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This paper aims to develop an automatic miscalibration detection and correction framework to maintain accurate calibration of LiDAR and camera for autonomous vehicle after the sensor drift. First, a monitoring algorithm that can continuously detect the miscalibration in each frame is designed, leveraging the rotational motion each individual sensor observes. Then, as sensor drift occurs, the projection constraints between visual feature points and LiDAR 3-D points are used to compute the scaled camera motion, which is further utilized to align the drifted LiDAR scan with the camera image. Finally, the proposed method is sufficiently compared with two representative approaches in the online experiments with varying levels of random drift, then the method is further extended to the offline calibration experiment and is demonstrated by a comparison with two existing benchmark methods. [ABSTRACT FROM AUTHOR]

Misunderstanding the driver behavior in the next short time is the primary reason of the false warning for the lane departure warning system. This paper proposes a learning-based evaluation to predict whether the driver notices the deviation of the vehicle and takes corrective actions. First, statistical Gaussian model and K-means clustering method are utilized to classify driving style of drivers and determine warning areas based on key driving parameters extracted in driving scenarios. Then, according to the vehicle trajectory in the warning area and the time of lane crossing (TLC) value of the two warning area boundaries, an advanced horizontal dual-area warning (HDAW) model that is trained by bi-direction long short-term memory (BiLSTM) originated from recurrent neural network (RNN) is applied to predict the lane departure and corrective behavior of driver. The personalized warning strategy is finally developed by considering driver characteristics, which allows the warning system to adapt to different driving styles of drivers. Natural driving data from 57 drivers in the experimental driving simulator are collected to train personalized prediction and verify proposed evaluation method. The recent directional sequence of piecewise lateral slopes (DSPLS) and traditional TLC are also researched and compared. Experimental results show that the proposed approach has as low as false alarm rate of 3.97% and can improve prediction accuracy approximately 41.39% over DSPLS method. [ABSTRACT FROM AUTHOR]

Four-wheel independently driven electric vehicles (FWID-EV) endow a flexible and scalable control framework to improve vehicle performance. This paper integrates the torque vectoring and active suspension system (ASS) to enhance the vehicle's longitudinal and vertical motion control performance. While the nonlinear characteristic of the tire model leads to a relatively heavier computational burden. To facilitate the controller design and ease the load, a half-vehicle dynamics system is built and simplified to the linear-time-varying (LTV) model. Then a model predictive controller is developed by formulating the objective function by comprehensively considering the safety, energy-saving and comfort requirements. The in-wheel motor efficiency and the power loss of tire slip are treated as optimization indices in this work to reduce energy consumption. Finally, the effectiveness of the proposed controller is verified through the rapid-control-prototype (RCP) test. The results demonstrate the enhancement of the energy-saving as well as comfort on the basis of vehicle stability. [ABSTRACT FROM AUTHOR]

This paper presents a novel structure of multi-cell tube filled with auxetic structures with the aim of improving the crashworthiness performances. The energy absorbing performances of the multi-cell tubes filled with auxetic structures are investigated by finite element analysis and validated experimentally. Compared with other typical energy absorbers, the multi-cell tubes filled with auxetic structures have better energy absorption performance. A parametric study is performed to analyze the effects of beam thickness, beam angle, and tube thickness on the crashworthiness performances. It is found that the tube and beam thicknesses and beam angles have great effects on the energy absorbing performances. An analytical model is also established to predict the energy absorption of multi-cell tubes filled with auxetic structures under axial impact loadings. The analytical predictions agree well with the numerical simulations indicating the analytical model is accurate to predict the energy absorption performances of the proposed structures. [ABSTRACT FROM AUTHOR]

Lightweight electric vehicles (LEVs) possess great advantages in the viewpoint of fuel consumption, environment protection and traffic mobility. However, due to the drastic reduction of vehicle weights and body size, the effects of payload parameter variation in LEV control and estimation system become much more pronounced and have to be systematically analysed. This paper proposes a novel payload parameter sensitivity analysis to provide quantitative insight into the sensitivity of payload parameter on the LEV system responses and state estimation. The analysis-oriented LEV dynamic model considering payload parameter variations is developed. Then, the trajectory sensitivity index of the influential parameters is defined and derived with the perturbation approach, the median method is used to improve the calculation accuracy for the trajectory sensitivity of payload parameter. Finally, the extended Kalman filter is designed to show the effect and importance of the sensitive payload parameters on the observation accuracy, the payload parameter variations along with fundamental state estimation such as vehicle sideslip angle, longitudinal velocity and vehicle roll angle are analysed. Simulation results with Matlab/Simulink-Carsim® show that the proposed method can accurately describe the relationship between the vehicle payload parameters and system state estimation, which is helpful to design and evaluate LEV controller and observer performances. [ABSTRACT FROM AUTHOR]

Electrified vehicles, especially fully driven electric ground vehicles, are expected to provide significantly increased traffic mobility and road utilization with faster response times, lower levels of fuel consumption, less environmental pollution, electrified power sources and actuators, and the benefits of greater driving safety and convenience integrated with diverse, dynamic subsystems [[1], [3]]. Achieving advanced vehicle stability control over vehicle dynamics is a very important aspect of improving vehicle handling and safety performance. Then, a fuzzy reasoning strategy for fusing the longitudinal tire adhesion coefficient and the lateral tire adhesion coefficient is designed, and the experimental results of vehicle tests conducted with a four-wheel independently driven vehicle modified via wire (UTV) prove that the proposed estimation method can accurately estimate the peak adhesion coefficient of pavement. [Extracted from the article]