In this paper, we propose a progressive model predictive control scheme (PMPCS) by considering the cooperative control of local planning and path tracking for intelligent vehicles. An improved particle swarm optimization (IPSO) based model predictive control (MPC) method is developed to solve the planning and tracking problem. With the PMPCS, the total computational burden can be reduced sharply because of the seamless connection and mutual promotion between the optimization of two layers. Besides we also propose a novel planning algorithm, which can take traffic lights and overtaking time constraint into account. To solve these problems, we first combine model predictive control with artificial potential field (APF) to get a collision-free path by treating the timevarying safety constraints as the scope of the repulsive force and an asymmetrical lane potential field function. Furthermore, the pseudo velocity planning method is adopted to take traffic lights into account in the planning module. Simulation results show the reliability of the proposed algorithm and the advantages of the scheme compared with general hierarchical algorithm.

Most applications of underwater cyber-physical system (UCPS) demand reliable data collection in an efficient and timely manner. However, the energy-constrained characteristic on acoustic communication makes it challenging to collect data. In this paper, we study the energy-efficient data collection for UCPS over autonomous underwater vehicle (AUV) assisted underwater acoustic sensor network. The sensor nodes perceive local physical parameters, whose lifetime is strictly restricted by local power. The AUV that has relatively sufficient energy is adopted to retrieve data from sensor nodes. To balance energy consumption and prolong underwater network lifetime, the energy optimization problem is formulated as minimizing the sum of edge lengths of a particular graph. Then, a min-weighted rigid graph based topology optimization scheme is developed for sensor deployment. With the optimized network topology, a local routing decision algorithm is developed for each sensor node to relay the collected data to the data collectors (sinks). A dynamic value-based path planning strategy is also designed for the AUV to dynamically visit data collectors to maximize the value of information (VoI) within a given time. Finally, simulation results demonstrate that the network lifetime with the topology optimization scheme can be prolonged, and the dynamic value-based path planning strategy can improve the VoI on AUV.

Security problem is a fundamental issue for autonomous vehicles. Trajectory planning is a significant component of autonomous vehicle system, which directly influences the automated traffic safety. In this paper, the motion prediction of other traffic participants is considered. We use Monte Carlo simulation to predict the probabilistic occupancy of the object, and give a map from probability statistics to actual scenarios. The non-time based reference trajectory can be obtained by using high precision map (HD map) and lane detection. Then model predictive control (MPC) is utilized to optimize the reference trajectory according to the current state of autonomous vehicle. Different prediction horizons and coordinate transformation are adopted to optimize the planning. By doing so, the constraint conditions can be easily involved and the result is more intuitive. The probabilistic occupancy of other traffic participants are computed offline and then the obtained results are used in real time application. Therefore, the real-time computational burden is reduced. The crash probability is put forward to verify the feasibility of real-time trajectory in safety assessment module. Two typical scenarios are analyzed: lane change on the straight road and turning at the intersection. The simulation results illustrate the efficiency of our method.

A novel integral sliding mode control (ISMC) strategy combining with disturbance observer (DO) for vehicle-following systems is presented in this paper. The vehicle platoon includes a leading vehicle and multiple following vehicles subjected to unknown acceleration uncertainties. Firstly, the ISMC based on DO and the constant time headway (CTH) policy is constructed to realize the string stability of the whole vehicle platoon in finite time, under the condition of zero initial spacing errors. The uncertainties of the vehicular system can be well estimated by extending DO even if the bounds of the uncertainties are not known exactly. In order to alleviate the chattering problem of the traditional ISMC, a continuous sliding surface including sign function is designed for the proposed ISMC strategy. Then, a modified constant time headway (MCTH) policy is proposed to overcome the requirement of zero initial spacing errors. In addition, the developed MCTH policy can effectively improve the string stability and safety of the vehicle platoon system. Finally, the effectiveness and advantages of the proposed algorithm are verified by the simulations and experiments.

Most applications of underwater acoustic sensor networks (UASNs) rely upon reliable location information of sensor nodes. However, the harsh underwater environment makes localization more challenging as compared to terrestrial sensor networks. This paper is concerned with a localization issue for UASNs, subject to clock asynchronization and node mobility. A hybrid architecture including autonomous underwater vehicles (AUVs), active and passive sensor nodes is designed, where AUVs act as anchor nodes to provide localization information for sensor nodes. In order to eliminate the effect of asynchronous clocks and compensate the mobility of sensor nodes, an asynchronous localization algorithm with mobility prediction is provided for active and passive sensor nodes. Then, two localization optimization problems are formulated as minimizing the sum of all measurement errors. To solve the localization optimization problems, iterative least squares estimators are designed. In addition, the convergence analysis and Cramér-Rao lower bound for the localization errors are also provided. Finally, simulation results show that the proposed localization approach can reduce the localization time by comparing with the exhaustive search-based localization method. Meanwhile, the asynchronous algorithm in this paper can effectively eliminate the impact of the clock asynchronization and node mobility.

Localization is a critical issue for many location-based applications in the Internet of Underwater Things (IoUT). Nevertheless, the asynchronous time clock, stratification effect and mobility properties of underwater environment make it much more challenging to solve the localization issue. This paper is concerned with an autonomous underwater vehicle (AUV) aided localization issue for IoUT. We first provide a hybrid network architecture that includes surface buoys, AUVs, active and passive sensor nodes. On the basis of this architecture, an asynchronous localization protocol is designed, through which the localization problem is provided to minimize the sum of all measurement errors. In order to make this problem tractable, a reinforcement learning (RL) based localization algorithm is developed to estimate the locations of AUVs, active and passive sensor nodes, where an online value iteration procedure is performed to seek the optimization locations. It is worth mentioned that, the proposed localization algorithm adopts two neural networks to approximate the increment policy and value function, and more importantly, it is much preferable for nonsmooth and nonconvex underwater localization problem due to its insensitivity to the local optimal. Performance analyses for the RL-based localization algorithm are also provided. Finally, simulation and experimental results reveal that the localization performance in this paper can be significantly improved as compared with the other works.