Virtual simulation testing is crucial for ensuring automated vehicles safety, which offers low cost and good repeatability. The key is to test in various virtual driving scenarios, but often fails to strike a balance between scenario coverage and test efficiency. To address this issue, we propose a scenario generation method based on a Genetic Algorithm optimized Hamiltonian Monte Carlo sampling approach. Specifically, a Markov chain is constructed converging to the joint probability density distribution function of scenario parameters. By defining a Hamiltonian function with a potential energy term related to the posterior distribution and a kinetic energy term, the sampling process moves efficiently towards high probability regions and achieves faster convergence to the target distribution. Moreover, Jensen-Shannon divergence between generated samples and raw data is proposed to evaluate the scenario coverage, and used as the objective function in Genetic Algorithm to optimize the algorithm parameters. The proposed method is validated by lead vehicle deceleration scenarios generation, where an S-shaped deceleration model is proposed to parameterize the scenario and 22,343 segments extracted from a naturalistic driving dataset are used to calibrate the scenario parameters. Subsequently, the joint probability distribution density function of scenario parameters fitted by a gaussian mixture model is imported into our proposed method. As the results, 1,649 concrete lead vehicle deceleration scenarios are generated with a coverage of 99.14% for the dataset. Compared with the previous Markov Chain Monte Carlo sampling method, our method achieved 9 times higher coverage with 28 times fewer scenarios.

Nowadays, the variance reduction (VR) technique is used to improve the performance of gradient-type algorithms in machine learning and deep learning. However, some existing VR algorithms require unrealistic assumptions or conditions such as τ-gradient dominated and Polyak-Lojasiewicz (PL) conditions, which limit their applications. In this paper, we present a Double Mini-batch StochAstic Recursive grAdient algoritHm (DM-SARAH) without these assumptions or conditions to solve the convex and non-convex optimization problems respectively. The main contributions of this paper are twofold: (1) At the theoretical level, we optimize the convergence rate and provide a complexity analysis of DM-SARAH, and (2) At the experimental level, we evaluate the effectiveness and efficiency of the proposed algorithm on various datasets. The experimental results indicate that the proposed algorithm outperforms existing methods.

Accurate modeling of lower-level controller plays an important role in the traffic flow of automated vehicles (AVs). However, there lacks enough attention with this respect. To address this issue, we conduct a field experiment with two vehicles that are equipped with developable autonomous driving system, where one can customize the upper-level control algorithm. Based on the field experimental data, two new lower-level control models are developed and compared with two widely used ones. The comparison results show that the proposed models outperform the two previous models in capturing the observed actual acceleration, especially the troughs of the acceleration time series. Furthermore, theoretical analysis indicates that comparing with the proposed models, the two previous models significantly overestimate the stability region of the traffic flow of the AVs and the capacity of stable traffic flow. Our study is expected to further shed light on the importance of accurate lower-level control modeling.

Deep deterministic policy gradient (DDPG)-based car-following strategy can break through the constraints of the differential equation model due to the ability of exploration on complex environments. However, the car-following performance of DDPG is usually degraded by unreasonable reward function design, insufficient training, and low sampling efficiency. In order to solve this kind of problem, a hybrid car-following strategy based on DDPG and cooperative adaptive cruise control (CACC) is proposed. First, the car-following process is modeled as the Markov decision process to calculate CACC and DDPG simultaneously at each frame. Given a current state, two actions are obtained from CACC and DDPG, respectively. Then, an optimal action, corresponding to the one offering a larger reward, is chosen as the output of the hybrid strategy. Meanwhile, a rule is designed to ensure that the change rate of acceleration is smaller than the desired value. Therefore, the proposed strategy not only guarantees the basic performance of car-following through CACC but also makes full use of the advantages of exploration on complex environments via DDPG. Finally, simulation results show that the car-following performance of the proposed strategy is improved compared with that of DDPG and CACC. Note to Practitioners—This article presents a new car-following strategy, which avoids the impact of deep deterministic policy gradient (DDPG) performance degradation on the system. In the proposed strategy, DDPG is replaced with cooperative adaptive cruise control (CACC) when the performance of DDPG is worse than that of CACC. Meanwhile, a switching rule is designed to guarantee that the change rate of acceleration is smaller than the threshold. Simulation results show that the performance of hybrid car-following strategy has been improved compared with that of only using CACC or DDPG. Moreover, the proposed strategy has the advantages of low computational burden, high real-time performance, and good scalability.

Vehicles stopping at signalized intersections during a red light is one of the main causes of traffic oscillations. Recently, deep reinforcement learning (DRL) methods have been applied to connected and automated vehicles (CAVs) traffic to reduce the traffic oscillation at signalized intersections. However, these methods do not perform well for mixed traffic flow, including human vehicles (HVs) and CAVs, especially when the CAV rate is low. We found that this was because they did not take into account the HVs stopping at a red light and causing oscillations. If this oscillation is ignored during the speed regulation, CAVs may conflict with the oscillation wave in the future, forcing a sudden and significant speed reduction and triggering the so-called “trajectory jerking” phenomenon. In order to address this problem, this study proposes a trajectory prediction-based DRL method. By introducing the prediction of the downstream vehicle trajectory into the design of the reward function, the leading CAV will adjust its speed in advance to avoid the future oscillation wave caused by HV’s stopping during the red phase. Simulation tests on various penetration rates of CAVs are conducted for the mixed traffic environment to evaluate the performance of the proposed method. The results show that the proposed method has two advantages. Even with low CAV penetration, the oscillations are suppressed remarkably well. And fuel consumption is also significantly reduced. This research provides a new idea to suppress traffic oscillations in a mixed traffic environment.

The optimal velocity-based model has been successfully applied to distributed car-following systems. However, the car-following performance is inevitably affected by a series of disturbances, particularly, sudden velocity changes of leading vehicle. To improve accuracy and response rate of car-following control in the presence of such disturbances, an improved super-twisting compensator (ISTC) is proposed and a composite controller is designed by combining ISTC with a finite- time controller. A second-order nominal system is constructed by using a virtual measurement signal along with its integration to facilitate the design of ISTC. By introducing the feedback of high-order estimation error, the accuracy and response rate of ISTC are increased significantly as compared with the conventional one under same gains. Such improvement further enhances the disturbance rejection ability of the composite controller. Both Lyapunov approach and numerical simulations are carried out to verify the effectiveness of the proposed method.

Cyberbullying has caused serious consequences especially for social network users in recent years. However, the challenge is how to fight against the cyberbullying effectively from the algorithmic perspective. In this article, we study the fighting against the cyberbullying problem, i.e., identify an initial witness set with a budget to spread the positive influence to protect the users in a specific target set such that the number of cybervictim users in the target set being activated by the seed set of cyberbullying is minimized. We first formulate this problem and show its NP-hardness. We further prove that the objective function is submodular with respect to the size of witnesses set when we convert the original problem into the maximal version. Then we propose a stochastic approach to solve this maximal version problem based on the Reverse Sampling Technique with a constant factor guarantee. In addition, we provide theoretical analysis and discuss the relationship between the optimal value and the value returned by the proposed algorithm. To evaluate the proposed approach, we implement extensive experiments on synthetic and real datasets. The experimental results show our approach is superior to the comparison methods.

Optimal velocity (OV)-based car-following model can be easily applied to the transportation system composed of intelligent connected vehicle (ICV) via vehicle-to-vehicle (V2V) communication, since this model only requires space headway and velocity differences of preceding vehicles. However, the sudden velocity changes of the leading vehicle will decrease the control performance of following vehicles. Particularly, the farther the distance between the following vehicle and the leading vehicle is, the worse the control performance of the following vehicle is. Besides this problem, the nonlinear information of OV-based car-following models is often not fully utilized due to the linearization for the convenience of stability analysis and controller design. To address these problems, the factors related to velocity sudden changes are taken into account for each ICV simultaneously and compensated by the proposed feedforward compensator. Based on the compensator, a finite-time traffic flow controller is designed for each ICV to smooth the space headway in traffic flow subject to sudden velocity changes and make full use of the nonlinear information of OV-based model. Finally, the theoretical analysis via Lyapunov approach and the numerical simulations are carried out to verify the effectiveness of the proposed method.

Ultra-wideband (UWB) as a state-of-the-art Real-Time Localization System (RTLS) has shown outstanding performance in tackling difficult positioning task. However, the implementation of this technology remains a challenge as several problems such as clock synchronization and line-of-sight (LOS) problem often occurs during integration. Moreover, when this technology faced with real-time multi targets detection, this technology still does not produce a stable result. This paper addresses these two problems by introduces a sound approach to tackle clock synchronization, LOS problem, and create a stable multi positioning system. We managed to secure 8.48 cm of RMS error for NLOS condition and 7.29 cm of RMS error for LOS condition. Besides, we also enhance the system by adding sensor fusion in order to create more effective multi targets localization in real-time condition. This enhancement derives from support by map information and speed sensor as a support system. Finally, this system is tested to support a realtime application of the model cars, and it can handle the task and obtains 11.27 cm of RMS error for dynamic positioning result.

The growing importance of social network for sharing and spreading various contents is leading to the changes in the way of information diffusion. To what extent can social content be diffused highly depends on the size of seed nodes and connectivity of the network. If the seed set is predetermined, then the best way to maximize the content spread is to add connectivities among the users. The existing work shows the content spread maximization problem to be NP-hard. One of the difficulties of designing an effective and efficient algorithm for the content spread maximization problem lies in that the objective function we aim to maximize lacks submodularity. In our work, we formulate the maximize content spread problem from an incremental marginal gain perspective. Although the objective function we derive is not submodular, both submodular lower and upper bounds are constructed and proved. Therefore, we apply the sandwich framework and devise a marginal increment-based algorithm (MIS) that guarantees a data-dependent factor. Furthermore, a novel scalable content spread maximization algorithm influence ranking and fast adjustment (IRFA), which is based on the influence ranking of a single node and fast adjustment with each boosting step in the network, is proposed. Through extensive experiments, we demonstrate that both MIS and IRFA algorithms are effective and outperform other edge selection strategies.

In recent years, social networks have become important platforms for people to disseminate information. However, we need to take effective measures such as blocking a set of links to control the negative rumors spreading over the network. In this article, we propose a Rumor Spread Minimization (RSM) problem, i.e., we remove an edge set from network such that the rumor spread is minimized. We first prove the objective function of RSM problem is not submodular. Then, we propose both submodular lower-bound and upper-bound of the objective function. Next, we develop a heuristic algorithm to approximate the objective function. Furthermore, we reformulate our objective function as the DS function (the Difference of Submodular functions). Finally, we conduct experiments on real-world datasets to evaluate our proposed method. The experiment results show that the upper and lower bounds are very close, which indicates the good quality of them. And, the proposed method outperforms the comparison methods.

One significant problem in exchange networks is finding the equilibrium. To solve this problem, the concept of stable outcome has been developed. However, there are few effective methods to solve it from the point of graph theory. In this paper, we propose a minimum cost stable outcome (MCSO) problem, which is to find a stable outcome whose total transaction cost is minimized. Two algorithms have been designed to solve this problem on unit and general profit networks respectively. For unit profit networks, we use minimum cost edge cover based method to give the optimal solution. For general profit networks, we develop an approximate algorithm and prove that performance ratio is no more than twice the optimal value. Moreover, we provide the probabilistic analysis. At last, extensive experiments have been conducted on synthetic and real-life datasets. Experimental results validate the performance of the proposed algorithms.

Influence maximization problem is to find a set of seeds in social networks such that the cascade influence is maximized. Traditional models assume that all nodes are willing to spread the influence once they are influenced, and they ignore the disparity between influence and profit of a product. In this paper, by considering the role that price plays in viral marketing, we propose price related (PR) frame that contains PR-I and PR-L models for classic independent cascade and linear threshold models, respectively, which is a pioneer work. Two pricing strategies are designed, one is binary pricing (BYC), in which the seeds are offered free samples. The other is panoramic pricing (PAP), in which the seeds are offered different discounts. Furthermore, we find that influence and profit are like two sides of the coin, high price hinders the influence propagation and to enlarge the influence some sacrifice on profit is inevitable. Based on this observation under PR frame, by adopting a parameter to denote the decision maker's preference toward influence and profit, we propose balanced influence and profit (BIP) maximization problem. We prove the NP-hardness of BIP maximization under PR-I and PR-L model. Unlike influence maximization, the BIP objective function is not monotone. Despite the nonmonotony, we show BIP objective function is submodular under certain conditions. Two unbudgeted greedy algorithms separately, named algorithm of BYC and algorithm of PAP are devised. We conduct extensive simulations on real world data sets, test the effectiveness of our proposed parameters, compare the algorithms' performances, and evaluate the superiority of our algorithms over existing ones.