As a severe tire failure, tire blowout during driving can significantly threaten vehicle stability and road safety. Tire blowout models were developed in the literature to conclude that a vehicle always deviates to the tire blowout side. However, this conclusion is proved to be inaccurate in this paper, since one important factor was largely ignored in the existing tire blowout models. Toe angle, as a basic and widely-applied setup on ground vehicles, can provide preset and symmetric lateral tire forces for normal driving. However, when tire blowout occurs, different toe angle setups can impact vehicle motions in different ways. For the first time, the toe angle is explicitly considered and integrated into a tire blowout model in this paper. For different tire blowout locations, driving maneuvers, and drivetrain configurations, the impacts of different toe angle setups on the variations of tire friction forces and vehicle motions are analyzed. The developed tire blowout model with toe angles is validated through both high-fidelity CarSim® simulation results and experimental results of a scaled test vehicle. Both simulation and experimental results show that a vehicle may not deviate to the tire blowout side, depending on the toe angle setups and driving maneuvers. Moreover, the experimental results also validate that the proposed tire blowout model can accurately evaluate the tire blowout impacts on vehicle dynamics.

Point-to-point path planning for a kinematic model of a differential-drive wheeled mobile robot (WMR) with the goal of minimizing input energy is the focus of this work. An optimal control problem is formulated to determine the necessary conditions for optimality and the resulting two point boundary value problem is solved in closed form using Jacobi elliptic functions. The resulting nonlinear programming problem is solved for two variables and the results are compared to the traditional shooting method to illustrate that the Jacobi elliptic functions parameterize the exact profile of the optimal trajectory. A set of terminal constraints which lie on a circle in the first quadrant are used to generate a set of optimal solutions. It is noted that for maneuvers where the angle of the vector connecting the initial and terminal point is greater than a threshold, which is a function of the radius of the terminal constraint circle, the robot initially moves into the third quadrant before terminating in the first quadrant. The minimum energy solution is compared to two other optimal control formulations: (1) an extension of the Dubins vehicle model where the constant linear velocity of the robot is optimized for and (2) a simple turn and move solution, both of whose optimal paths lie entirely in the first quadrant. Experimental results are used to validate the optimal trajectories of the differential-drive robot.

Hybrid electric vehicle (HEV) control strategies are often designed around specific driving conditions. However, when driving conditions differ from the designed conditions, HEV performance can suffer. This paper develops a novel HEV energy management strategy (EMS) that is robust to uncertain driving conditions by augmenting a stochastic dynamic programing (SDP) controller with minimax dynamic programing (MDP). This combination of MDP and SDP has not previously been studied in the literature. The stochastic element uses a Markov chain model to represent driver behavior and is used to optimize the control for expected future driver behavior. The minimax element instead optimizes against potential worst-case (maximal cost) future driver behavior. The resulting EMS can be directly implemented on a vehicle. This method is demonstrated on a series hybrid electric bus model. Robustness to uncertain driving conditions is tested by simulating on a variety of heavy-duty vehicle drive cycles that differ from the drive cycle on which the EMS was trained. A single tuning parameter is used to balance the stochastic and minimax elements of the EMS, and a parametric study shows the effects of this tuning parameter. It was found that using minimax control could increase the vehicle fuel economy on multiple uncertain driving conditions, with a tradeoff of decreased fuel economy when the driving conditions match the designed conditions. That is, it offers an exchange of performance on the nominal driving conditions for performance on uncertain driving conditions.

Recent deep learning techniques promise high hopes for self-driving cars while there are still many issues to be addressed such as uncertainties (e.g., extreme weather conditions) in learned models. In this work, for the uncertainty-aware lane keeping, we first propose a convolutional mixture density network (CMDN) model that estimates the lateral position error, the yaw angle error, and their corresponding uncertainties from the camera vision. We then establish a vision-based uncertainty-aware lane keeping strategy in which a high-level reinforcement learning policy hierarchically modulates the reference longitudinal speed as well as the low-level lateral control. Finally, we evaluate the robustness of our strategy against the uncertainties of the learned CMDN model coming from unseen or noisy situations, as compared to the conventional lane keeping strategy without taking into account such uncertainties. Our uncertainty-aware strategy outperformed the conventional lane keeping strategy, without a lane departure in our test scenario during high-uncertainty periods with random occurrences of fog and rain situations on the road. The successfully trained deep reinforcement learning agent slows down the vehicle speed and tries to minimize the lateral error during high uncertainty situations similarly to what human drivers would do in such situations.

A controller for critical vehicle maneuvering is proposed that avoids obstacles and keeps the vehicle on the road while achieving heavy braking. It operates at the limit of friction and is structured in two main steps: a motion-planning step based on receding-horizon planning to obtain acceleration-vector references, and a low-level controller for following these acceleration references and transforming them into actuator commands. The controller is evaluated in a number of challenging scenarios and results in a well behaved vehicle with respect to, e.g., the steering angle, the body slip, and the path. It is also demonstrated that the controller successfully balances braking and avoidance such that it really takes advantage of the braking possibilities. Specifically, for a moving obstacle, it makes use of a widening gap to perform more braking, which is a clear advantage of the online replanning capability if the obstacle should be a moving human or animal. Finally, real-time capabilities are demonstrated. In conclusion, the controller performs well, both from a functional perspective and from a real-time perspective.

This study uses a low-density solid-state flash lidar for estimating the trajectories of road vehicles in vehicle collision avoidance applications. Low-density flash lidars are inexpensive compared to the commonly used radars and point-cloud lidars, and have attracted the attention of vehicle manufacturers recently. However, tracking road vehicles using the sparse data provided by such sensors is challenging due to the few reflected measurement points obtained. In this paper, such challenges in the use of low-density flash lidars are identified and estimation algorithms to handle the same are presented. A method to use the amplitude information provided by the sensor for better localization of targets is evaluated using both physics-based simulations and experiments. A two-step hierarchical clustering algorithm is then employed to group multiple detections from a single object into one measurement, which is then associated with the corresponding object using a Joint Integrated Probabilistic Data Association (JIPDA) algorithm. A Kalman filter is used to estimate the longitudinal and lateral motion variables and the results are presented, which show that good tracking, especially in the lateral direction, can be achieved using the proposed algorithm despite the sparse measurements provided by the sensor.

As a new strategy for magnetic levitation envisioned in the 1990s, the Inductrack system with Halbach arrays of permanent magnets has been intensively researched. The previous investigations discovered that an uncontrolled Inductrack system may be unstable even if the vehicle travels well below its operating speed and that instability can be persistent near and beyond the operating speed. It is therefore necessary to stabilize the system for safety and reliability. With strong nonlinearities and complicated electromagneto-mechanical coupling, however, the transient response of such a dynamic system is difficult to predict with fidelity. Because of this, model-based feedback control of Inductrack systems has not been well addressed. In this paper, by taking advantage of a recently available two degrees-of-freedom transient model, a new feedback control method for Inductrack systems is proposed. In the control system development, active Halbach arrays are used as an actuator, and a feedback control law, which combines a properly tuned proportional-integral-derivative controller and a nonlinear force-current mapping function, is created. The proposed control law is validated in numerical examples, where the transient motion of an Inductrack vehicle traveling at constant speeds is considered. As shown in the simulation, the control law efficiently stabilizes the Inductrack system in a wide range of operating speed, and in the meantime, it renders a smooth system output (real-time levitation gap) with fast convergence to any prescribed reference step input (desired levitation gap).

This paper presents the design and analysis of a predictive ecological control strategy for a heavy-duty truck that achieves substantial fuel savings while maintaining safe following distances in the presence of traffic. The hallmark of the proposed algorithm is the fusion of a long-horizon economic model predictive controller (MPC) for ecological driving with a command governor (CG) for safe vehicle following. The performance of the proposed control strategy was evaluated in simulation using a proprietary medium-fidelity Simulink model of a heavy-duty truck. Results show that the strategy yields substantial fuel economy improvements over a baseline, the extent of which are heavily dependent on the horizon length of the CG. The best fuel and vehicle-following performance are achieved when the CG horizon has a length of 20–40 s, reducing fuel consumption by 4–6% when compared to a Gipps car-following model.

The vehicle transfer case clutch plays an important role for four-wheel drive (4WD) vehicles since the torque transmitted through the clutch determines the amount of traction torque on tires, which is important for vehicle performance. However, the clutch torque measurement is usually unavailable on production vehicles and needs to be estimated accurately to improve vehicle performance. This paper proposes a unified scheme to model clutch output torque under all three conditions: open (no torque output), slipping, and overtaken. Specifically, the clutch torque model under clutch overtaken condition is first investigated using the vehicle longitudinal and tire dynamics. It was found that effective radius of front tires, powered by the transfer case clutch torque, cannot be assumed as constant and should be compensated by vehicle acceleration, while the effective radius of rear tires connected directly to the propulsion system does not need to be compensated. In addition, it was found that torque model under clutch overtaken condition cannot be used under slip condition. As a result, a general clutch torque model is developed for both slip and overtaken conditions with a clutch slip speed compensation, resulting a root-mean-square error percentage (RMSE%) of 6.8% comparing with the experimental measurement data. Note that overtaken torque model is a special case of the general torque model by setting slip speed equal to zero. The general clutch torque model is able to calculate clutch output torque accurately under both slip and overtaken conditions.

This work considers the cooperative trajectory-planning problem along a double lane change scenario for autonomous driving. In this paper, we develop two frameworks to solve this problem based on distributed model predictive control (MPC). The first approach solves a single nonlinear MPC problem. The general idea is to introduce a collision cost function in the optimization problem at the planning task to achieve a smooth and bounded collision function, and thus to prevent the need to implement tight hard constraints. The second method uses a hierarchical scheme with two main units: a trajectory-planning layer based on mixed-integer quadratic program (MIQP) computes an on-line collision-free trajectory using simplified motion dynamics, and a tracking controller unit to follow the trajectory from the higher level using the nonlinear vehicle model. Connected and automated vehicles (CAVs) sharing their planned trajectories lay the foundation of the cooperative behavior. In the tests and evaluation of the proposed methodologies, matlab - carsim cosimulation is utilized. carsim provides the high-fidelity model for the multibody vehicle dynamics. matlab - carsim conjoint simulation experiments compare both approaches for a cooperative double lane change maneuver of two vehicles moving along a one-way three-lane road with obstacles.

In this paper, the issue of string stability for acceleration-controlled vehicles interconnected in a chain is studied. String stability is concerned with having bounded displacements between vehicles in such a way that displacements should not grow unboundedly with respect to the perturbation. Different definitions can be given to string stability: one that relates to the amplification of a local disturbance acting on one vehicle toward the whole vehicle chain, more strict definition that is related to the boundedness of vector norm of displacements with respect to the bounded vector norm of disturbance inputs acting on all vehicles; and, most practical definition that considers the boundedness of signal norm of each individual displacement with respect to the bounded signal norm of disturbance inputs acting on all vehicles, independently from the number of vehicles. It has been proven that these definitions are all impossible to be achieved using any linear homogeneous unidirectional distributed controllers with constant spacing policy. This paper proposes linear heterogeneous controllers where each vehicle behaves differently from others in a vehicle chain. We prove that three different definitions of string stability can be attained using the proposed heterogeneous controller. We propose sufficient conditions to guarantee string stability and boundedness of acceleration of each vehicle. Finally, simulation results are given to illustrate the effectiveness of proposed heterogeneous control synthesis.

A new method is proposed to estimate and analyze the vehicle lateral stability region, which provides a direct and intuitive demonstration for the safety and stability control of ground vehicles. Based on a four-wheel vehicle model and a nonlinear two-dimensional (2D) analytical LuGre tire model, a local linearization method is applied to estimate the vehicle lateral stability regions by analyzing the vehicle stability at each operation point on a phase plane, which includes but not limited to the equilibrium points. As the collections of all the locally stable operation points, the estimated stability regions are conservative because both vehicle and tire stability are simultaneously considered, which are especially important for characterizing the stability features of highly/fully automated ground vehicles (AGV). The obtained lateral stability regions can be well explained by the vehicle characteristics of oversteering and understeering in the context of vehicle handling stability. The impacts of vehicle lateral load transfer, longitudinal velocity, tire-road friction coefficient, and steering angle on the estimated stability regions are presented and discussed. To validate the correctness of the estimated stability regions, a case study by matlab / simulink and CarSim ® co-simulation is presented and discussed.

This paper introduces a hierarchical economic model predictive control (MPC) approach for maximizing the fuel economy of a heavy-duty truck, which simultaneously accounts for aggregate terrain changes that occur over very long length scales, fine terrain changes that occur over shorter length scales, and lead vehicle behavior that can vary over much shorter time/length scales. To accommodate such disparate time and length scales, the proposed approach uses a multilayer MPC approach wherein the upper-level MPC uses a long distance step, a long time-step, and coarse discretization to account for the slower changes in road grade, while the lower-level MPC uses a shorter time-step to account for fine variations in road grade and rapidly changing lead vehicle behavior. The benefit of this multirate, multiscale approach is that the lower-level MPC leverages the upper-level's sufficiently long look-ahead while allowing for safe vehicle following and adjustment to fine road grade variations. The proposed strategy has been evaluated over four real-world road profiles in both open-highway and traffic environments, using a medium-fidelity simulink model furnished by Volvo Group North America. Compared with a conventional cruise control system plus vehicle following controller as a baseline, results show 4–5% fuel savings in an open highway setting and 6–8% fuel savings in the presence of traffic, without compromising trip time.

The development of vehicle connectivity and autonomy in the ground transportation sector is not only able to enhance traffic safety and driving comfort as well as fuel economy. This study presents a receding-horizon optimization-based control strategy integrated with the preceding vehicle speed prediction model to achieve an eco-driving strategy for connected and automated vehicles (CAVs). In the real traffic scenario where the CAV follows the preceding vehicle on the road, a gated recurrent unit (GRU) network is used to predict the behavior of the preceding vehicle by utilizing the historical inter-vehicle information collected through on-board sensors. Then, a nonlinear model predictive control (NMPC) algorithm is adopted for CAV to minimize the accumulated fuel consumption within the preview horizon. The NMPC approach solves the fuel-optimal speed profile of the CAV, considering a predicted short-term speed preview of the preceding vehicle. With the awareness of the preview speed conditions, the fuel consumption of the CAV is reduced by avoiding unnecessary braking and acceleration, especially during transient traffic conditions. The Pareto front framework is used to examine a trade-off between the vehicle speed prediction accuracy, computational burden, and the fuel consumption of the CAV in the proposed GRU-NMPC design. To analyze the effectiveness of the GRU-NMPC design, adaptive cruise control with constant time headway policy (ACC-CTH) is adopted as a benchmark control design. Comparison results show significant fuel economy improvement of the proposed design and expose possible fuel benefits from vehicle autonomy and sensor fusion technology.

In preview-based vehicle suspension applications, the preview of the road profile is highly dependent on the preview sensors. In some scenarios such as heavy traffic situations, the preview of road profile can only be estimated by other vehicles because the view of the preview sensors may be blocked by other vehicles. The estimated preview road information can contain errors, which thus requires the controller to have a good robust performance. In this paper, an incremental model predictive control (MPC) strategy for active suspension systems along with a road profile estimator using preview information from a lead vehicle is proposed. The efficacy of the proposed strategy is experimentally validated on two scaled-down active suspension stations with comparison to two conventional active suspension control approaches.

This paper presents a multilevel model-based hierarchical estimation framework for complex thermal management systems of electrified vehicles. System dynamics are represented by physics-based lumped parameter models derived from a graph-based modeling approach. The complexity of the hierarchical models is reduced by applying an aggregation-based model-order reduction technique that preserves the physical correspondence between a reduced-order model and the physical system. This paper also presents a case study in which a hierarchical observer is designed to estimate the dynamics of a candidate system. The hierarchical observer is connected to a previously developed hierarchical controller for closed-loop control, and the closed-loop performance is demonstrated through simulation and real-time experimental results. A comparison between the proposed hierarchical observer and a centralized observer shows the tradeoff between the estimation accuracy and the computational complexity of the two approaches.

Benefits of introducing active suspension in autonomous vehicles in terms of improving the driver's work/leisure ability can be tested under laboratory conditions using a high-performance shaker rig. In this paper, five different suspension configurations, including passive, semi-active, and fully active suspensions (FAS) and related optimal controls with or without road preview information, are implemented in vehicle dynamics simulator. They are experimentally examined having the driver in the loop to obtain objective and subjective measures of ride comfort and ability to do certain tasks. In total, 44 drivers of various age, gender, and driving experience performed three different tasks while being driven on shaker-rig (reading and writing, drawing, and subjective grading). The examination results show that FAS with road preview control offers highest ride comfort improvement, which can be perceived by the driver and greatly improves his/her ability to text and particularly draw.

This paper presents a rigorous analysis of a promising bi-modal multirotor vehicle that can roll and fly. This class of vehicle provides energetic and locomotive advantages over traditional unimodal vehicles. Despite superficial similarities to traditional multirotor vehicles, the dynamics of the vehicle analyzed herein differ substantially. This paper is the first to offer a complete and rigorous derivation, simulation, and validation of the vehicle's terrestrial rolling dynamics. Variational mechanics is used to develop a six degrees-of-freedom dynamic model of the vehicle subject to kinematic rolling constraints and various nonconservative forces. The resulting dynamic system is determined to be differentially flat and the flat outputs of the vehicle are derived. A functional hardware embodiment of the vehicle is constructed, from which empirical motion data are obtained via odometry and inertial sensing. A numerical simulation of the dynamic model is executed, which accurately predicts complex dynamic phenomena observed in the empirical data, such as gravitational and gyroscopic nonlinearities; the comparison of simulation results to empirical data validates the dynamic model.

The performance of an electrified powertrain in extreme operating conditions is greatly compromised. This is due to the fact that meeting the road loads, ensuring efficient powertrain operation, and minimizing the loss of lifetime (aging) of an electric machine are three essential but conflicting targets. In this paper, a unified multi-objective linear parameters varying (LPV)-based field-oriented control (FOC) framework is proposed to solve the problem of conflicting objectives mentioned above. The outer loop in a unified control framework generates the reference currents using linear matrix inequality (LMI)-based torque and flux controllers. Moreover, optimal flux is estimated using LPV observer to ensure efficient machine operations. In the inner loop of a unified control framework, the multi-objective controller (MOC) is synthesized by selecting optimal weighting functions using LMI-based convex optimization approach. The stability of the proposed unified control framework is also analyzed. The effectiveness of the proposed unified control framework is tested for a direct drive electrified powertrain of a three-wheeled vehicle commonly found in urban transportation for Asian countries. The urban driving schedule-based simulation results confirm that the lifetime of traction machine can be enhanced by appropriate control framework design without compromising its performance.

This paper proposes a singular system-based approach for active vibration control of vehicle seat suspensions, where the drivers' acceleration is augmented into the conventional seat suspension model together with seat suspension deflection and relative velocity as system states to make the suspension model as a singular system. In this novel seat suspension system, all the system states are easy to measure in real-time. A friction observer is applied to estimate the real system friction and an H ∞ controller is designed to achieve the optimal ride comfort performance with consideration of the friction compensation, actuator saturation, and time delay issues. The cone complementarity linearization (CCL) algorithm is applied to solve the nonlinear constraints. The experimental results show that good ride comfort performance can be achieved by the proposed controller in both the time and frequency domain compared with the uncontrolled seat suspension.