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In view of the rapid development of edge computing and vehicular network, edge-assisted vehicular crowdsensing system has brought significant benefits to Intelligent Transportation Systems (ITS). To satisfy the requirements of ITS applications, a substantial number of sensed data are generated continuously, which incurs considerable communication and computation delays. In addition, with fast-moving vehicles and the sheer amount of data, data loss becomes an issue during the process of data uploading. To deal with these challenges, this paper aims to propose a cost-and-quality aware data collection scheme for edge-assisted vehicular crowdsensing system, so as to satisfy the accuracy and timeliness requirements of ITS applications without overloading the network. Particularly, we design a functional architecture for edge-assisted vehicular crowdsensing system, where vehicular sensing, data uploading and sensing parameter adjustment interwork to enable ITS applications. Based on the architecture, two effective algorithms are proposed, which are adaptive clustering and online sensing parameter adjustment. Adaptive clustering algorithm can support reliable and timely data uploading by taking into account the cluster stability and the communication reliability during the process of clustering vehicles. Online sensing parameter adjustment algorithm can guide the actions of intelligent vehicles based on the real-time traffic condition, which can achieve a tradeoff between sensing accuracy and communication overhead. In order to evaluate the performance of our scheme, we develop a simulation platform in which all the components in the system are built. By conducting extensive simulation, the superiority of the proposed scheme is validated. [ABSTRACT FROM AUTHOR]

A challenge for rescue teams when fighting against wildfire in remote areas is the lack of information, such as the size and images of fire areas. As such, live streaming from Unmanned Aerial Vehicles (UAVs), capturing videos of dynamic fire areas, is crucial for firefighter commanders in any location to monitor the fire situation with quick response. The 5G network is a promising wireless technology to support such scenarios. In this paper, we consider a UAV-to-UAV (U2U) communication scenario, where a UAV at a high altitude acts as a mobile base station (UAV-BS) to stream videos from other flying UAV-users (UAV-UEs) through the uplink. Due to the mobility of the UAV-BS and UAV-UEs, it is important to determine the optimal movements and transmission powers for UAV-BSs and UAV-UEs in real-time, so as to maximize the data rate of video transmission with smoothness and low latency, while mitigating the interference according to the dynamics in fire areas and wireless channel conditions. In this paper, we co-design the video resolution, the movement, and the power control of UAV-BS and UAV-UEs to maximize the Quality of Experience (QoE) of real-time video streaming. We applied the Deep Q-Network (DQN) and Actor-Critic (AC) to maximize the QoE of video transmission from all UAV-UEs to a single UAV-BS to learn the dynamic fire areas and communication environment. Simulation results show the effectiveness of our proposed algorithm in terms of the QoE, delay and video smoothness compared to the Greedy algorithm. [ABSTRACT FROM AUTHOR]

Ultra-reliable low-latency communication (uRLLC) emerges as one of the three service categories in fifth-generation (5G) wireless networks, with a potential to support multiple advanced services for latency-sensitive communications. The inherent openness of the wireless environment makes security in 5G unprecedentedly urgent to be addressed. Accordingly, the physical layer security (PLS) technique exploits the randomness characteristics of the wireless channel to fulfill security and confidentiality of information transmission. Different from many current works, which ignored the significance of security in uRLLC, this paper studies secrecy improvement strategy design in uRLLC scenarios. First, we propose suitable performance metrics for evaluating PLS of uRLLC and make a compromise between reliability, latency, and security, while maximizing the network throughput. Next, we identify the key challenging issues for achieving PLS in the context of uRLLC, and discuss the role of cooperative communication and mobility in terms of possible solutions. To the best of our knowledge, this is the first paper to make a trade-off among reliability, security and network throughput. Simulations validate the theoretical analyses. [ABSTRACT FROM AUTHOR]

With the development of 6G wireless communication technologies, various resource-intensive and delay-sensitive vehicle application tasks are generated. These application tasks can be offloaded to Mobile Edge Computing (MEC) which deploys computing resources at the edge of networks. Besides, the recent proposed Cybertwin, as the digital representation of the complicated physical end-systems, can help the terminals obtain the required services from networks. Vehicles enabled by Cybertwin can offload their tasks to MEC and achieve better performance. In this paper, we focus on the study of a hybrid energy-powered multi-server MEC system with Cybertwin. Vehicles enabled by Cybertwin and edge servers send the current network status and unprocessed vehicle application tasks to the macro base station (MBS) to achieve the better allocation of resources. Energy harvesting (EH) devices are deployed on edge servers to form a “green energy-grid” hybrid energy supply model. We formulate a stochastic offloading optimization problem, and the goal is to minimize the system cost. The stochastic optimization problem is decomposed into three sub-problems. Then, we design an efficient multi-vehicle task offloading (EMT) algorithm to achieve the trade-off between system cost and task queue length. Theoretical analysis shows that EMT algorithm can optimize the total cost of the MEC system and guarantee the system performance. According to experimental evaluation, we verify the performance of the EMT algorithm. [ABSTRACT FROM AUTHOR]

A zero forcing (ZF) assisted single layer beamforming is developed for spatial modulation (SM) and space shift keying (SSK) modulation multiple input multiple output (MIMO) systems, termed as SZF-SM/SZF-SSK, to achieve both transmit beamforming and receive diversity gain in this paper. Specifically, the proposed SZF-SM/SZF-SSK maps part of the bits to spatial domain, simultaneously forming a ZF precoding assisted single layer data both in multiple input single output (MISO) and MIMO systems, that differs greatly from conventional ZF beamforming assisted MIMO systems. With MISO configuration, the ZF precoder of SZF-SM/SZF-SSK converges to the maximum ratio transmission (MRT), achieving only the transmit beamforming gain. With MIMO configuration, SZF-SM/SZF-SSK tranceiver could be further modified along with the MIMO channel to achieve single layer transmission and joint detection, attaining both the transmit beamforming and receive diversity gain in SM-MIMO system. The theoretical analysis and the simulation results of the proposed SZF-SM/SZF-SSK with MISO and MIMO configurations are presented in this paper respectively, which show an attractive performance gain over the conventional SM/SSK. Compared with the conventional ZF beamforming assisted MIMO systems and some other CSI assisted SM-MIMO schemes, the proposed scheme exhibits better performance, especially under highly correlated MIMO channels, rendering a new and practical way to achieve low rank MIMO transmission with good performance and high data rate. [ABSTRACT FROM AUTHOR]

Transmissibility is a mathematical model that relates a subset of a system’s outputs to another subset of outputs of the same system without knowledge of the external excitation or the dynamics of the system. This study investigates fault detection, localization, and mitigation of connected autonomous vehicles (CAV) platoons using transmissibility operators. A CAV platoon is a network of connected autonomous vehicles that communicate together to move in a specific path with the desired velocity. Failure in a physical component of a vehicle, or failure in the form of an internal delay, a cyber-attack, or a communication time-delay affects the safety and security of the CAV platoons. In this paper, we use measurements from sensors available in CAV platoons to identify transmissibility operators, which are used for health monitoring, fault localization, and fault mitigation in the platoon. We first consider the case of vehicle-to-cloud communication (V2C) to monitor the platoon’s health. Then, we assume that the platoon loses communication with the cloud, and we monitor the health of the platoon based on vehicle-to-vehicle (V2V) communication. We apply the proposed technique to a model of the platoon obtained using the bond graph approach, and an experimental setup consisting of three connected autonomous robots. [ABSTRACT FROM AUTHOR]

Sudden torque change caused by the TM (traction motor) of HEV (hybrid electric vehicle) can lead to severe driveline oscillation, furthermore the backlash between engaging components in the driveline could increase the vibration amplitude. This paper focuses on the driveline oscillation of a series-parallel hybrid vehicle on launch condition. An analytic model of driveline torsional vibration considering disturbance brought by nonlinear backlash is established and simplified for the convenience of controller design. Based on the simplified model, a feedforward control strategy is proposed to suppress HEV launch vibration. However, the control effect will deteriorate sharply with the increase of equivalent backlash in the driveline system. To improve vibration attenuation performance, a feedback compensator is established and incorporated into the control system, with a robust sliding mode observer which is designed to estimate unknown disturbances as feedback signal. Numerical simulation is applied to validate vibration attenuation performance of the feedforward-feedback hybrid control strategy. Simulation results demonstrate that the feedforward-feedback control strategy has better robustness to cancel the unwanted effects of disturbance brought by nonlinear backlash compared with other control methods, which can be regarded as an effective approach to attenuate driveline oscillation and thus improve ride comfort on launch condition. [ABSTRACT FROM AUTHOR]

Dynamic wireless charging (DWC) is one of the most promising and economical power supply solutions for electric vehicles (EVs), which can address range anxiety and downsize the volume of onboard batteries. To solve the null power phenomenon and transfer power pulsation phenomenon in DWC scheme, this paper proposes a new magnetic coupler array to implement continuous charging and reduce the output power variation. The transmitters are placed with a proper interval to eliminate the adjacent coupling and enlarge the coil array’s effective length. ANSYS Maxwell is utilized to demonstrate the finite element analysis (FEA) of the DWC system. A larger receiver coil is designed to significantly mitigate power pulsation and increase the magnetic coupling as the receiver moves along the array. With the well-designed receiver coil, the output power pulsation is $\pm$ 1.52 $\%$. For the high-power capacity scenario, a multilevel connected DC-DC converter is designed on the receiver side to regulate the transfer power. Besides, a passivity-based control (PBC) is developed for the receiving DC-DC converter based on the Euler-Lagrange (EL) model, and a current sharing compensator is employed to balance each phase inductor current. Finally, a 500- $\text{W}$ prototype is constructed, and experimental results are represented to validate the proposed optimized coil array and the proposed PBC method. [ABSTRACT FROM AUTHOR]

In this paper, the problem of decision making for autonomous vehicles changing lanes is addressed by formulating multiple games in normal form for pairs of agents. This formulation generates the optimal action for the Ego vehicle at a given state and does not consider global optimality for all agents. The payoff matrices of the games are designed based on a user-defined set of rules. The constant parameters of these payoffs are then adjusted using neural learning to generate optimal behavior among the vehicles. An algorithm integrating deep reinforcement learning and game theory, regarded as Nash Q-learning, is included in the decision-making scheme. The applicability of the proposed method in a lane-changing scenario is tested via simulation. [ABSTRACT FROM AUTHOR]

The available road grade information makes a significant impact on improving the quality of vehicle control. In order to solve the limited application scenario and insufficient accuracy of current road grade estimation methods, this paper presents a novel model-based road grade estimation approach. First, a Quaternion Unscented Kalman Filter (QUKF) using the three-axle angular velocities and three-axle accelerations from a low-cost Inertial Measurement Unit (IMU) and the vehicle speed from CAN bus is designed to estimate the pitch angle of the vehicle. In particular, the measurement noise of UKF is analyzed by integrating Allan variance method. Second, a simplified vehicle-road model is derived to represent the road grade with the estimated pitch angle and longitudinal acceleration. Then, the performance of the proposed algorithm is tested by co-simulation of MATLAB/Simulink and CarSim, which indicates that the error rate of estimation is within 4%. Finally, the feasibility and accuracy of the proposed method implemented in the embedded prototype are verified in experiments conducted on standard slopes. [ABSTRACT FROM AUTHOR]

Robust and adaptive vehicle state estimation and tracking algorithms have become a very important part within the autonomous driving field. The family of interacting multiple model (IMM) filters has shown to provide very effective and accurate state estimation in systems whose behavior patterns change significantly over time. The main reason for the improved performance of IMM filters compared to single model approaches is the mode mixing, which constantly aligns the different models. This paper proposes an innovative way for the mode mixing, when the state-vectors of the models are of different size. The proposed mixing approach consists of two parts: firstly mixing the common states and secondly weighting between original and mixed states based on the model probabilities. Results from artificial simulations and real world measurement setups are shown to demonstrate the validity of the approach. Compared to previously suggested solutions, the proposed approach is more general and the overall complexity of the mode mixing step is reduced, which is the main contribution of the presented paper. [ABSTRACT FROM AUTHOR]

Traffic awareness is the prerequisite of autonomous driving. Given the limitation of on-board sensors (e.g., precision and price), remote measurement from either infrastructure or other vehicles can improve traffic safety. However, the wireless communication carrying the measurement result undergoes fading, noise and interference and has a certain probability of outage. When the communication fails, the vehicle state can only be predicted by Bayesian filtering with a low precision. Higher communication resource utilization (e.g., transmission power) reduces the outage probability and hence results in an improved estimation precision. The power control subject to an estimate variance constraint is a difficult problem due to the complicated mapping from transmit power to vehicle-state estimate variance. In this paper, we develop an estimator consisting of several Kalman filters (KFs) or extended Kalman filters (EKFs) and an interacting multiple model (IMM) to estimate and predict the vehicle state. We propose to apply deep reinforcement learning (DRL) for the transmit power optimization. In particular, we consider an intersection and a lane-changing scenario and apply proximal policy optimization (PPO) and soft actor-critic (SAC) to train the DRL model. Testing results show satisfactory power control strategies confining estimate variances below given threshold. SAC achieves higher performance compared to PPO. [ABSTRACT FROM AUTHOR]

In this paper, we give performance analysis in an unmanned-aerial-vehicle (UAV) assisted ground short-packet communication system, which adopts a UAV as a wireless relay to accomplish ultra-reliable and low-latency communications (URLLC) between a base station and a user equipment (UE). Both large-scale fading, which is modeled as the free space path loss or the classic 3-Dimensional channel, and small-scale fading, which follows Nakagami- $m$ distribution, are considered in our system model. We first derive the closed-form expressions for the average overall block error ratio (BLER) at the UE by adopting Gasssian-Legendre quadrature (GLQ) method. Then, under the reliability and latency constraints at the UE, we apply both bisection search and one-dimensional search algorithms to jointly optimize blocklength, transmit power and UAV flying height for maximizing the energy-efficiency (EE) of UAV. Finally, Monte-Carlo simulations are provided to verify our analytical results. [ABSTRACT FROM AUTHOR]

Maglev train is a new vehicle of rail transit. The time-varying load caused by large capacity is an important test of suspension system. If the state variable matrix and parameter matrix caused by time-varying mass cannot be estimated effectively, it is very likely to lead to the decline of suspension performance and instability. Firstly, an NNs state observer based on neural network is proposed to effectively estimate the system state and parameter matrix. Secondly, based on the NNs observer model, an inverse control algorithm with stronger regulation ability is designed by integrating the output limited characteristics and Lyapunov function, which can ensure the stability of the system when disturbed. The simulation results show that the control algorithm proposed in this paper can have faster response speed and stability in the flotation process of 16mm-10mm, and has better anti-disturbance ability in the face of time-varying mass disturbance. Finally, based on the single point suspension experimental platform, the effectiveness and practicability of the proposed control algorithm are verified, and the correctness of the proposed NNs observer model is also verified. [ABSTRACT FROM AUTHOR]

The user-centric cooperative transmission provides a compelling way to alleviate frequent handovers caused by an ever-increasing number of randomly deployed base stations (BSs) in ultra-dense networks (UDNs). This paper proposes a new user-centric cooperative transmissions-based handover scheme, i.e., the group-cell handover (GCHO) scheme, with the aim of reducing the handover rate in UDNs. In the proposed scheme, the boundary of the cooperating cluster depends on the distance among the user equipment (UE) and cooperating BSs. The new scheme captures the dynamicity and irregularity of the cooperating cluster topology resulting from randomly distributed BSs. Based on stochastic-geometry tools where BSs locations are modeled as the Poison point process (PPP), we derive an analytical expression of the handover rate for the UE with an arbitrary movement trajectory. Furthermore, a GCHO skipping (GCHO-S) scheme is proposed to minimize the handover cost, i.e., the percentage of time wasted in handover signaling in user-centric cooperative transmissions scenarios. The numerical results show that the GCHO scheme decreases the handover rate by 42.3% and 72.7% compared with the traditional single BS association and fixed-region cooperative network topology handover approaches, respectively. Moreover, under the same group-cell size and constant velocity, the GCHO-S scheme diminishes the handover cost by 50% against the GCHO scheme. [ABSTRACT FROM AUTHOR]

Satellite and unmanned aerial vehicle (UAV) networks have been introduced as enhanced approaches to provide dynamic control, massive connections and global coverage for future wireless communication systems. This paper considers a coordinated satellite-UAV communication system, where the UAV performs the environmental reconnaissance task with the assistance of satellites in a hostile jamming environment. To fulfill this task, the UAV needs to realize autonomous trajectory control and upload the collected data to the satellite. With the aid of the uploading data, the satellite builds the environment situation map integrating the beam quality, jamming status, and traffic distribution. Accordingly, we propose a closed-loop anti-jamming dynamic trajectory optimization approach, which is divided into three stages. Firstly, a coarse trajectory planning is made according to the limited prior information and preset points. Secondly, the flight control between two adjacent preset points is formulated as a Markov decision process, and reinforcement learning (RL) based automatic flying control algorithms are proposed to explore the unknown hostile environment and realize autonomous and precise trajectory control. Thirdly, based on the collected data during the UAV’s flight, the satellite utilizes an environment situation estimating algorithm to build an environment situation map, which is used to reselect the preset points for the first stage and provide better initialization for the RL process in the second stage. Simulation results verify the validity and superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

The problem of sensor fault estimation has been studied for Unmanned Surface Vehicle (USV) systems in networked environment. Assuming that the process noise is related to the measurement noise and packet loss will occur during the measurement transmission. We use an event-trigger mechanism (ETM) to save communication resources. Accordingly, an observer is proposed to estimate the state and sensor fault, and the trigger threshold is associated with the correlation coefficient. By adjusting the threshold, the observer performance and energy consumption can be balanced in an ideal situation. The observer designed in this paper reduces the effects of correlation noises effectively and good handling network packet losses, and it has been proved that the expected covariance of the estimation error is bounded. Finally, the simulation experiment on the identified model of USV in the laboratory is performed to validate the relevant conclusions. [ABSTRACT FROM AUTHOR]

Range-based positioning using wireless signal has gained remarkable attention in past decades. Accurately extracting the distance information, i.e., estimating parameters of individual multipath, is of interest. It has been shown that diffuse multipath component (DMC) in wireless propagation channel has significant influence on channel estimation and, thus, the ranging performance. In addition to specular component (SC), DMC should be considered in order to improve the estimated accuracy of channel paths’ parameters and, thus, the ranging performance. In this paper, we extend the quasi-maximum likelihood space-alternating generalized expectation-maximization (SAGE) algorithm by incorporating an autoregressive moving average (ARMA) filter to estimate the channel parameters under the presence of DMC. The parameters of both DMC and SC are estimated in an iterative approach, where the DMC is assumed as colored noise and the ARMA-filter is used to estimate the contributions from DMC. The validation of the algorithm based on measured data shows that both SC and DMC can be accurately estimated by the proposed approach. A comparison with standard SAGE and AR-SAGE is conducted, where the results reveal that the proposed method outperforms other two approaches in terms of ranging accuracy. [ABSTRACT FROM AUTHOR]

Noncoherent two- and three-phase two-way relaying (TWR) techniques are bandwidth efficient and entail no pilot symbol–related rate loss. However, noncoherent two-phase TWR (2P-TWR) has not been compared with three-phase TWR (3P-TWR), and several unanswered questions remain. In this paper, we focus on the noncoherent detection of the relay with the decode-and-forward technique. For the noncoherent maximum-likelihood detector at the relay, we propose a distance measure for the transmitted signals of the two terminal nodes. For 2P-TWR, we propose that the signal constellation of one terminal node is the signal constellation of the other terminal node rotated by an angle. Using the proposed distance measure, we obtain the optimal angles and thus resolve those unanswered questions pertaining to 2P-TWR. Finally, noncoherent 3P-TWR was found to significantly outperform noncoherent 2P-TWR in simulations and distance analyses. [ABSTRACT FROM AUTHOR]

In this paper, we design receivers for filter bank multicarrier-based (FBMC-based) massive MIMO considering practical aspects such as channel estimation and equalization. In particular, we propose a spectrally efficient pilot structure and a channel estimation technique in the uplink to jointly estimate all the users’ channel impulse responses. We mathematically analyze our proposed channel estimator and find the statistics of the channel estimation errors. These statistics are incorporated into our proposed equalizers to deal with the imperfect channel state information (CSI) effect. We revisit the channel equalization problem for FBMC-based massive MIMO, address the shortcomings of the existing equalizers in the literature, and make them more applicable to practical scenarios. The proposed receiver in this paper consists of two stages. In the first stage, a linear combining of the received signals at the base station (BS) antennas provides a coarse channel equalization and removes any multiuser interference. In the second stage, a per subcarrier fractionally spaced equalizer (FSE) takes care of any residual distortion of the channel for the user of interest. We propose an FSE design based on the equivalent channel at the linear combiner output. This enables the applicability of our proposed technique to small and/or distributed antenna setups such as cell-free massive MIMO. Finally, the efficacy of the proposed techniques is corroborated through numerical analysis. [ABSTRACT FROM AUTHOR]

Applying non-orthogonal multiple access (NOMA) in millimeter-wave (mmWave) networks has the potential to significantly enhance the spectral efficiency. In this paper, we propose an opportunistic beam-splitting NOMA scheme to fully exploit the antenna array gain in downlink mmWave networks, where single- and double-beam NOMA transmissions are dynamically enabled according to the spatial angle difference between the paired NOMA users, thereby enhancing the flexibility of applying NOMA in mmWave networks. The base station with a single radio frequency chain implements the double-beam NOMA transmission by adopting the beam-splitting technique. We develop a unified theoretical performance analysis framework for the proposed scheme, while taking into account the spatially random users, link blockage model, and general distance-based user pairing strategy. By using tools from stochastic geometry, we derive the exact expressions of the coverage probability and the sum rate of the proposed scheme under both the LoS exponential and ball models. We further derive the corresponding upper bound to simplify the analytical expressions. Simulations validate the theoretical analysis and demonstrate the performance gains of the proposed scheme in terms of the coverage probability and sum rate over the conventional single-beam NOMA and orthogonal multiple access (OMA) schemes. [ABSTRACT FROM AUTHOR]

In this paper, we present unified performance analysis framework of distributed transmit beamforming (DTB) with residual phase errors which arise from various sources such as imperfect synchronization and channel feedback quantization. More specifically, we derive statistics of the receive signal-to-noise ratio of DTB in Rayleigh and Rician fading channels in the presence of residual phase errors. In the case of residual phase errors caused by channel feedback quantization, we show that the distribution of residual phase errors can be modeled by uniform distribution in Rayleigh fading channels and Gaussian distribution in Rician fading channels. Also, our results reveal that a small number of bits are sufficient for quantized feedback to achieve comparable performance to DTB with perfect channel feedback. Finally, capitalizing on the derived statistics, we present closed-form expressions for the outage probability and the ergodic capacity of DTB with quantized channel feedback. [ABSTRACT FROM AUTHOR]

The large-scale application of electric taxis (ETs) in various regions has aroused great interest in developing high-efficiency charging solutions. ETs and charging stations (CSs) can send information to the information management center for real-time information sharing with the technical support of the vehicle-to-grid network. This paper studies the charging problem of intelligent transportation system composed of CSs and networked ETs. Each taxi selects the CS with the lowest charging cost based on the charging price and regional hotspot information. The CS obtains the strategy of taxi and adjusts the charging price to maximize its revenues. We use a multi-leader and multi-follower Stackelberg game to model the interaction between the CSs and ETs. Finally, the hotspot thematic map is generated by using the taxi information of the global positioning system in one day. Simulation results show that the model can effectively reduce the operating cost of taxis and guarantee the revenue of CSs considering the regional hotspot information and reliability coefficient. [ABSTRACT FROM AUTHOR]

Automotive radars have faced to various jamming signals with different types and parameters. One counter measure is to identify the fine-grained modulation types of these jamming signals, which can provide supplementary information for intercepting. Most existing signal modulation recognition methods attempt to establish a machine-learning mechanism by training with a large number of annotated samples, which is hardly applicable in real-world electronic reconnaissance scenarios where only a few samples can be intercepted in advance. Few-Shot Learning aims to learn from base classes with many samples and infuse this knowledge to support classes with only a few samples, thus realizing model generalization. In this paper, inspired by the fact that the energy distribution of the signal of interest is always concentrated in time-frequency images, a novel few-shot learning framework based on foreground segmentation is proposed, which benefits from its powerful filtering to remove the noise and clutter from background. The experimental results show that the proposed method can achieve excellent performance for fine-grained signal modulation recognition even with only one support sample and is robust under different signal-to-noise-ratio conditions. [ABSTRACT FROM AUTHOR]

In this study, we investigate the operation of a user-number threshold-based base station (BS) on/off control, in which the BS turns off when the number of users is less than a specific threshold value. This paper presents a space-based analysis of the BS on/off control system to which a stochastic geometric approach is applied. In particular, we derive the approximated closed-form expression of the coverage probability of a homogeneous network (HomNet) with the user-number threshold-based BS on/off control. Moreover, the optimal user-number threshold for maximizing coverage probability is analytically derived. In addition to HomNet, we also derive the approximated closed-form expression of the coverage probability and the optimal user-number thresholds for a heterogeneous network (HetNet). The results show that HetNet, the analysis of which seems intractable, can be analyzed in the form of a linear combination of HomNets with weighted densities. In addition, the optimal user-number threshold of each tier is obtained independently from other tiers. The modeling and analysis presented in this paper are not only limited to the case of user-number threshold-based on/off control, but also applicable to other novel on/off controls with minor modifications. Finally, by comparing the analytical results with the simulated results, the theoretical contributions of this study are validated. [ABSTRACT FROM AUTHOR]

To mitigate the impact of the dramatic growth of wireless video traffic and provide users with heterogeneous video services, a collaborative caching-transmission method in cache-enabled terahertz (THz) heterogeneous networks is studied in this paper. We propose a novel scalable video coding (SVC)-based two-tier cooperative caching strategy that can provide video services with different perceptual qualities. In addition, we propose a hierarchical hybrid transmission scheme where users obtain the requested layer files through the cooperative cellular multicast-D2D and THz transmissions. A base layer-based multicast group aggregation process is presented to improve resource utilization, while an efficient multicast transmission resource scheduling scheme can make better use of the spatial reusability. We derive the expressions for the average offloading capacity gain and average energy consumption, and formulate an efficiency function optimization problem. The problem is efficiently solved by the greedy strategy-gradient descent based caching solution and long tail distribution based heuristic caching solution. Simulation results validate the two solutions and demonstrate that the proposed caching-transmission method can achieve quasi-optimal performance compared with other contrasting schemes. [ABSTRACT FROM AUTHOR]

With the explosive growth of vehicle applications, vehicular networks based on millimeter wave (mmWave) bands have attracted interests from both academia and industry. mmWave communications are able to utilize the huge available bandwidth to provide multiple Gbps transmission rates among vehicles. In this paper, we address the content distribution scheduling problem in mmWave vehicular networks. It has been challenging for all vehicles in the same network to complete content downloading due to the limited communication resources of roadside units (RSUs) and the high mobility of vehicles. We propose a joint vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) scheduling scheme to minimize the total number of content distribution time slots from a global optimization perspective. In the V2I phase, the RSU serially transmits integrity content to vehicles, which are selected according to the vehicular network topology and transmission scheduling scheme. In the V2V phase, full-duplex communications and concurrent transmissions are exploited to achieve content sharing between vehicles and improve transmission efficiency. Performance evaluations demonstrate that our proposed scheme reduces the number of time slots and significantly improves system throughput when compared with other schemes, especially under large-size file transfers and a large number of vehicles. [ABSTRACT FROM AUTHOR]

In this paper, we propose an adaptive vehicle clustering algorithm based on fuzzy C-means algorithm, which aims at minimizing power consumption of the vehicles. Specifically, the proposed algorithm firstly dynamically allocates the computing resources of each virtual machine in the vehicle, according to the popularity of different virtualized network functions. The optimal clustering number to minimize the total energy consumption of vehicles is determined using the fuzzy C-means algorithm and the clustering head is selected based on vehicles moving direction, weighted mobility, and entropy. Simulation results are provided to confirm that the proposed algorithm can decrease the power consumption of vehicles while satisfying the vehicle delay requirement. [ABSTRACT FROM AUTHOR]

Explorations on the Lunar Far-side Surface (LFS) have recently developed rapidly with various landers, cruisers, and orbital space vehicles. Research on relay satellites with Earth-Moon Libration Point (EMLP) orbits has aroused widespread interest in supporting these probe missions, i.e., “Queqiao” spacecraft, due to their particular relative positions in the Earth-Lunar system. For example, a Halo orbital relay satellite at the Earth-Moon L2 point can provide more transmission opportunities for communication nodes on LFS to the destination nodes on the Earth. However, a single Halo orbital spacecraft could not provide all-time connections for the cislunar communication with its limited coverage on the LFS. In this paper, we proposed a dual-relay Halo orbit constellation for the cislunar communication network, providing high-proportion coverage on the LFS at any time using an analytical method of the grid point. Based on the proposed constellation, we designed an energy efficiency-optimal relay selection algorithm for the nodes on the LFS to return data back to the Earth by solving a MIP (Mixed-Integer-Programming) problem with collaborative considerations of source and relay node’s power allocation. The simulation results demonstrate that the proposed algorithm has the maximum energy efficiency (EE) while ensuring the user’s quality of service, compared with the algorithm of Spectrum Efficiency Maximization (SEM). [ABSTRACT FROM AUTHOR]

This paper develops a novel homography-based visual servo control method for the vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV) tracking the trajectory of a 6 degrees of freedom (6-DOF) moving ship. Different from the classical homography-based visual servoing which is only suitable for the trajectory tracking of a planar moving target, an extended homography-based visual servoing framework is proposed to track the trajectory of a 6-DOF moving target regardless of its time-varying rotational motion. Taking entries in the homography matrix as feedback, the visual dynamics of UAV is established and decoupled into a translational motion sub-dynamics and an attitude sub-dynamics based on the hierarchical control strategy. In the translational motion subsystem, the robust controller is designed by embedding the nonlinear differentiator, the adaptive method, and a smooth saturation model in the backstepping control scheme. The saturation model is introduced to generate the constrained control inputs to ensure the nonsingular attitude extraction and help the visual points remain in the camera’s field of view (FoV) simultaneously. In the attitude subsystem, a robust state-constrained control method is proposed by utilizing a robust control barrier function (RCBF), the quadratic programming (QP) and a saturation model to guarantee the visibility, where RCBF can accommodate unknown dynamics. The theoretical analysis demonstrates the asymptotic stability of the closed-loop system. Simulations are carried out to further validate the controller performance. [ABSTRACT FROM AUTHOR]

This paper investigates the fundamentals of motion planning for minimizing motion sickness in transportation systems of higher automation levels. The optimum velocity profile is sought for a predefined road path from a specific starting point to a final one within specific and given boundaries and constraints in order to minimize the motion sickness and the journey time. An empirical approach based on British standard is used to evaluate motion sickness. The trade-off between minimizing motion sickness and journey time is investigated through multi-objective optimization by altering the weighting factors. The correlation between sickness and journey time is represented as a Pareto front because of their conflicting relation. The compromise between the two components is quantified along the curve, while the severity of the sickness is determined using frequency analysis. In addition, three case studies are developed to investigate the effect of driving style, vehicle speed, and road width, which can be considered among the main factors affecting motion sickness. According to the results, the driving style has higher impact on both motion sickness and journey time compared to the vehicle speed and the road width. The benefit of higher vehicle speed gives shorter journey time while maintaining relatively lower illness rating compared with lower vehicle speed. The effect of the road width is negligible on both sickness and journey time when travelling on a longer road. The results pave the path for the development of vehicular technologies to implement for real-world driving from the outcomes of this paper. [ABSTRACT FROM AUTHOR]

Inter-satellite links (ISLs) are adopted in global navigation satellite systems (GNSSs) for high-precision orbit determination and space-based end-to-end telemetry telecommand control and communications. Due to limited onboard ISL terminals, the polling time division duplex (PTDD) mechanism is usually proposed for space link layer networking. By extending the polling mechanism to ground-satellite links (GSLs), a unified management system of the space segment and the ground segment can be realized. However, under the polling system how to jointly design the topology of ISLs and GSLs that are both time-slotted to improve data interaction has not been studied. In this paper, we formulate the topology design problem as an integer linear programming problem, aiming at minimizing the average delay of data delivery from satellites to ground stations while satisfying the ranging requirement for orbit determination. To tackle the computational complexity problem, we first present a novel modeling method of delay to reduce the number of decision variables. Furthermore, we propose a more efficient heuristic based on maximum weight matching algorithms. Simulation results demonstrate the feasibility of the proposed methods for practical operation in GNSSs with time-slotted GSLs. Comparing the two methods, the heuristic can achieve similar performance with respect to average delay but with significantly less complexity. [ABSTRACT FROM AUTHOR]

Dq impedance measurement is of great importance as a part of the impedance-based analysis. At present, impedance of objects in single-phase vehicle-grid system are mainly obtained by injecting the voltage or current disturbance of one single frequency into the system circularly. However, as the number of measurement frequency points increases, there will produce much time-consuming of measurement. In this paper, a quick dq impedance measurement algorithm for single-phase vehicle-grid system based on multi-frequency sweep is proposed. First, pseudo-random binary sequences (PRBS) and ternary sequences are modified as the driving signals rather than directly injecting into the control link. Then three types of disturbance with specific frequency characteristics are introduced into the system and their parameters setting principle are given for the first time. According to the different characteristics of measurement objects, the proposed algorithms are also different to ensure the maximum specificity and sensitivity. Finally, for different objects in vehicle-grid system like traction network, single-phase converter and dual-interleaved converter, simulation and experimental results based on the hardware in the loop real-time simulation platform both show the correctness of the proposed algorithm, and the possible causes of errors are also analyzed in detail. Moreover, the proposed algorithm makes it possible to quickly measure the dq impedance of the single-phase vehicle-grid system, and the correctness of the theoretical and measured value can be judged and used for the stability analysis of electrical systems. [ABSTRACT FROM AUTHOR]

The Internet of Underwater Things (IoUT) gradually becomes the future direction of ocean. Many devices, which are called the sources, are deployed in the sea to fulfill scientific, civilian, and military needs. If the locations of these sources are leaked, it will cause a serious economic and military crisis. Thus, in this context, the source location privacy protection becomes critical. In this paper, we focus on the source location privacy protection of underwater acoustic sensor networks (UASNs), where a multi-channel interference based source location privacy protection scheme (MCISLP) is proposed. The MCISLP mainly contains four steps. First, to enhance the sense of autonomous underwater vehicles (AUV) for nodes, a noise reduction method is proposed. Then, based on the previous step, an AUV movement prediction method for nodes is presented to ensure the success of packet transmission. After that, for the information exchange among nodes, given the interference of multiple underwater channels, this interference is then utilized to protect the source location privacy. Further, to reduce the channel interference on the source packet, the time slot control is added to weaken the influence. Last, the ant colony algorithm is adopted for the data collection of AUV. The simulation results show that the MCISLP can increase the network safety time by up to 57 $\%$ (compared with data collection algorithms), and balance the energy consumption of nodes (for about 20 $\%$ energy saving), with a minor compromise of energy consumption of AUV and delay. [ABSTRACT FROM AUTHOR]

This paper explores the near-field regional target localization problem with the reconfigurable intelligent surface (RIS) assisted system. Traditional near-field localization strategies typically estimate the position of the target through line-of-sight (LOS) signals transmitted from the anchor node. In practice, accurate location estimation is a challenging issue when the LOS link between the anchor node and target may be unavailable due to the obstacle. To this end, this paper investigates the possibility to confirm the position of the target node consisted in an area of interest (AOI) by retrieving information from the RIS reflection signals. Specifically, we establish a general framework for RIS-assisted regional localization, which consists of RIS phase design and position determination. By defining the average localization accuracy (ALA) of the AOI, we first present a discretization method to design the RIS phase. Then, a robust phase design problem is formulated via transforming the AOI into an uncertainty model of position parameters, and an efficient iterative entropy regularization (IER)-based algorithm is proposed to solve it. Using the designed RIS phase, we develop a near-field target localization algorithm and discuss the power optimization problem for the RIS-assisted localization system (RALS). Numerical results demonstrate the effectiveness of the proposed framework, in which both phase design strategies almost coincide with the optimal RIS phase, and the proposed localization method can attain the near-optimal localization performance by applying the designed RIS phase schemes. [ABSTRACT FROM AUTHOR]

Channel allocation has a direct and profound impact on the performance of vehicle-to-everything (V2X) networks. Considering the dynamic nature of vehicular environments, it is appealing to devise a blended strategy to perform effective resource sharing. In this paper, we exploit deep learning techniques predict vehicles’ mobility patterns. Then we propose an architecture consisting of centralized decision making and distributed channel allocation to maximize the spectrum efficiency of all vehicles involved. To achieve this, we leverage two deep reinforcement learning techniques, namely deep Q-network (DQN) and advantage actor-critic (A2C) techniques. In addition, given the time varying nature of the user mobility, we further incorporate the long short-term memory (LSTM) into DQN and A2C techniques. The combined system tracks user mobility, varying demands and channel conditions and adapt resource allocation dynamically. We verify the performance of the proposed methods through extensive simulations and prove the effectiveness of the proposed LSTM-DQN and LSTM-A2C algorithms using real data obtained from California state transportation department. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) has recently risen as a promising paradigm to meet the increasing resource requirements of the terminal devices. Meanwhile, small cell network (SCN) with MEC has been emerging to handle the exponentially increasing data traffic and improve the network coverage, and is recognized as one key component of the next generation wireless networks. However, with the growing number of terminal devices requiring computation offloading to the edge servers, the network would be heavily congested and thus the performance would be degraded and unbalanced among multiple devices. In this paper, we propose the joint admission control and computation resource allocation in the MEC enabled SCN, and formulate it as a stochastic optimization problem. The goal is to maximize the system utility combining the throughput and fairness while bounding the queue. We decouple the original problem into three independent subproblems, which can be solved in a distributed manner without requiring the system statistical information. An admission control and computation resource allocation (ACCRA) algorithm is designed to obtain the optimal solutions of the subproblems. Theoretical analysis proves that the ACCRA algorithm can achieve the close-to-optimal system utility and reach the arbitrary tradeoff between the utility and the queue length. Experiments are conducted to validate the derived analytical results and evaluate the performance of the ACCRA algorithm. [ABSTRACT FROM AUTHOR]

Massive Machine-Type Communication (mMTC) is proposed by ITU to provide a large scale of connectivity services for billions of machine-type communications devices (MTCDs) via cellular networks. However, MTCDs are generally energy-constrained, security capability limited and short-packet transmission dominated. It has great challenges for massive MTCDs to perform short-packet transmission simultaneously while guaranteeing the confidentiality and energy efficiency of the information transmission. In this paper, we investigate the energy-efficient secure short-packet transmission of Non-Orthogonal Multiple Access (NOMA) assisted mMTC networks, in which MTCDs aim to transmit secrecy messages to the destination base station via trusted relays with a passive eavesdropper present. To realize secure, reliable and energy-efficient transmission, we maximize the secrecy energy efficiency (SEE) by implementing joint relay and power level selection. Furthermore, we formulate a decentralized stateless Q-learning-based multi-agent reinforcement learning (MARL) algorithm for the dynamic and complex mMTC systems. The simulation results show that adopting the proposed decentralized MARL algorithm and relaying with NOMA can improve the performance of SEE while reducing information exchange overhead. [ABSTRACT FROM AUTHOR]

Recently, vehicle-to-vehicle (V2V) based localization has attracted much attention due to its potential to achieve high accuracy. However, the error caused by non-line-of-sight (NLOS) propagation significantly affects the localization performance. In this paper, a novel method combining V2V communication and NLOS identification is proposed to improve accuracy of vehicle localization in NLOS scenarios. First, the algorithm identifies NLOS links with statistical methods. In this step, identification is conducted by decision theory or z-test, which depends on whether priori NLOS information is known or not. After that, NLOS links are discarded. Using the information including length of remaining links and GPS positions of surrounding vehicles, the estimated positions of target vehicles can be obtained by multilateration. It is shown by simulations that the proposed algorithm outperforms several classical methods in accuracy. Specifically, when priori NLOS information is available, 80% of vehicles have a localization error less than 5 m. For the case where NLOS information cannot be obtained, the algorithm still have good performance, and the ratio of vehicles having error within 5 m reaches 70%. [ABSTRACT FROM AUTHOR]

Free-space optics (FSO)-based, low-earth orbit (LEO) satellite-assisted Internet of vehicles (IoVs) has recently attracted research efforts worldwide. For the majority of Internet applications, transmission control protocol (TCP) is one of the most widely deployed protocols. Nevertheless, a complete understanding of the TCP performance over FSO-based satellite-assisted IoVs networks is still lacking in the literature. This is a highly challenging issue, as a consequence of the non-trivial interdependencies between transmission losses caused by the dynamics/uncertainty of atmospheric last-mile channels and the buffer overflows on the Internet, as well as their joint impact on the TCP congestion control mechanisms. This paper offers a comprehensive analytical framework that allows assessing the TCP throughput performance under the joint impact of congestion losses occurred in the Internet and transmission errors at the last-mile link from the satellite-to-vehicle. Internet access for emerging unmanned aerial vehicles (UAVs) is considered while the analysis can be also applied to other kinds of vehicles, which, in fact, require simpler channel models. The link-layer incremental redundancy hybrid automatic repeat request (IR-HARQ) protocol is employed at satellite-to-UAV channels to further improve the TCP throughput performance. Numerical results provide non-trivial findings and insights for the TCP performance in satellite/FSO-based IoVs, in which optimal UAV’s parameters are chosen to maximize the TCP throughput. [ABSTRACT FROM AUTHOR]

In this paper, empirical data obtained from the literature were used to develop models that capture the impact of the vehicle position in a platoon of homogeneous vehicles and the distance gap to its lead (and following) vehicle on its drag coefficient. These models are developed for light duty vehicles (LDVs), buses, and heavy duty trucks (HDTs). The models that are fit using a constrained optimization formulation are then used to extrapolate the empirical measurements to a wide range of vehicle distance gaps. The results show a significant reduction in the vehicle fuel consumption when compared with those based on a constant drag coefficient assumption. Specifically, considering a minimum time gap between vehicles of 0.5 seconds running at a speed of $100 \; km/hr$ , the fuel reduction that is achieved is $5 \%$ , $17 \%$ , and $12 \%$ for LDV, bus, and HDT platoons, respectively. For longer time gaps (gap $\approx 2$ seconds), the bus and HDT platoons still experience fuel reductions in the order of $8 \%$ , whereas LDVs incur negligible fuel savings. [ABSTRACT FROM AUTHOR]

This paper proposes a coordinated tracking controller for a tractor-trailer to keep a desired distance and heading angle relative to a combine harvester for the autonomous unloading of harvested crops in automatic agriculture operations. A coordinate transformation and the prescribed performance control (PPC) technique are employed to develop a second-order Euler-Lagrange formulation of the tracking errors. Then, a neural adaptive saturated velocity observer-based controller is proposed to force that the trailer center is exactly placed under the end point of the harvester unloading tube to collect cereal grains. By an effective application of the PPC technique and computing a safe motion boundary, controller non-singularity, collision avoidance and connectivity between two vehicles are preserved continuously. The model uncertainties including unknown vehicle parameters, variable trailer mass and moment of inertia during the crop collection, surface friction, climate and crop conditions and external disturbances are compensated by an effective combination of a neural network and an adaptive robust control law. Lyapunov’s direct method is employed to prove that the tracking errors are semi-globally uniformly ultimately bounded and converge to a neighborhood of the origin with a pre-specified maximum overshoot, convergence rate and steady-state accuracy. Finally, comparative simulation results are presented to demonstrate the effectiveness of the proposed controller for the crop harvesting operations in a practical perspective. [ABSTRACT FROM AUTHOR]

In practical propagation environments, different massive MIMO users can have correlated angles in spatial paths. In this paper, we study the effect of angle correlation on inter-user channel correlation via a combination of measurement and analysis. We show three key results. First, we collect a massive MIMO channel dataset for examining the inter-user channel correlation in a real-world propagation environment; the dataset is now open-access. We observed channel correlation higher than 0.48 for all close-by users. Additionally, over 30% of far-away users, even when they are tens of wavelengths apart, have inter-user channel correlation that is at least twice higher than the correlation in the i.i.d. Rayleigh fading channel. Second, we compute the inter-user channel correlation in closed-form as a function of inter-user angle correlation, the number of base-station antennas, and base-station inter-antenna spacing. Our analysis shows that inter-user angle correlation increases the inter-user channel correlation. Inter-user channel correlation reduces with a larger base-station array aperture, i.e., more antennas and larger inter-antenna spacing. Third, we explain the measurements with numerical experiments to show that inter-user angle correlation can result in significant inter-user channel correlation in practical massive MIMO channels. [ABSTRACT FROM AUTHOR]

Due to the scarcity in the wireless spectrum and limited energy resources especially in mobile applications, efficient resource allocation strategies are critical in wireless networks. Motivated by the recent advances in deep reinforcement learning (DRL), we address multi-agent DRL-based joint dynamic channel access and power control in a wireless interference network. We first propose a multi-agent DRL algorithm with centralized training (DRL-CT) to tackle the joint resource allocation problem. In this case, the training is performed at the central unit (CU) and after training, the users make autonomous decisions on their transmission strategies with only local information. We demonstrate that with limited information exchange and faster convergence, DRL-CT algorithm can achieve 90% of the performance achieved by the combination of weighted minimum mean square error (WMMSE) algorithm for power control and exhaustive search for dynamic channel access. In the second part of this paper, we consider distributed multi-agent DRL scenario in which each user conducts its own training and makes its decisions individually, acting as a DRL agent. Finally, as a compromise between centralized and fully distributed scenarios, we consider federated DRL (FDRL) to approach the performance of DRL-CT with the use of a central unit in training while limiting the information exchange and preserving privacy of the users in the wireless system. Via simulation results, we show that proposed learning frameworks lead to efficient adaptive channel access and power control policies in dynamic environments. [ABSTRACT FROM AUTHOR]

In this paper, we consider the orthogonal frequency division modulation (OFDM) based integrated radar-communication (Rad-Com) for automobile systems operating in millimeter wave band with imperfect time-frequency synchronization for communications. Since different pilot power allocation and placement strategies can result in different performance, we investigate the power allocation and placement design for the Rad-Com system. We analyze the effect of power allocation and pilot placement on the system performance. Based on the analysis, we provide, along with a channel estimation scheme, the pilot power allocation and placement designs that can optimize the radar and communication performance, respectively. Moreover, the tradeoff between radar and communication performance is discussed and a tradeoff design is presented. We conduct simulations to validate our analysis and evaluate the proposed designs. Results show that our analysis is valid and insightful and the proposed designs are effective. [ABSTRACT FROM AUTHOR]

In this paper, we investigate joint retrospective interference alignment (RIA) and caching schemes for multiple-input single-output (MISO) broadcast channels (BC) with delayed channel state information at the transmitter (CSIT). By exploiting the cached content, we propose one sophisticated cache-aided RIA scheme to cancel the interference. Importantly, our scheme takes the number of symbols in a packet into account, as different number of symbols in a packet result in different system configuration, which may have significant impact on the design of the transmission scheme and achievable degrees of freedom (DoF). Furthermore, we present two theorems to quantify the achievable sum DoF of the proposed cached-aided RIA scheme for different system configuration. Moreover, the system configuration of the existing cache-aided RIA scheme with mixed CSIT can be treated as a special case in our paper. And also our scheme can simultaneously support more users than the existing cache-aided RIA scheme with mixed CSIT to invoke zero forcing (ZF) precoding, which limits the number of users simultaneously supported. [ABSTRACT FROM AUTHOR]

Accurate and globally referenced global navigation satellite system (GNSS) based vehicular positioning can be achieved in outlier-free open areas. However, the performance of GNSS can be significantly degraded by outlier measurements, such as multipath effects and non-line-of-sight (NLOS) receptions arising from signal reflections of buildings. Inspired by the advantage of batch historical data in resisting outlier measurements, in this paper, we propose a graduated non-convexity factor graph optimization (FGO-GNC) to improve the GNSS positioning performance, where the impact of GNSS outliers is mitigated by estimating the optimal weightings of GNSS measurements. Different from the existing local solutions, the proposed FGO-GNC employs the non-convex Geman McClure (GM) function to globally estimate the weightings of GNSS measurements via a coarse-to-fine relaxation. The effectiveness of the proposed method is verified through several challenging datasets collected in urban canyons of Hong Kong using automobile level and low-cost smartphone level GNSS receivers. [ABSTRACT FROM AUTHOR]

Path planning is a critical part for improving the driving safety and driver comfort of autonomous vehicles (AVs), especially in complex maneuvering conditions. In addition, different drivers have different preferences for AVs, thus, how to provide personalized trajectories for different drivers is a vital issue for AVs. The collision-free path planning problem in conditions with large road curvatures is investigated in this paper, with the consideration of environmental safety constraints, drivers’ comfort, vehicle actuator constraints, etc. Firstly, a Driver-Vehicle-Road (DVR) system is established based on the combination of the kinematic vehicle model and the two-point visual preview driver model, such that the driver's individual handling characteristics can be considered in the controller. The kinematic vehicle model is modified to have the similar understeering characteristics with those of the nonlinear full car models, and then the proposed DVR system can satisfy different groups of drivers and cars. Secondly, for environmental constraints, a new artificial potential field (APF) method is proposed, which can form a banana-shaped 3-D dangerous imaginary mountain and a lane boundary cliff suitable for arbitrary curvature roads to generate a collision-free evasive path. Finally, the Linear-Time-Varying (LTV) model predictive control (MPC) method is adopted to design the path planner. The CarSim-Simulink joint simulation illustrates that with the proposed planner, the host vehicle is capable of avoiding obstacles with a safer and more comfortable maneuver on large curvature roads. And the proposed path planner can provide individually safe trajectories for different drivers with good maneuverability. [ABSTRACT FROM AUTHOR]

This paper aims to explore the performance potential of a novel dual-mode power-split hybrid powertrain. First, the steady-state power split characteristics for the proposed hybrid powertrain in different modes are analyzed. The mode switching strategy is developed to maximize powertrain efficiency, which is verified by using dynamic programming based on direct transmit points (DTPs). Moreover, a novel multi-objective evolutionary algorithm based on the decomposition (MOEA/D) method used in a nested way with dynamic programming is proposed to solve the multi-objective optimization of the PS-HEVs for the first time, and the computational efficiency and superiority of the proposed algorithm is compared with the commonly used NSGA-II algorithm. The results show that the MOEA/D based multi-objective optimization framework has similar performance in the search for the Pareto frontier but significantly higher computational efficiency than that of the NSGA-II algorithm. The obtained Pareto frontier provides optimal design candidates for hybrid powertrain systems. [ABSTRACT FROM AUTHOR]

In the field of advanced driver assistance systems (ADAS), effective learning of driver fatigue characteristics representation is a major challenge due to uncertainties of both real roads and drivers. To tackle this problem, this paper proposes a novel model of learning interpretable representations for fatigue features, so as to improve the performance of the monitoring system on real roads through the learned features. First, the approximate entropy of the Steering Wheel Angle (SWA)sequence is used to crop adaptive lengths of the Recurrent Neural Network (RNN) input data. Then, it will learn statistical indexes to discover the random steering characteristics. The Long Short-Term Memory (LSTM) unit can memorize drivers’ long-term operating characteristics and instantaneous change patterns, and mine their fatigue characteristics. Finally, the information gain method is used to discover the strong correlation between potential characteristics and fatigue levels, thereby obtaining the best feature representation for driver fatigue. The proposed method makes use of the advantages of recurrent neural network learning to explicitly capture various potential characteristics of fatigue driving and interactions between non-linear characteristics in driving. Experimental results on real driving data validate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]