518 research outputs found

    Vehicle Dynamics, Lateral Forces, Roll Angle, Tire Wear and Road Profile States Estimation - A Review

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    Estimation of vehicle dynamics, tire wear, and road profile are indispensable prefaces in the development of automobile manufacturing due to the growing demands for vehicle safety, stability, and intelligent control, economic and environmental protection. Thus, vehicle state estimation approaches have captured the great interest of researchers because of the intricacy of vehicle dynamics and stability control systems. Over the last few decades, great enhancement has been accomplished in the theory and experiments for the development of these estimation states. This article provides a comprehensive review of recent advances in vehicle dynamics, tire wear, and road profile estimations. Most relevant and significant models have been reviewed in relation to the vehicle dynamics, roll angle, tire wear, and road profile states. Finally, some suggestions have been pointed out for enhancing the performance of the vehicle dynamics models

    Road Condition Estimation with Data Mining Methods using Vehicle Based Sensors

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    The work provides novel methods to process inertial sensor and acoustic sensor data for road condition estimation and monitoring with application in vehicles, which serve as sensor platforms. Furthermore, methods are introduced to combine the results from various vehicles for a more reliable estimation

    Road Condition Estimation with Data Mining Methods using Vehicle Based Sensors

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    The work provides novel methods to process inertial sensor and acoustic sensor data for road condition estimation and monitoring with application in vehicles, which serve as sensor platforms. Furthermore, methods are introduced to combine the results from various vehicles for a more reliable estimation

    Kinematics-Based Analytical Solution for Wheel Slip Angle Estimation of a RWD Vehicle with Drift

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    Accurate real-time information of wheel slip angle is essential for various active stability control systems. A number of techniques have been proposed to enhance quality of GPS based estimation. This paper exhibits a novel cost-effective strategy of individual wheel slip angle estimation for a rear-wheel-drive (RWD) vehicle. At any slip condition, the slip angle can be estimated using only measurement of steering angle, front wheel speeds, yaw rate, longitudinal and lateral accelerations, without requiring GPS data. On the basis of zero longitudinal slip at both front tires, the closed-form solutions for direct computation of wheel slip angles were derived via kinematic analysis of a planar four-wheel vehicle, and then primarily verified by computational simulation with prescribed functions of radius of curvature, vehicle speed, sideslip and steering angle. Neither integration nor tire friction model is required for this estimation methodology. In terms of implementation, a 1:10th scaled RWD vehicle was modified so that the steering angle, the front wheel rolling speeds, the vehicle yaw rate and the linear accelerations can be measured. Preliminary experiment was done on extremely random sideslip maneuvers beneath the global positioning using four recording cameras. By comparing with the vision-based reference, the individual wheel slip angles could be well estimated despite extreme tire slip. Other vehicle state variables - radius of curvature, vehicle sideslip and speed - may also be directly obtained from the kinematic relations. This proposed estimation methodology could then be alternatively applied for the full range slip angle estimation in advanced active safety systems

    Sensor Fusion and Non-linear MPC controller development studies for Intelligent Autonomous vehicular systems

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    The demand for safety and fuel efficiency on ground vehicles and advancement in embedded systems created the opportunity to develop Autonomous controller. The present thesis work is three fold and it encompasses all elements that are required to prototype the autonomous intelligent system including simulation, state handling and real time implementation. The Autonomous vehicle operation is mainly dependent upon accurate state estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, 3-axis accelerometer, and 3-axis magnetometer. The IMU provides vehicle orientation in 3D space in terms of yaw, roll and pitch. Out of which, yaw is a major parameter to control the ground vehicle’s lateral position during navigation. The accelerometer is responsible for attitude (roll-pitch) estimates and magnetometer is responsible for yaw estimates. However, the magnetometer is prone to environmental magnetic disturbances which induce errors in the measurement. The initial work focuses on alleviating magnetic disturbances for ground vehicles by fusing the vehicle kinematics information with IMU senor in an Extended Kalman filter (EKF) with the vehicle orientation represented using Quaternions. The previous studies covers the handling of sensor noise data for vehicle yaw estimations and the same approach can be applied for additional sensors used in the work. However, it is important to develop simulations to analyze the autonomous navigation for various road, obstacles and grade conditions. These simulations serve base platform for real time implementation and provide the opportunity to implement it on real road vehicular application and leads to prototype the controller. Therefore, the next section deals with simulations that focuses on developing Non-linear Model Predictive controller for high speed off-road autonomous vehicle, which avoids undesirable conditions including stationary obstacles, moving obstacles and steep regions while maintaining the vehicle safety from rollover. The NMPC controller is developed using CasADi tools in MATLAB environment. As mentioned, the above two sections provide base platform for real time implementation and the final section uses these techniques for developing intelligent autonomous vehicular system that would track the given path and avoid static obstacles by rejecting the considerable environmental disturbance in the given path. The Linear Quadratic Gaussian (LQG) is developed for the present application, The model developed in the LQG controller is a kinematic bicycle model, that mimics 1/5th scale truck and cubic spline has been used to connect and generate the continuous target path

    A Systematic Survey of Control Techniques and Applications: From Autonomous Vehicles to Connected and Automated Vehicles

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    Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration

    Comparative analysis of MPC controllers applied to Autonomous Driving

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    Este trabajo presenta el diseño de un sistema de evasión de obstáculos, aplicable en situaciones de emergencia. La solución propone un MPC multivariable para controlar la posición, orientación y velocidad del vehículo autónomo. El controlador considera las limitaciones físicas del vehículo, así como la morfología de la vía para conseguir minimizar los posibles daños que puedan afectar al sistema y en consecuencia a la pérdida de control del vehículo. Las restricciones principales están basadas en las fuerzas laterales que afectan a los neumáticos, obtenidas de la implementación de los modelos cinemático y dinámico de la planta. Inicialmente, el controlador hace que el sistema siga una trayectoria predefinida. No obstante, tomará las acciones de evasión necesarias cuando detecte obstáculos, para conseguir realizar trayectorias libres de colisiones. Los resultados obtenidos tras la validación del sistema se presentan con el simulador para conducción autónoma CARLA.This work presents the design of an obstacle avoidance system, employable in emergency situations. The solution proposes a multivariable Model Predictive Controller (MPC) to control the position, orientation and velocity of an autonomous vehicle. The controller considers the vehicle0s physical limitations, as well as the road morphology, to minimize any possible damage to the system and the loss of control of the vehicle. Its main constraints are based on the lateral tire forces, obtained from the implementation of a kinematic and dynamic plant model. The controller, initially following a predefined trajectory, will take the needed evasive actions in order to perform a collision-free trajectory, in case of an obstacle detection. The results obtained from the system validation are presented with CARLA open-source simulator for autonomous driving.Grado en Ingeniería en Electrónica y Automática Industria

    State and parameter estimator design for control of vehicle suspension system

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    Modern vehicle stability and navigational systems are mostly designed using inaccurate bicycle models to approximate the full-car models. This results in incomplete models with various unknown parameters and states being neglected in the controller and navigation system design processes. Earlier estimation algorithms using the bicycle models are simpler but have many undefined parameters and states that are crucial for proper stability control. For existing vehicle navigation systems, direct line of sight for satellite access is required but is limited in modern cities with many high-rise buildings and therefore, an inertial navigation system utilizing accurate estimation of these parameters is needed. The aim of this research is to estimate the parameters and states of the vehicle more accurately using a multivariable and complex full-car model. This will enhance the stability of the vehicle and can provide a more consistent navigation. The proposed method uses the kinematics estimation model formulated using special orthogonal SO3 group to design estimators for vehicles velocity, attitude and suspension states. These estimators are used to modify the existing antilock braking system (ABS) scheme by incorporating the dynamic velocity estimation to reduce the stopping distance. Meanwhile the semi-active suspension system includes suspension velocity and displacement states to reduce the suspension displacements and velocities. They are also used in the direct yaw control (DYC) scheme to include mass and attitude changes to reduce the lateral velocity and slips. Meanwhile in the navigation system, the 3-dimensional attitude effects can improve the position accuracy. With these approaches, the stopping distance in the ABS has been reduced by one meter and the vehicle states required for inertial navigation are more accurately estimated. The results for high speed lane change test indicate that the vehicle is 34% more stable and 16% better ride comfort on rough terrains due to the proposed DYC and the active suspension system control. The methods proposed can be utilized in future autonomous car design. This research is therefore an important contribution in shaping the future of vehicle driving, comfort and stability

    On the vehicle sideslip angle estimation: a literature review of methods, models and innovations

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    Typical active safety systems controlling the dynamics of passenger cars rely on real-time monitoring of the vehicle sideslip angle (VSA), together with other signals like wheel angular velocities, steering angle, lateral acceleration, and the rate of rotation about the vertical axis, known as the yaw rate. The VSA (aka attitude or “drifting” angle) is defined as the angle between the vehicle longitudinal axis and the direction of travel, taking the centre of gravity as a reference. It is basically a measure of the misalignment between vehicle orientation and trajectory therefore it is a vital piece of information enabling directional stability assessment, in transients following emergency manoeuvres for instance. As explained in the introduction the VSA is not measured directly for impracticality and it is estimated on the basis of available measurements like wheel velocities, linear and angular accelerations etc. This work is intended to provide a comprehensive literature review on the VSA estimation problem. Two main estimation methods have been categorised, i.e. Observer-based and Neural Network-based, focusing on the most effective and innovative approaches. As the first method normally relies on a vehicle model, a review of the vehicle models has been included. Advantages and limitations of each technique have been highlighted and discussed

    Hierarchical Off-Road Path Planning and Its Validation Using a Scaled Autonomous Car\u27

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    In the last few years. while a lot of research effort has been spent on autonomous vehicle navigation, primarily focused on on-road vehicles, off-road path planning still presents new challenges. Path planning for an autonomous ground vehicle over a large horizon in an unstructured environment when high-resolution a-priori information is available, is still very much an open problem due to the computations involved. Localization and control of an autonomous vehicle and how the control algorithms interact with the path planner is a complex task. The first part of this research details the development of a path decision support tool for off-road application implementing a novel hierarchical path planning framework and verification in a simulation environment. To mimic real world issues, like communication delay, sensor noise, modeling error, etc., it was important that we validate the framework in a real environment. In the second part of the research, development of a scaled autonomous car as part of a real experimental environment is discussed which provides a compromise between cost as well as implementation complexities compared to a full-scale car. The third part of the research, explains the development of a vehicle-in-loop (VIL) environment with demo examples to illustrate the utility of such a platform. Our proposed path planning algorithm mitigates the challenge of high computational cost to find the optimal path over a large scale high-resolution map. A global path planner runs in a centralized server and uses Dynamic Programming (DP) with coarse information to create an optimal cost grid. A local path planner utilizes Model Predictive Control (MPC), running on-board, using the cost map along with high-resolution information (available via various sensors as well as V2V communication) to generate the local optimal path. Such an approach ensures the MPC follows a global optimal path while being locally optimal. A central server efficiently creates and updates route critical information available via vehicle-to-infrastructure(V2X) communication while using the same to update the prescribed global cost grid. For localization of the scaled car, a three-axis inertial measurement unit (IMU), wheel encoders, a global positioning system (GPS) unit and a mono-camera are mounted. Drift in IMU is one of the major issues which we addressed in this research besides developing a low-level controller which helped in implementing the MPC in a constrained computational environment. Using a camera and tire edge detection algorithm we have developed an online steering angle measurement package as well as a steering angle estimation algorithm to be utilized in case of low computational resources. We wanted to study the impact of connectivity on a fleet of vehicles running in off-road terrain. It is costly as well as time consuming to run all real vehicles. Also some scenarios are difficult to recreate in real but need a simulation environment. So we have developed a vehicle-in-loop (VIL) platform using a VIL simulator, a central server and the real scaled car to combine the advantages of both real and simulation environment. As a demo example to illustrate the utility of VIL platform, we have simulated an animal crossing scenario and analyze how our obstacle avoidance algorithms performs under different conditions. In the future it will help us to analyze the impact of connectivity on platoons moving in off-road terrain. For the vehicle-in-loop environment, we have used JavaScript Object Notation (JSON) data format for information exchange using User Datagram Protocol (UDP) for implementing Vehicle-to-Vehicle (V2V) and MySQL server for Vehicle-to-Infrastructure (V2I) communication
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