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The accurate knowledge of tire–road friction coefficient (TRFC) contributes to the optimization of driver maneuvers for further improving the safety of intelligent vehicles. The performance of the existing estimation methods would decline when a vehicle performs complex driving maneuvers. In addition, the mass parameter mismatch also deteriorates the estimation accuracy of TRFC. To address these problems, in this article, an integrated scheme for TRFC estimation is proposed by combining a strong tracking unscented Kalman filter and an interactive multiple model unscented Kalman filter. Real-time experiments are implemented on a mass-produced vehicle to demonstrate the feasibility and effectiveness of the proposed method. The results show that the proposed approach has better estimation accuracy than the existing ones under various driving scenarios. [ABSTRACT FROM AUTHOR]

As one type of associative memory, content-addressable memory (CAM) has become a critical component in several applications, including caches, routers, and pattern matching. Compared with the conventional CAM that could only deliver a “matched or not-matched” result, emerging multilevel CAM (ML-CAM) is capable of delivering “the degree of match” with multilevel distance calculation. This feature has been desired in applications that need beyond-Boolean matching results. However, existing ML-CAM designs are limited by the bit-cell device discharging current mismatch and vulnerability to the timing of sensing operations for distance calculation. This inherent constraint makes it difficult to further improve the accuracy and scalability toward higher accuracy and higher dimension matching. In this work, we propose CapCAM, a multilevel Capacitive Content Addressable Memory. It could be implemented based on either static random-access memory (SRAM) or emerging technologies, e.g., the ferroelectric field-effect transistor (FeFET). CapCAM could provide linear and stable voltage drop scaled by the match degree and need no strict timing for result sensing, which embraces the high-accuracy and high-scalability search. The inherent enabler of CapCAM is the charge-domain computing mechanism. This article will present the basic concept, operating mechanisms, detailed circuit designs, and circuit-level simulations of CapCAM. Besides, we apply CapCAM to few-shot learning applications and compare CapCAM with the current-domain TCAM designs. Results show 99.2% accuracy for a five-way five-shot classification task with our proposed CapCAM design while considering 1-fF capacitors, 20-domain FeFETs, and 256 columns. In contrast, the prior work based on discharging dynamics requires strict timing controls and suffers from accuracy degradation under the same configuration, which demonstrates CapCAM’s capability of low-power, accurate, and scalable multilevel CAM (ML-CAM) computing. [ABSTRACT FROM AUTHOR]

Lane-changing is a critical issue for autonomous vehicles (AVs), especially in complex environments. In addition, different drivers have different handling preferences. How to provide personalized maneuvers for individual drivers to increase their trust is another issue for AVs. Therefore, a framework of human-like path planning is proposed in this paper, considering driver characteristics of visual-preview, subjective risk perception, and degree of aggressiveness. In the decision making module, a model is built to select the most suitable merging spot, with respect to safety factors and the driver’s degree of aggressiveness. And a novel environmental potential field (PF) suitable for arbitrary road structures is designed to describe the driver’s individual risk perception. In the trajectory planning module, a model predictive control (MPC) based path planner is designed according to the decisions in coincidence with the driver’s individual intentions of collision avoidance. Simulation results have demonstrated that the proposed path planner can provide with personalized trajectories for different combinations of driver preferences and steering characteristics, in scenarios of curved roads with different risks of collision. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic stable control protocol for heterogeneous vehicle platoon subject to communication topologies change, external disturbance, and information delay. First, a random vehicle platoon system composed entirely of several pure electric vehicles is built. The random variation of data transmission link among the platoon in a natural traffic environment is considered and molded by the Markov chain combined with the directed graph method. The influence of delays and discrete data in wireless communication, road slope, and air resistance on the vehicle platoon is also considered by introducing the external interferences and equivalent information delays. Additionally, to ensure the vehicle platoon’s inner-vehicle stability, the variable-gain distributed controller is proposed based on the Markovian jumping system stability theory and $ H_\infty$ control. Finally, the $ \mathcal {L}_{2}$ stochastic string stability is defined to attenuate perturbations as they propagate through the platoon. Simulation studies about a vehicle platoon under four communication topologies random switching with two different control methods are provided to verify the theoretical result. It is shown that, compared to traditional platoon robust control, it is possible to achieve the vehicle platoon’s stability even if in the continuous mutation of unstable topologies by using the proposed control approach. [ABSTRACT FROM AUTHOR]

The accurate information of sideslip angle (SA) and tire cornering stiffness (TCS) is essential for advanced chassis control systems. However, SA and TCS cannot be directly measured by in-vehicle sensors. Thus, it is a hot topic to estimate SA and TCS with only in-vehicle sensors by an effective estimation method. In this article, we propose a novel fuzzy adaptive robust cubature Kalman filter (FARCKF) to accurately estimate SA and TCS. The model parameters of the FARCKF are dynamically updated using recursive least squares. A Takagi–Sugeno fuzzy system is developed to dynamically adjust the process noise parameter in the FARCKF. Finally, the performance of FARCKF is demonstrated via both simulation and experimental tests. The test results indicate that the estimation accuracy of SA and TCS is higher than that of the existing methods. Specifically, the estimation accuracy of SA is at least improved by more than 48%, while the estimators of TCS are closer to the reference values. [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]

Onboard GNSS signal loss is a challenging issue for automated vehicles during driving in urban areas due to the effects of tall and dense buildings, flyovers, tunnels, and vegetation. To improve the vehicle localization accuracy when GNSS signals temporarily lost, this paper proposes a Geometry-based cooperative localization method (GCL) for the Internet of Vehicles. The vehicle position is estimated using mathematical geometry information, including vehicle dynamics and road shapes, which could reduce the environment-aware position constraint of cooperative localization. The relocation approach is enabled by communicating with neighboring vehicles to attenuate GNSS signal loss on localization-based services. The efficiency and scalability of GCL are evaluated in different driving conditions. The results showed that GCL achieved better localization performance than the state-of-the-art techniques in shorter and longer GNSS signal loss situations. The experimental results demonstrated the capabilities and effectiveness of the proposed algorithm to handle typical GNSS signal loss driving scenarios. [ABSTRACT FROM AUTHOR]

This article establishes a novel electroencephalograph (EEG)-based brain–computer interface (BCI) system for ground vehicle control with potential application of mobility assistance to the disabled. To enable an intuitive motor imagery (MI) paradigm of “left,” “right,” “push,” and “pull,” a driving simulator based EEG data recording and automatic labeling platform is built for dataset making. In the preprocessing stage, a wavelet and canonical correlation analysis (CCA) combined method is used for artifact removal and improving signal-to-noise ratio. An ensemble learning based training and testing framework is proposed for MI EEG data classification. The average classification accuracy of proposed framework is about 91.75%. This approach essentially takes advantage of the common spatial pattern (CSP) with ability of extracting the feature of event-related potentials and the convolutional neural networks (CNNs) with powerful capacity of feature learning and classification. To convert the classification results of EEG data segments into motion control signals of ground vehicle, shared control strategy is used to realize the control command of “left-steering,” “right-steering,” “acceleration,” and “stop” considering collision avoidance with obstacles detected by a single-line LIDAR. The online experimental results on a model vehicle platform validate the significant performance of the established BCI system and reveal the application potential of BCI on the vehicle control and automation. [ABSTRACT FROM AUTHOR]

Compute-in-memory (CiM) is a promising approach to alleviating the memory wall problem for domain-specific applications. Compared to current-domain CiM solutions, charge-domain CiM shows the opportunity for higher energy efficiency and resistance to device variations. However, the area occupation and standby leakage power of existing SRAM-based charge-domain CiM (CD-CiM) are high. This brief proposes the first concept and analysis of CD-CiM using nonvolatile memory (NVM) devices. The design implementation and performance evaluation are based on a proposed 2-transistor-1-capacitor (2T1C) CiM macro using ferroelectric field-effect-transistors (FeFETs), which is free from leakage power and much denser than the SRAM solution. With the supply voltage between 0.45V and 0.90V, operating frequency between 100MHz to 1.0GHz, binary neural network application simulations show over 47%, 60%, and 64% energy consumption reduction from existing SRAM-based CD-CiM, SRAM-based current-domain CiM, and RRAM-based current-domain CiM, respectively. For classifications in MNIST and CIFAR-10 data sets, the proposed FeFET-based CD-CiM achieves an accuracy over 95% and 80%, respectively. [ABSTRACT FROM AUTHOR]

Some vehicle state information that cannot be measured by in-vehicle sensors is quite important for the active safety control of intelligent vehicles. To obtain these key information in real-time, many advanced estimation algorithms are proposed. However, the existing studies focus on the effect of sensor measurement noise on estimation accuracy and rarely consider the impact of sensor data loss. In this article, a novel adaptive fault-tolerant extended Kalman filter is proposed to estimate vehicle state in case of partial loss of sensor data. The randomness of the data loss is first defined by a discreet distribution in interval [0,1]. Then, the fault-tolerant extended Kalman filter is derived based on a recursive filter framework. Furthermore, a fading factor on the basis of the orthogonal theory is used to improve the adaptability of fault-tolerant extended Kalman filter. Experimental results demonstrate that the estimation performance of the proposed approach is better than the extended Kalman filter. [ABSTRACT FROM AUTHOR]

Long queues of vehicles are often found at signalized intersections, which increases the energy consumption of all the vehicles involved. This paper proposes an enhanced eco-approach control (EEAC) strategy with consideration of the queue ahead for connected electric vehicles (EVs) at a signalized intersection. The discharge movement of the vehicle queue is predicted by an improved queue discharge prediction method (IQDP), which takes both vehicle and driver dynamics into account. Based on the prediction of the queue, the EEAC strategy is designed with a hierarchical framework: the upper-stage uses dynamic programming to find the general trend of the energy-efficient speed profile, which is followed by the lower-stage model predictive controller to computes the explicit solution for a short horizon with guaranteed safe inter-vehicular distance. Finally, numerical simulations are conducted to demonstrate the energy efficiency improvement of the EEAC strategy. Besides, the effects of the queue prediction accuracy on the performance of the EEAC strategy are also investigated. [ABSTRACT FROM AUTHOR]

Reconstitution of control architecture creates a great challenge for distributed drive electric vehicles (DDEV), due to the emergence of a new distributed driving strategy. To this end, a novel distributed control architecture is proposed in this paper for integrated control of active front steering (AFS) system and direct yaw moment (DYC) system. First, a multi-agent system (MAS) is employed to construct a general framework, where AFS and DYC act as agents that work together to improve vehicle lateral stability and simultaneously reduce workloads of drivers during path tracking. The cooperative control strategies of two agents are obtained through Pareto-optimality theory to ensure optimal control performance of AFS and DYC. Then, on the basis of dynamic interaction between agents, terminal constraints, including terminal cost function and terminal input with local static feedback, are designed to guarantee the asymptotic stability of the close-loop system. Finally, virtual simulations are conducted to evaluate the proposed controller. The results indicate that the proposed control architecture can effectively preserve vehicle stability and reduce workloads of drivers, especially for the inexperienced driver. Furthermore, the hardware-in-loop (HIL) test results also demonstrate the feasibility of the proposed controller. [ABSTRACT FROM AUTHOR]

Accurate knowledge of the vehicle states is the foundation of vehicle motion control. However, in real implementations, sensory signals are always corrupted by delays and noises. Network induced time-varying delays and measurement noises can be a hazard in the active safety of over-actuated electric vehicles (EVs). In this paper, a brain-inspired proprioceptive system based on state-of-the-art deep learning and data fusion technique is proposed to solve this problem in autonomous four-wheel actuated EVs. A deep recurrent neural network (RNN) is trained by the noisy and delayed measurement signals to make accurate predictions of the vehicle motion states. Then unscented Kalman predictor, which is the adaption of unscented Kalman filter in time-varying-delay situations, combines the predictions of the RNN and corrupted sensory signals to provide better perceptions of the locomotion. Simulations with a high-fidelity, CarSim, full-vehicle model are carried out to show the effectiveness of our RNN framework and the entire proprioceptive system. [ABSTRACT FROM AUTHOR]

Power-split hybrid technologies have been transformed from single mode to multimode to realize even greater savings in fuel and more powerful output. However, the optimization procedure of the mode shift map for multimode hybrid electric vehicles (HEVs) has not been formalized. In this paper, the types of mode shift are first classified as direct/indirect and conditional/unconditional. The optimal shift path with minimum energy variation for the indirect mode shift is formulated as a shortest path problem and solved by Dijkstra’s algorithm. By taking the energy loss during mode shift into consideration, the method for designing a mode shift map is presented from the perspective of the normalized system efficiency. The derived mode shift map is combined with a novel energy management strategy, referred to as normalized efficiency maximum strategy, to formulate an integrated real-time control strategy for the multimode HEV. Finally, by comparing two other commonly used strategies, dynamic programing and equivalent consumption minimization strategy, the proposed control strategy shows great potential in improving fuel economy while maintaining smooth engine operation, thus leading to better ride comfort of the vehicle. [ABSTRACT FROM AUTHOR]

This paper presents an integrated platoon control framework for heterogeneous vehicles on curved roads with varying slopes, aerodynamic drag, and wireless communication delays. First, a linear heterogeneous vehicle platoon model with decoupled longitudinal and lateral dynamics is built based on nonlinear feedback linearization and arc-length parametric transformation of an arbitrary curved road. The external disturbances are also considered, including the up-down road slopes, wind and equivalent time-delays caused by the wireless communication delay and discrete data in onboard sensors. Second, to achieve stable platoon control on a curved road, the platoon uses the state-feedback control method in the longitudinal direction while each vehicle tracks the centerline of the curved road to realize lateral control. Longitudinally, an $ H_\infty$ platoon controller is designed based on the Lyapunov-Krasovskii functional to ensure the inner-vehicle and string stability of the platoon, which suffers from parameter uncertainty, and external disturbances. Additionally, the $ \mathcal {L}_2$ string stability is defined to guarantee that the perturbations do not grow unbounded as they propagate through the platoon. Simulation results indicate that the proposed robust controller achieves proper platoon intervehicle spacing in both the longitudinal and lateral directions. [ABSTRACT FROM AUTHOR]

This paper presents a robust fuzzy $H_{\infty }$ control strategy for improving vehicle lateral stability and handling performance through integration of direct yaw moment control system (DYC) and active front steering. Since vehicle lateral dynamics possesses inherent nonlinearities, the main objective is dedicated to deal with the nonlinear challenge in vehicle lateral dynamics by applying Takagi-Sugeno (T-S) fuzzy modeling approach. First, the nonlinear Brush tire dynamics and the nonlinear functions of longitudinal velocity are represented via a T-S fuzzy modeling technique, and vehicle parametric uncertainties are handled by the norm-bounded uncertainties. An uncertain nonlinear vehicle lateral dynamic T-S fuzzy model is then obtained with multi-fuzzy-rules. The resulting robust fuzzy $H_{\infty }$ state-feedback controller is designed with the parallel-distributed compensation strategy and premise variables, and solved via a set of linear matrix inequalities derived from Lyapunov asymptotic stability and quadratic $H_{\infty }$ performance. Simulations for two different maneuvers are implemented with a high-fidelity, CarSimⓇ, full-vehicle model to verify the effectiveness of the developed approach. It is confirmed from the results that the proposed controller can effectively preserve vehicle lateral stability and enhance yaw handling performance. [ABSTRACT FROM AUTHOR]