Energy consumption of a vehicle is greatly influenced by its driving behavior in highly interacting urban traffic. Strategies for fuel efficient driving have been studied and experimented with in various conceptual frameworks. This paper presents a novel control system to drive a vehicle efficiently on roads containing varying traffic and signals at intersections for improved fuel economy. The system measures the relevant information of the current road and traffic, predicts the future states of the preceding vehicle, and computes the optimal vehicle control input using model predictive control (MPC). A typical control objective is chosen to maximize fuel economy by regulating a safe head-distance or cruising at the optimal velocity under bounded driving torque condition. The proposed vehicle control system is evaluated in urban traffic containing thousands of diverse vehicles using the microscopic traffic simulator AIMSUN. Simulation results show that the vehicles controlled by the proposed MPC method significantly improve their fuel economy.

Human facial emotion recognition (FER) has attracted the attention of the research community for its promising applications. Mapping different facial expressions to the respective emotional states are the main task in FER. The classical FER consists of two major steps: feature extraction and emotion recognition. Currently, the Deep Neural Networks, especially the Convolutional Neural Network (CNN), is widely used in FER by virtue of its inherent feature extraction mechanism from images. Several works have been reported on CNN with only a few layers to resolve FER problems. However, standard shallow CNNs with straightforward learning schemes have limited feature extraction capability to capture emotion information from high-resolution images. A notable drawback of the most existing methods is that they consider only the frontal images (i.e., ignore profile views for convenience), although the profile views taken from different angles are important for a practical FER system. For developing a highly accurate FER system, this study proposes a very Deep CNN (DCNN) modeling through Transfer Learning (TL) technique where a pre-trained DCNN model is adopted by replacing its dense upper layer(s) compatible with FER, and the model is fine-tuned with facial emotion data. A novel pipeline strategy is introduced, where the training of the dense layer(s) is followed by tuning each of the pre-trained DCNN blocks successively that has led to gradual improvement of the accuracy of FER to a higher level. The proposed FER system is verified on eight different pre-trained DCNN models (VGG-16, VGG-19, ResNet-18, ResNet-34, ResNet-50, ResNet-152, Inception-v3 and DenseNet-161) and well-known KDEF and JAFFE facial image datasets. FER is very challenging even for frontal views alone. FER on the KDEF dataset poses further challenges due to the diversity of images with different profile views together with frontal views. The proposed method achieved remarkable accuracy on both datasets with pre-trained models. On a 10-fold cross-validation way, the best achieved FER accuracies with DenseNet-161 on test sets of KDEF and JAFFE are 96.51% and 99.52%, respectively. The evaluation results reveal the superiority of the proposed FER system over the existing ones regarding emotion detection accuracy. Moreover, the achieved performance on the KDEF dataset with profile views is promising as it clearly demonstrates the required proficiency for real-life applications.

This paper presents a coordination scheme of automated vehicles at an intersection without using any traffic lights. Using a two-way communication network, vehicles approaching the intersection from all sections are globally coordinated, by considering their states all together in a model predictive control framework, in order to achieve smooth traffic flows at the intersection. The optimal trajectories of the vehicles are computed based on avoidance of their cross-collision risks around the intersection under relevant constraints and preferences. The scheme efficiently utilizes the intersection area by preventing each pair of conflicting vehicles from approaching their cross-collision point at the same time, instead of reserving the whole intersection area for the conflicting vehicles one after another. The scheme also enables left- or right-turning movements of vehicles under constrained velocity without using any auxiliary lanes. The proposed vehicle-intersection coordination scheme is evaluated through numerical simulation in a typical test intersection consisting of both multilanes and single-lane approaches with turning movements of vehicles. Observations under different traffic flow conditions reveal that the proposed scheme significantly improves intersection performance compared with the traditional signalized intersection scheme.

Traffic management on road networks is an emerging research field in control engineering due to the strong demand to alleviate traffic congestion in urban areas. Interaction among vehicles frequently causes congestion as well as bottlenecks in road capacity. In dense traffic, waves of traffic density propagate backward as drivers try to keep safe distances through frequent acceleration and deceleration. This paper presents a vehicle driving system in a model predictive control framework that effectively improves traffic flow. The vehicle driving system regulates safe intervehicle distance under the bounded driving torque condition by predicting the preceding traffic. It also focuses on alleviating the effect of braking on the vehicles that follow, which helps jamming waves attenuate to in the traffic. The proposed vehicle driving system has been evaluated through numerical simulation in dense traffic.

This paper presents a novel adaptive traffic signal control scheme that addresses a mixed manual-automated traffic scenario in a typically isolated intersection. The traffic signals are optimized in a receding horizon control framework that aims at minimizing the total crossing time of all vehicles, considering their dynamical states. The control scheme ensures comfortable crossing of manually driven vehicles by retaining the basic features of the traditional signal management systems. The optimal signal changing times are broadcasted one cycle ahead, which enables the automated vehicles to tune their speed in order to cross the intersection with minimum stop-delay. More specifically, the framework optimizes the green time of each signal without considering the existing cycle-split concept explicitly. The proposed signal control scheme is evaluated in microscopic traffic simulation considering the different proportion of turning traffic at the intersection and various penetration rates of the automated vehicles. It is observed that the optimization process usually results in the shortest possible green period of each signal that can be realized without reducing the capacity of the intersection at any traffic volumes. Consequently, the resulting short signal cycle which is adaptive to the traffic around the intersection improves the average speed and reduces both the traffic density and the number of idling vehicles. As a consequence, the fuel consumption efficiency and the rate of CO₂ emission around the intersection are also reduced. These results are compared with both the traditional fixed time and the actuated signal control schemes. As the portion of the automated vehicles increases in the case of the proposed scheme, the overall traffic flow performance further improves.

Traffic anticipation enhances driving intelligence and strengthens the ability to take early vehicle control action, e.g., lane change and speed adjustment, in a dynamically varying traffic environment. This paper presents an efficient vehicle driving system, based on detailed anticipation of surrounding traffic, that aims at optimizing the driving performance of individual vehicles and smoothening traffic flows on multilane roads. More elaborately, under a connected vehicle environment, the system receives the states of all vehicles that exist within its communication range. Based on their predicted states in a look forward horizon, the system generates the optimal acceleration and makes lane change decision simultaneously in the model predictive control framework. A fast hierarchical optimization scheme is used in the framework for its onboard implementation. The proposed efficient driving system is applied to a fraction of traffic, and both the individual and overall traffic performances are evaluated using a microscopic traffic simulator. It is revealed that the vehicles under the proposed efficient driving system improve their fuel economy and travel efficiency, significantly. In the mixed traffic, by the influence of the vehicle with the proposed driving system, the other traditionally driven vehicles also improve their performance.