considering various road contexts, i.e., curvatures and surface conditions. Firstly, a nonlinear optimization problem is formulated considering a suitable prediction horizon and an objective function based on factors affecting fuel consumption, emissions, and driving safety. Secondly, the EDS dynamically computes the optimal velocity trajectory for the host vehicle considering its dynamics model, the state of the preceding vehicle, and information of road contexts that reduces fuel consumption and carbon emissions. Finally, we analyze the effect of different penetration rates of the EDS on overall traffic performance. The effectiveness of the proposed scheme is demonstrated using microscopic traffic simulations under dense and mixed traffic environment, and it is found that the proposed EDS substantially reduces the fuel consumption and carbon emissions of the host vehicle compared to the traditional (human-based) driving system (TDS), while ensuring driving safety. The proposed scheme can be employed as an advanced driver assistance system (ADAS) for semi-autonomous vehicles.