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The plunging force (PF) induced by friction between internal parts of tripod joints of drive-shaft systems will cause NVH (noise, vibration, and harshness) problems of the vehicle. To study the PF more deeply, a deterministic model of the PF of drive-shaft systems considering rough surface characteristics is proposed on the basis of the kinematics analysis of drive-shaft systems and fractal methods. The deterministic model suggests that the PF is related to the fractal parameters (which are used to characterize rough surface characteristics of contact pairs), the material parameters, the bending angle of drive-shaft systems. The validity of the deterministic model is verified by measuring the PF of a drive-shaft system. Due to manufacturing, assembly, measurement, and other errors, uncertainty exists in drive-shaft systems. To evaluate the PF more effectively, a hybrid uncertain model with random and interval variables (HUMRIV) of the PF considering rough surface characteristics is proposed based on the proposed deterministic model and orthogonal polynomials. In the HUMRIV, the fractal parameters are considered as interval variables, while the material parameters and the bending angle are considered as random variables. Combining orthogonal polynomials, perturbation method, and vertex method, an efficient method for solving the responses of the HUMRIV is subsequently proposed, which is named orthogonal-perturbation-vertex method. Based on the reliability analysis method, a hybrid uncertain optimization method for the PF considering rough surface characteristics is proposed. The study result suggests that the proposed orthogonal-perturbation-vertex method ensures both efficiency and accuracy of analysis and optimization of the complex HUMRIV; and the hybrid uncertain analysis and optimization for the PF considering rough surface characteristics can provide a new way for the design optimization of the PF. [ABSTRACT FROM AUTHOR]

Dynamic multiobjective optimization problems (DMOPs) challenge multiobjective evolutionary algorithms (MOEAs) because of the varying Pareto-optimal sets (POS) over time. Research on DMOPs has attracted a great interest from academic, due to widespread applications of DMOPs. Recently, a few learning-based approaches have been proposed to predict new solutions in the following environments as an initial population for a multiobjective evolutionary algorithm. In this paper, we propose an alternative learning-based method for DMOPs, a deep multi-layer perceptron-based predictor to generate an initial population for the MOEA in the new environment. The historical optimal solutions are used to train a deep multi-layer perceptron which then predicts a new set of solutions as the initial population in the new environment. The deep multi-layer perceptron is incorporated with the multiobjective evolutionary algorithm based on decomposition to solve DMOPs. Empirical results demonstrate that our proposed algorithm is effective in tracking varying solutions over time and shows great superiority comparing with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Radar and LiDAR are two environmental sensors commonly used in autonomous vehicles, Lidars are accurate in determining objects' positions but significantly less accurate as Radars on measuring their velocities. However, Radars relative to Lidars are more accurate on measuring objects velocities but less accurate on determining their positions as they have a lower spatial resolution. In order to compensate for the low detection accuracy, incomplete target attributes and poor environmental adaptability of single sensors such as Radar and LiDAR, in this paper, an effective method for high-precision detection and tracking of surrounding targets of autonomous vehicles. By employing the Unscented Kalman Filter, Radar and LiDAR information is effectively fused to achieve high-precision detection of the position and speed information of targets around the autonomous vehicle. Finally, the real vehicle test under various driving environment scenarios is carried out. The experimental results show that the proposed sensor fusion method can effectively detect and track the vehicle peripheral targets with high accuracy. Compared with a single sensor, it has obvious advantages and can improve the intelligence level of autonomous cars. [ABSTRACT FROM AUTHOR]

Due to the bus characteristics of large quality, high center of gravity and narrow wheelbase, the research of its yaw stability control (YSC) system has become the focus in the field of vehicle system dynamics. However, the tire nonlinear mechanical properties and the effectiveness of the YSC control system are not considered carefully in the current research. In this paper, a novel adaptive nonsingular fast terminal sliding mode (ANFTSM) control scheme for YSC is proposed to improve the bus curve driving stability and safety on slippery roads. Firstly, the STI (Systems Technologies Inc.) tire model, which can effectively reflect the nonlinear coupling relationship between the tire longitudinal force and lateral force, is established based on experimental data and firstly adopted in the bus YSC system design. On this basis, a more accurate bus lateral dynamics model is built and a novel YSC strategy based on ANFTSM, which has the merits of fast transient response, finite time convergence and high robustness against uncertainties and external disturbances, is designed. Thirdly, to solve the optimal allocation problem of the tire forces, whose objective is to achieve the desired direct yaw moment through the effective distribution of the brake force of each tire, the robust least-squares allocation method is adopted. To verify the feasibility, effectiveness and practicality of the proposed bus YSC approach, the TruckSim-Simulink co-simulation results are finally provided. The co-simulation results show that the lateral stability of bus under special driving conditions has been significantly improved. This research proposes a more effective design method for bus YSC system based on a more accurate tire model. [ABSTRACT FROM AUTHOR]

The piecewise affine (PWA) model represents an attractive model structure for approximating nonlinear systems. In this paper, a procedure for obtaining the PWA model of the tire nonlinear cornering properties is introduced. In this approach, the highly nonlinear dynamic of the tire cornering properties is well approximated by a set of affine maps which relate inputs and outputs. These maps are defined in disjunctive regions in the regression space, itself composed of system inputs and outputs. The tire cornering properties tests are firstly carried out through a high-performance flat-plate test bench, thus the experimental data which can accurately reflect the tire cornering properties is obtained. On this basis, the PWA identification of the tire cornering properties is composed of the data clustering, the parameter estimation of the affine submodels and the calculation of the hyperplane coefficient matrices, which are respectively achieved by means of fuzzy c-means clustering, weighted least-squares and support vector machines. Finally, to verify the PWA model accuracy in approximating the tire nonlinear cornering properties, the comparison between the simulation results of the PWA identification model and the experimental data is conducted and the comparison results are analyzed. [ABSTRACT FROM AUTHOR]

Tire cornering characteristics have significant influence on vehicle lateral dynamics control. Unlike traditional tire mechanics models which are established based on the research experience or the mechanics mechanism, in this study, a novel experimental data-driven modeling approach is presented to model the tire cornering characteristics based on piecewise affine (PWA) identification method. In this approach, the highly nonlinear dynamic of the tire cornering characteristics is well approximated by several affine submodels acting on different regions. To obtain the experimental data which can accurately reflect the tire cornering characteristics, the tire tests are firstly carried out through a high-performance flat-plate test bench. On this basis, the PWA identification of the tire cornering characteristics is composed of the data clustering, the parameter estimation of the affine submodels and the calculation of the hyperplane coefficient matrices. The simulation results of the PWA identification model are finally compared with the experimental data to illustrate that the identified model has high accuracy in approximating the tire nonlinear cornering characteristics under wide range driving conditions. [ABSTRACT FROM AUTHOR]

Crowd counting has played a substantial role in intelligent surveillance. This work presents a multi-scale multi-task convolutional neural network (MSMT-CNN) to estimate accurate density maps, thus can count the crowd through summing up all values in the estimated density maps. The ground truth density maps used for training are generated by a novel adaptive human-shaped kernel. In addition to resolving the scale problem with the multi-scale strategy, the multi-task learning strategy is added so as to make the estimated density maps more accurate. A weighted loss function is proposed to enhance the activations in dense regions and suppress the background noise. Experimental results on two benchmarking datasets reveal the strong ability of MSMT-CNN. Compared with existing crowd counting methods, the root mean squared error is decreased by 39.8 on the UCF_CC_50 dataset, and the mean absolute error is decreased by 2.3 on the World Expo'10 dataset. Furthermore, the evaluations in practical bus videos verify the practicability of our MSMT-CNN. [ABSTRACT FROM AUTHOR]

A hybrid model predictive control (HMPC) strategy is proposed in this paper to autonomously regulate intelligent vehicle longitudinal velocity considering nonlinear tire dynamics. Since the tire longitudinal dynamics, which has significant influence on vehicle longitudinal velocity control performance, exhibits highly nonlinear dynamical behaviors, the piecewise affine (PWA) identification is conducted firstly based on experimental data to accurately model the tire longitudinal dynamics. On this basis, due to that the intelligent vehicle needs to be operated in two distinct modes (drive and brake) for autonomous velocity regulation and because of the affine submodel switching behaviors of the PWA-identified tire model, the intelligent vehicle longitudinal dynamics control process considered in this work can be regarded as a hybrid system with both continuous variables and discrete operating modes. Thus, the mixed logical dynamical framework is further used to model the intelligent vehicle longitudinal dynamics, and a HMPC controller, which allows us to optimize the switching sequences of the operation modes (binary control inputs) and the torques acted on the wheels (continuous control inputs), is tuned based on online mixed-integer quadratic programming. Simulation results finally demonstrate the effectiveness of the proposed HMPC controller for intelligent vehicle longitudinal velocity regulation under typical driving conditions. [ABSTRACT FROM AUTHOR]

In this paper, a trajectory tracking control system, which consists of a model predictive control unit and an active safety steering control unit, has been developed. A nonlinear bicycle vehicle model, including the longitudinal, lateral, yaw, and quasi-static roll motions, was derived as a predictive model to simulate and test the proposed model predictive control (MPC) system. A 4-DOF vehicle model was used to reflect the characteristics of vehicle dynamics to avoid rollover accidents of automobiles. Simulation was performed and experiment results demonstrated good performance of both MPC unit and active safety steering control unit. Finally, it was proved that the proposed trajectory tracking control system is easy to realize with low cost. [ABSTRACT FROM AUTHOR]

Cyclist trajectory prediction is of great significance for both active collision avoidance and path planning of intelligent vehicles. This paper presents a trajectory prediction method for the motion intention of cyclists in real traffic scenarios. This method is based on dynamic Bayesian network (DBN) and long short-term memory (LSTM). The motion intention of cyclists is hard to predict owing to potential large uncertainties. The DBN is used to infer the distribution of cyclists’ intentions at intersections to improve the prediction time. The LSTM with encoder-decoder is used to predict the cyclists’ trajectories to improve the accuracy of prediction. Therefore, the DBN and LSTM are adopted to guarantee prediction accuracy and improve the prediction time. The experiment results are presented to show the effectiveness of the predict strategies. [ABSTRACT FROM AUTHOR]

Vehicle height and leveling control of electronically controlled air suspension (ECAS) still poses theoretical challenges for researchers that have not been adequately addressed in prior research. This paper investigates the design and verification of a new controller to adjust the vehicle height and to regulate the roll and pitch angles of the vehicle body (leveling control) during the height adjustment procedures. A nonlinear mechanism model of the vehicle height adjustment system is formulated to describe the dynamic behaviors of the system. By using mixed logical dynamical (MLD) approach, a novel control strategy is proposed to adjust the vehicle height by controlling the on-off statuses of the solenoid valves directly. On this basis, a correction algorithm is also designed to regulate the durations of the on-off statuses of the solenoid valves based on pulse width modulated (PWM) technology, thus the effective leveling control of the vehicle body can be guaranteed. Finally, simulations and vehicle tests results are presented to demonstrate the effectiveness and applicability of the proposed control methodology. [ABSTRACT FROM AUTHOR]

Traditional vehicle detection algorithms do not include targeted processing to handle the vehicle occlusion phenomenon. To address this issue, this paper proposes a locally-connected, deep-model-based, occluded vehicle detection algorithm. Firstly, a suspected occluded vehicle is generated using a cascaded Adaboost Classifier. Any sub-images that are rejected during the last two stages of the cascaded Adaboost Classifier are considered as a suspected occluded vehicle. Then, eight types of vehicle occlusion visual models are manually established. The suspected occluded vehicle will be assigned to a certain type of model by color histogram matching. Finally, the sub image of the suspected occluded vehicle will be loaded into a locally connected deep model of the corresponding type to make the final determination. An experiment using the KITTI dataset has demonstrated that compared with existing vehicle detection algorithms such as the cascaded Adaboost, the Deformable Part Model (DPM), Deep Convolutional Neural Networks (DCNN) and the Deep Belief Network (DBN), this algorithm has a much higher occluded vehicle detection rate. Additionally, this method requires minimal extra processing time, at around 5 % higher than the cascaded Adaboost. [ABSTRACT FROM AUTHOR]

Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification. These types of methods generally have high processing times and low vehicle detection performance. To address this issue, a visual saliency and deep sparse convolution hierarchical model based vehicle detection algorithm is proposed. A visual saliency calculation is firstly used to generate a small vehicle candidate area. The vehicle candidate sub images are then loaded into a sparse deep convolution hierarchical model with an SVM-based classifier to perform the final detection. The experimental results demonstrate that the proposed method is with 94.81% correct rate and 0.78% false detection rate on the existing datasets and the real road pictures captured by our group, which outperforms the existing state-of-the-art algorithms. More importantly, high discriminative multi-scale features are generated by deep sparse convolution network which has broad application prospects in target recognition in the field of intelligent vehicle. [ABSTRACT FROM AUTHOR]

The control problems associated with vehicle height adjustment of electronically controlled air suspension (ECAS) still pose theoretical challenges for researchers, which manifest themselves in the publications on this subject over the last years. This paper deals with modeling and control of a vehicle height adjustment system for ECAS, which is an example of a hybrid dynamical system due to the coexistence and coupling of continuous variables and discrete events. A mixed logical dynamical (MLD) modeling approach is chosen for capturing enough details of the vehicle height adjustment process. The hybrid dynamic model is constructed on the basis of some assumptions and piecewise linear approximation for components nonlinearities. Then, the on-off statuses of solenoid valves and the piecewise approximation process are described by propositional logic, and the hybrid system is transformed into the set of linear mixed-integer equalities and inequalities, denoted as MLD model, automatically by HYSDEL. Using this model, a hybrid model predictive controller (HMPC) is tuned based on online mixed-integer quadratic optimization (MIQP). Two different scenarios are considered in the simulation, whose results verify the height adjustment effectiveness of the proposed approach. Explicit solutions of the controller are computed to control the vehicle height adjustment system in realtime using an offline multi-parametric programming technology (MPT), thus convert the controller into an equivalent explicit piecewise affine form. Finally, bench experiments for vehicle height lifting, holding and lowering procedures are conducted, which demonstrate that the HMPC can adjust the vehicle height by controlling the on-off statuses of solenoid valves directly. This research proposes a new modeling and control method for vehicle height adjustment of ECAS, which leads to a closed-loop system with favorable dynamical properties. [ABSTRACT FROM AUTHOR]

Maximum margin criterion (MMC) is a promising feature extraction method proposed recently to enhance the well-known linear discriminant analysis. However, due to the maximization of L2-norm-based distances between different classes, the features extracted by MMC are not robust enough in the sense that in the case of multi-class, the faraway class pair may skew the solution from the desired one, thus leading the nearby class pair to overlap. Aiming at addressing this problem to enhance the performance of MMC, in this paper, we present a novel algorithm called complete robust maximum margin criterion (CRMMC) which includes three key components. To deemphasize the impact of faraway class pair, we maximize the L1-norm-based distance between different classes. To eliminate possible correlations between features, we incorporate an orthonormality constraint into CRMMC. To fully exploit discriminant information contained in the whole feature space, we decompose CRMMC into two orthogonal complementary subspaces, from which the discriminant features are extracted. In such a way, CRMMC can iteratively extract features by solving two related constrained optimization problems. To solve the resulting mathematical models, we further develop an effective algorithm by properly combining polarity function and optimal projected gradient method. Extensive experiments on both synthesized and benchmark datasets verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]