Extracting roads in aerial images has numerous applications in artificial intelligence and multimedia computing, including traffic pattern analysis and parking space planning. Learning deep neural networks, though very successful, demand vast amounts of high-quality annotations, of which acquisition is time-consuming and expensive. In this work, we propose a semi-supervised approach for image-based road extraction in which only a small set of labeled images are available for training to address this challenge. We design a pixel-wise contrastive loss to self-supervise the network training to utilize the large corpus of unlabeled images. The key idea is to identify pairs of overlapping image regions (positive) or non-overlapping image regions (negative) and encourage the network to make similar outputs for positive pairs or dissimilar outputs for negative pairs. We also develop a negative sampling strategy to filter false-negative samples during the process. An iterative procedure is introduced to apply the network over raw images to generate pseudo-labels, filter and select high-quality labels with the proposed contrastive loss, and retrain the network with the enlarged training dataset. We repeat these iterative steps until convergence. We validate the effectiveness of the proposed methods by performing extensive experiments on the public SpaceNet3 and DeepGlobe Road datasets. Results show that our proposed method achieves state-of-the-art results on public image segmentation benchmarks and significantly outperforms other semi-supervised methods.

This paper proposed a speed harmonization controller for partially connected and automated traffic. It regulates the flow rate of the entire traffic by adjusting only the target cruising speed of Connected and Automated Vehicles (CAVs). The proposed controller bears the following features: i) compatibility enabled with partially connected and automated traffic consisting of CAVs and Human-driven Vehicles (HVs); ii) stability ensured for the traffic system under control; iii) precision guaranteed for the demand management on a multi-lane road with the help of a small portion of vehicles. To evaluate the proposed controller, a microscopic simulation evaluation was conducted. Results confirm that the control accuracy of the proposed controller is generally over 80% across all CAV Penetration Rates, demand levels (v/c ratio) and target demand drops (within 20%). A case study is presented to demonstrate the benefit of applying the proposed controller on a bottleneck. By preventing the onset of a breakdown and, along with it, a capacity drop, the proposed controller is able to increase the flow rate by 6%, reduce the number of stops by up to 90% and delay by approximately 5%.

This research aims to model system dynamics for mixed traffic flow consisting of Connected and Automated Vehicles (CAVs) and Human-driven Vehicles (HVs). It quantifies the impact of CAVs’ speed change on the overall traffic state on a real-time basis. The model describes the impedance of CAVs’ speed reduction on traffic flow and considers the impact of potential additional lane change induced by the speed reduction. To validate the effectiveness of the proposed model, a VISSIM based microscopic simulation evaluation is performed. The results confirm that the accuracy of the proposed model is generally over 80% with the CAVs’ speed reduction constrained within 20 km/h. Sensitivity analysis is conducted in terms of various CAV penetration rates and congestion levels. The proposed model demonstrates consistently good performance across all CAV penetration rates and congestion levels. A showcase is presented to show the effect of the system dynamics in active traffic management. The proposed model could serve as the foundation of CAV based traffic management applications, such as variable speed limit and speed harmonization.

The string stability of cooperative adaptive cruise control (CACC) platoons is largely affected by complex driving environment and abnormal driving behaviors. Fast and repetitive driving-state changes always occur during the period of changing driving states (such as leaving a platoon or lane-change), due to errors made in driver decisions or automatic driving system. This research proposes a framework which combines recognition of driving states with platoon operations and risk-prediction in order to reduce disturbance and unnecessary platoon operations resulting from driving-state jitters. First of all, long short-term memory (LSTM) neural networks were used in this research combined with a time-window in order to recognize driving states. Based on this research, the LSTM mode with an added time-window was found to be able to effectively reduce comparatively the jitters of recognition results. After that, an integrated mode which incorporates a recognition mode with danger probabilities was demonstrated to present better platoon operations. Monte Carlo simulation and importance sampling method will be given to predict platoons’ and vehicles’ trajectories and compute danger probabilities. In addition, an innovative strategy is implemented to identify an additional leader and execute a platoon splitting in order to improve driving smoothness, if a vehicle is recognized in an abnormal car-following state with a high danger-probability. In summary, this research has conducted extensive numerical tests to evaluate performances of the proposed system and the analysis results show that the proposed strategies will effectively increase smoothness and safety for a multi-platooning system.

This research proposes a TSPcut controller that enables connected and automated buses to cut through traffic to make TSP green light. The proposed controller overcomes the shortcomings of conventional TSP strategies and is able to: 1) overtake slowing moving vehicles in order to catch TSP green time; 2) decide the best time to pass the intersection; 3) considering the stochasticity of surrounding traffic; and 4) functional under partially connected and automated environment. It takes full advantage of connected vehicle technology by taking in real-time vehicle and infrastructure information as optimization input. The problem is formulated as an SMPC problem and is solved by a high-efficient dynamic programming algorithm. The nonlinear bicycle model is adopted as the system dynamics to realize CAV bus’s lane-changing and overtaking function. The stochasticity of surrounding traffic is considered as a probability distribution which is transformed into a linear chance constraint. Simulation evaluation is conduct to compare the TSPcut against NTSP, CTSP and BocTSP. Sensitive analysis is conducted for congestion levels. The evaluation results demonstrate that the TSPcut improves the bus delay reduction by 17.9%–49.1%, and the benefits are 3.5% to 16.1% greater than that of other TSP systems. The range is caused by different congestion levels. In addition. Further tests are conducted to analyze how CAV bus’s arrival time and the speed of background traffic influence the performance of the TSPcut.

Traffic flow modeling for traffic state estimation is a vital component in many traffic management and operation systems. To leverage both machine learning (ML) methods and classical traffic flow models, the previous study has developed a hybrid framework for encoding traffic flow into multivariant Gaussian Process. However, the computational efficiency is low due to multiple inputs, outputs and equations. To improve the efficiency of the previous method, this paper presents a new modeling framework, named gradual physics regularized learning, to incrementally encode complex traffic flow models into the ML process. More specifically, the method starts with the involvement of traffic flow models from the lower-order version, such as the fundamental diagram and the kinetic wave models. Then the learned parameters and hyperparameters can be further fine-tuned with the high-order models. A field test based on real-world freeway measurements indicates the proposed model can leverage the additional physical equations to achieve better performance in estimation accuracy and robustness. Meanwhile, the gradual learning method can significantly reduce the computational efforts and further enables its application to scenarios with either larger datasets or more complex traffic flow models.

This paper proposed a speed harmonization controller for partially connected and automated traffic. It regulated the flow rate of the entire traffic by adjusting only the target cruising speed of Connected and Automated Vehicles (CAVs). The major breakthrough of the proposed controller is that it is able to manage mesoscopic level traffic by controlling microscope level status (desired speed) of a small portion of vehicles. To evaluate the proposed controller, a VISSIM based microscopic simulation evaluation was conducted. Sensitivity analysis was performed for CA V Penetration Rate (PR) and demand level (v/c ratio). Results confirm that the control accuracy of the proposed controller is over 85% across all CA V PRs and demand levels.

This study aims to leverage the advances of connected automated vehicle (CAV) technology to design an eco-driving and platooning system that can improve both fuel and operational efficiency of vehicles on the freeways. The proposed algorithm optimizes CAVs’ trajectories with three objectives, including travel time minimization, fuel consumption minimization, and traffic safety improvement, following a two-stage control logic. The first stage, designed for CAV trajectory planning, is carried out with two optimization models. The first model functions to predict the freeway traffic states in the near future and accordingly optimize CAVs’ desired speed profile to minimize total freeway travel time. Notably, the interactions between CAVs and human-driven vehicles (HVs) are described in the embedded traffic flow model and the optimization can fully account for CAVs’ impact to HVs’ speeds. Then grounded on the obtained speed profile, the second eco-driving model would further update it so as to platoon CAVs and minimize their fuel consumption. The second stage, for real-time control purpose, is developed to ensure the operational safety of CAVs. Particularly, based on the speed profile from the first stage, real-time adaptions would be placed on CAVs to dynamically adjust speeds, in response to local driving conditions. To evaluate the proposed algorithms, this study selects a freeway segment of I-15 in Salt Lake City as the study site. The extensive numerical simulation results confirmed the effectiveness of the proposed framework in both mitigating freeway congestion and reducing vehicles’ fuel consumption.

In this paper, we propose a new design framework on Device-to-Device (D2D) coded caching networks with optimal communication load (rate) but significantly less file subpacketizations compared to that of the well-known D2D coded caching scheme proposed by Ji, Caire and Molisch (JCM). The proposed design framework is referred to as the Packet Type-based (PTB) design, where each file is partitioned into packets according to their pre-defined types while the cache placement and user multicast grouping are based on the packet types. This leads to the so-called raw packet saving gain for the subpacketization levels. By a careful selection of transmitters within each multicasting group, a so-called further splitting ratio gain of the subpacketizatios can also be achieved. By the joint effect of the raw packet saving gain and the further splitting ratio gain, an order-wise subpacketization reduction can be achieved compared to the JCM scheme while preserving the optimal rate. In addition, as the first time presented in the literature according to our knowledge, we find that unequal subpacketizaton is a key to achieve subpacketization reductions when the number of users is odd. As a by-product, instead of directly translating shared link caching schemes to D2D caching schemes, at least for the sake of subpackeitzation, a new design framework is indeed needed.

To improve a vehicle's fuel efficiency when operating on roadways, this study develops an ecological driving system under the connected and automated vehicle (CAV) environment. The system includes three critical functions, including traffic state prediction, eco-driving speed control, and powertrain control implementation. According to the real-time traffic information obtained from vehicle-to-infrastructure and vehicle-to-vehicle communications, the embedded traffic state prediction model will estimate and predict the average speeds and densities of freeway subsections. With an objective of minimizing the fuel consumption, the eco-driving speed control function follows a two-stage hierarchical framework. The first stage, which is executed at the global level, aims to optimize the travel speed profile of the CAV over a certain time period. The second stage, local speed adaption, is designed to dynamically adjust the CAV's speed and make lane-changing decisions based on the local driving condition. The resulting control parameters will then be forwarded to the powertrain control system for implementations. To evaluate the proposed system, this study performs comprehensive numerical tests by using simulation models. This results confirm the effectiveness of the proposed system in reducing fuel consumption. Further comparisons with different models highlights the need to consider traffic state information in the first-stage optimization and lane-changing decision module in the local adaption function.

Most existing discussions regarding the time-dependent distribution of queue length was undertaken in the context of isolated intersections. However, computing queue length distributions for a signalized network with generic topology is very challenging because such process involves convolution and nonlinear transformation of random variables, which is analytically intractable. To address such issue, this study proposes a stochastic queue model considering the strong interdependence relations between adjacent intersections using the probability generating function as a mathematical tool. Various traffic flow phenomena, including queue formation and dissipation, platoon dispersion, flow merging and diverging, queue spillover, and downstream blockage, are formulated as stochastic events, and their distributions are iteratively computed through a stochastic network loading procedure. Both theoretical derivation and numerical investigations are presented to demonstrate the effectiveness of the proposed approach in analyzing the delay and queues of signalized networks under different congestion levels.