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In this report, a combined longitudinal and lateral eighteen-state vehicle chassis, engine, and drive train model is developed and validated against existing longitudinal-only and lateral-only vehicle models. The full-size model is simplified to a three-state model to facilitate controller design. The control task in a combined maneuver is defined...
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This report summarizes an experimental effort in integrating and testing an automated vehicle lateral control system. The project, a cooperative effort between the California Partners for Advanced Transit and Highway ( PATH) Program and IMRA America, Inc., included a discrete roadway reference system, on-vehicle magnetic sensing system, a computer...
Citations
... GUO Konghui [13] established a relatively complete and systematic multi degree of freedom vehicle dynamics model in the process of studying the dynamic response of vehicles under steering drive and speed control. PHAM h [14] accurately described a multi degree of freedom vehicle dynamics model, which can accurately describe the vehicle dynamics characteristics. LI Bai [15] started with the decision-making and planning of a single vehicle, and characterized by fine modeling and efficient solution, respectively explained the collaborative decision-making and planning technology from the perspectives of robotics, numerical optimization, automatic driving and intelligent transportation, focusing on the collaborative decision-making and planning methods of intelligent networked vehicles under various traffic scenarios. ...
... I parameter variation, robust (sliding mode[16]) and adaptive[17]lane keeping controllers were proposed. A combined longitudinal and lateral controller which achieves asymptotically stable trajectory without measurement of the lateral vehicle velocity and accurate knowledge of the vehicle parameters was reported in[18]. The objective of the lane change maneuver is to transfer a vehicle which is under lane following control to lane following in an adjacent lane[19]. ...
In this paper, we present a mathematical model and adaptive controller for an autonomous vehicle overtaking maneuver. We consider the problem of an autonomous three-phase overtaking without the use of any roadway marking scheme or intervehicle communication. The developed feedback controller requires information for the current relative intervehicle position and orientation, which are assumed to be available from onboard sensors. We apply standard robotic nomenclature for translational and rotational displacements and velocities and propose a general kinematic model of the vehicles and the relative intervehicle kinematics during the overtaking maneuver. The overtaking maneuver is investigated as a tracking problem with respect to desired polynomial virtual trajectories for every phase, which are generated in real time. An update control law for the automated overtaking vehicle is designed that allows tracking the desired trajectories in the presence of unknown velocity of the overtaken vehicle. Simulation results illustrate the performance of the proposed controller.
... These vehicle models include single-track model, quarter-car model, half-car model or full-car model etc. A combined vehicle dynamics model including 6-DOF chassis model, suspension model, tire model, engine and brake model etc. has been validated and adapted by California PATH Program at UC Berkeley for automated vehicle control design [1,7] . The model presented in this paper has been modified by ignoring some unnecessary dynamics, i.e. pitch and vertical dynamics for control design and simulation. ...
Rollover prevention for vehicles with elevated center of gravity (CG) is presented in this paper. A 4-degree-of-freedom vehicle model for rollover prevention has been established using a combined vehicle dynamics modeling methodology based on previous work. For predicting the rollover impendence, the lateral load transfer ratio is also used for the maneuver-induced tripped rollover which has been marked the second most dangerous form of severe accidents. In the phase of control design, a type of active device, i.e. active anti-roll bar is used to generate the anti-roll moment with sliding mode control technique based on a further simplified vehicle model. Thereafter, the simulation with Fishhook and J-turn maneuvers has been conducted to evaluate the vehicle dynamics control system for rollover prevention. Topics: 1 Vehicle Dynamics; 5 Vehicle Control; 17 Active Safety NOMENCLATURE h distance from CG to roll center, m h r distance from roll center to the ground, m m s vehicle sprung mass, kg m vehicle mass, kg g acceleration due to gravity, m/s 2 I xx moment of inertia about x-axis, kgm 2 I yy moment of inertia about x-axis, kgm 2 I zz moment of inertia about x-axis, kgm 2 K s roll stiffness of suspension, Nm/rad l f distance from CG to front axle, m l r distance from CG to rear axle, m R load transfer ratio T track width, m α tire slip angle μ road adhesion coefficient λ s sliding variable η sliding variable φ roll angle ψ yaw angle δ steering angle
... Fig. 9. Test track geometry at UCB Richmond Field Station, California, USA. 1 Details and further references about the measuring equipment used can e.g. be found in [16]. ...
A driver model is designed which relates the driver's action to his perception, driving experience, and preferences over a wide range of possible traffic situations. The basic idea behind the work is that the human uses his sensory perception and his expert knowledge to predict the vehicle's future behavior for the next few seconds (prediction model). At a certain sampling rate the vehicle's future motion is optimized using this prediction model, in order to meet certain objectives. The human tries to follow this optimal behavior using a compensatory controller. Based on this hypothesis, human vehicle driving is modeled by a hierarchical controller. A repetitive nonlinear optimization is employed to plan the vehicle's future motion (trajectory planning task), using an SQP algorithm. This is combined with a PID tracking control to minimize its deviations. The trajectory planning scheme is experimentally verified for undisturbed driving situations employing various objectives, namely ride comfort, lane keeping, and minimized speed variation. The driver model is then applied to study path planning during curve negotiation under various preferences. A highly dynamic avoidance maneuver (standardized ISO double lane change) is then simulated to investigate the overall stability of the closed loop vehicle/driver system.
... As the wheel angular velocity can easily be measured with sensors, only the vehicle velocity is needed to calculate the wheel slip λ. The vehicle longitudinal velocity can be directly measured [9], [10], indirectly measured [11], and/or estimated through the use of observers [12], [13]. Since the problem of measuring the longitudinal velocity is out of the scope of this paper, we assume that both the vehicle velocity and the wheel angular velocities are directly measured. ...
During skid braking and spin acceleration, the driving force exerted by the tires is reduced considerably, and the vehicle cannot speed up or brake as desired. It may become very difficult to control the vehicle under these conditions. To solve this problem, a second-order sliding-mode traction controller is presented in this paper. The controller design is coupled with the design of a suitable sliding-mode observer to estimate the tire-road adhesion coefficient. The traction control is achieved by maintaining the wheel slip at a desired value. In particular, by controlling the wheel slip at the optimal value, the proposed traction control enables antiskid braking and antispin acceleration, thus improving safety in difficult weather conditions, as well as stability during high-performance driving. The choice of second-order sliding-mode control methodology is motivated by its robustness feature with respect to parameter uncertainties and disturbances, which are typical of the automotive context. Moreover, the proposed second-order sliding-mode controller, in contrast to conventional sliding-mode controllers, generates continuous control actions, thus being particularly suitable for application to automotive systems.
Mathematical models of vehicle dynamics will form essential components of future autonomous vehicles. They may be used within inverse or forward control loops, or within predictive learning systems. Often, nonlinear physical models are used in this context, which, though conceptually simple (especially for decoupled, longitudinal dynamics), may be computationally costly to parameterise and also inaccurate if they omit vehicle-specific dynamics. In this study we sought to determine the relative merits of a commonly used nonlinear physical model of vehicle dynamics versus data-driven models in large-scale real-world driving conditions. To this end, we compared the performance of a standard nonlinear physical model with a linear state-space model and a neural network model. The large-scale experimental data was obtained from two vehicles; a Lancia Delta car and a Jeep Renegade sport utility vehicle. The vehicles were driven on regular, public roads, during normal human driving, across a range of road gradients. Both data-driven models outperformed the physical model. The neural network model performed best for both vehicles; the state-space model performed almost as well as the neural network for the Lancia Delta, but fell short for the Jeep Renegade whose dynamics were more strongly nonlinear. Our results suggest that the linear data-driven model gives a good trade-off in accuracy and simplicity, whilst the neural network model is most accurate and is extensible to more nonlinear operating conditions, and finally that the widely used physical model may not be the best choice for control design.
This paper presents two Fault Tolerant schemes for more reliable spacing control of an autonomous vehicle. The nonlinear longitudinal model of the vehicle is described by the Lipschitz representation. Based upon this representation, a state feedback integral controller is designed, as well as, fault estimation observers. This proposal work is focused on Lyapunov theory ensuring H∞ criterion and L2-gain norm in the development of the Linear Matrix Inequality constraints. The design also includes an estimation of vehicle states and an additive fault with the purpose of achieving highly robust and safe vehicle inter-distance control. To carry out this work, experiments are highlighted on prototype vehicle to validate the proposed FTC scheme in different autonomous driving scenarios. © 2018 International Association for Mathematics and Computers in Simulation (IMACS)
In this contribution, a fault-tolerant control strategy for the longitudinal dynamics of an autonomous vehicle is presented. The aim is to be able to detect potential failures of the vehicle's speed sensor and then to keep the vehicle in a safe state. For this purpose, the separation principle, composed of a static output feedback controller and fault estimation observers, is designed. Indeed, two observer techniques were proposed: the proportional and integral observer and the descriptor observer. The effectiveness of the proposed scheme is validated by means of the experimental demonstrator of the VEDECOM (Véhicle Décarboné et Communinicant) Institut.
