Kinematic Model Of Vehicle Research Articles

Autonomous platooning of multiple ground vehicles in rough terrain

AbstractThis study proposes an autonomous platooning algorithm, composed of velocity, and path planning systems, of multiple ground vehicles in rough terrain. The velocity planning system aims to maintain desired distance between preceding and rear vehicles, and the path planning system generates reliable path to make the vehicles move safely under the given rough environment. To supply a reliable velocity command for the maintenance of the predefined distance, a disturbance observer (DOB)‐based control method is employed. An uncertainty‐free longitudinal kinematic model is first utilized for a nominal velocity generator and the DOB is added to the nominal velocity planner for handling various uncertainties generated during the autonomous platooning. On the other hand, the path planning method is developed by employing a passivity‐based model predictive control method. With a derived passivity constraint, the stability of the platooning architecture, that is, the platooning performance, is improved. In addition, the proposed path planning method utilizes dynamic and kinematic models of the ground vehicle as equality constraints, and limited range of states and control input as inequality constraints, and perception result about the vehicle's own neighborhood is employed for generation of the path only in traversable region. Its solution is obtained using the particle swarm optimization method. Then, to follow the generated velocity and path commands, a well developed driving controller, commercially obtainable, is utilized. Finally, field tests are conducted to validate the performance of the proposed algorithm.

Active vehicle obstacle avoidance based on integrated horizontal and vertical control strategy

ABSTRACT In this paper, an integrated control method is proposed which is based on a planning of vehicle’s path and speed with respect to obstacles and a model predictive control for tracking this path. The planning layer builds a model predictive control framework based on the vehicle kinematics model; based on the potential field theory, comprehensively considers the vehicle’s state information and the relative position and velocity information of the obstacles, establishes the potential field function, introduces the optimization objective function, and optimizes vehicle’s path and speed. The tracking layer builds a model predictive control framework based on the vehicle dynamics model, establishes an optimized objective function that takes the optimal front wheel rotation angle and optimal longitudinal acceleration as inputs, and constrains the lateral acceleration and yaw angular velocity to achieve the vehicle’s obstacle avoidance path track. A co-simulation platform of CarSim and Matlab/Simulink was built to analyse the performance of the vehicle under static and dynamic obstacles under different initial speed conditions. The results show that the vehicle can track the reference path and reference speed smoothly, realize the horizontal and vertical comprehensive control of active obstacle avoidance, and verify the effectiveness of the proposed control method.

Path and Control Planning for Autonomous Vehicles in Restricted Space and Low Speed

This paper presents models of path and control planning for the parking, docking, and movement of autonomous vehicles at low speeds, considering space constraints. Given the low speed of motion, and in order to test and approve the proposed algorithms, vehicle kinematic models are used. Recent works on the development of parking algorithms for autonomous vehicles are reviewed. Bicycle kinematic models for vehicle motion are considered for three basic types of vehicles: passenger car, long wheelbase truck, and articulated vehicles with and without steered semitrailer axes. Mathematical descriptions of systems of differential equations in matrix form and expressions for determining the linearization elements of nonlinear motion equations that increase the speed of finding the optimal solution are presented. Options are proposed for describing the interaction of vehicle overall dimensions with the space boundaries, within which a maneuver should be performed. An original algorithm that considers numerous constraints is developed for determining vehicle permissible positions within the closed boundaries of the parking area, which are directly used in the iterative process of searching for the optimal plan solution using nonlinear model predictive control (NMPC). The process of using NMPC to find the best trajectories and control laws while moving in a semi-limited space of constant curvature (turnabouts, roundabouts) are described. Simulation tests were used to validate the proposed models for both constrained and unconstrained conditions and the output (state-space) and control parameters’ dependencies are shown. The proposed models represent an initial effort to model the movement of autonomous vehicles for parking and have the potential for other highway applications.

Design of Dynamic Tracking System for Unmanned Intelligent Vehicle Trajectory Based on Inversion Method

When using automatic driving control system to track vehicle trajectory dynamically, there are certain errors in tracking curve. A dynamic tracking system for unmanned intelligent vehicle (UIV) trajectory based on inversion method is designed. The monitoring module transmits the information collected by the camera and the obstacle, edge and battery information judged by the sensor to the monitoring and control terminals through the TCP network communication protocol. At the visual end and the control end, according to the received information, the control end makes corresponding operation instructions for the UIV and transmits them to the inversion controller to control, so that the position and prediction error of the tracking system tends to zero. The vehicle kinematics model is made linear by system software to obtain the vehicle’s kinematics linear tracking error model to predict the future behaviour of the vehicle. Back-stepping control algorithm is used to complete the dynamic tracking of the unmanned intelligent vehicle trajectory. The experimental results show that the distance deviation is controlled in the range of ± 0.1 m and the azimuth deviation is in the range of ± 5°. The system can track the reference trajectory accurately and quickly in different scenarios as simulated using vortex seal time multimedia dynamics platform.

A Vehicle Trajectory Tracking Method With a Time-Varying Model Based on the Model Predictive Control

The vehicle trajectory tracking algorithm is one of the key and difficult issues of intelligent driving technologies. In current control algorithms for the vehicle trajectory tracking, there are three main assumptions: the standard working condition for the driving path, the same control model used for the entire control process, and a fixed value for the longitudinal vehicle speed. However, the above determinations in current control algorithms are inconsistent with the actual vehicle driving conditions. To overcome those problems, a vehicle trajectory tracking method with a time-varying model is proposed. The time-varying model is developed by using a two-dimensional vehicle kinematics model. This method considers the influences of the longitudinal speed and road curvature on the vehicle trajectory tracking stability under the low-speed complex driving condition. Thus, the proposed method can improve the trajectory tracking accuracy when the unmanned vehicle is located at the road with a sharp curvature under the low-speed complex driving condition. Moreover, the proposed model can achieve the real time calculation. Meanwhile, the prediction accuracy of the vehicle kinematics model is ensured. The proposed approach with the above characteristics can complete the trajectory tracking for the route composed of arbitrary curves. The results show that the proposed method can effectively improve the trajectory tracking stability of the unmanned vehicle on the roads with different curvatures under complex driving conditions.

Extraction of Preview Elevation Information Based on Terrain Mapping and Trajectory Prediction in Real-Time

Preview elevation values are used as inputs to suspensions to improve a vehicle’s ride. In a previous study, we obtained preview terrain elevation information in post-processing. In this paper, we contributed a method to reconstruct the terrain elevation map in real-time to extract preview elevation information according to the predicted trajectory. We use interdisciplinary knowledge to solve an engineering problem in the field of mechanical engineering, and the innovation of this manuscript belongs to Engineering Application Innovation. First, in the process of data collection, the terrain elevation map is updated with LIDAR measurements. A Kalman filter is used to update the elevation map. Second, as the vehicle moves, the uncertainty in the vehicle’s motion is estimated, and the error variance that propagates to the terrain elevation map is computed. Third, the vehicle trajectory is predicted based on a vehicle kinematics model. According to the predicted trajectory, the elevation information is extracted from the terrain elevation map in advance. The proposed method has been extensively evaluated with the simulations in Gazebo and outdoor environments, and the results demonstrate that the algorithm is effective, with a maximum error of 2.58 cm for the estimated elevation value.

Path Tracking Control for Urban Autonomous Driving

Abstract A path tracking controller for autonomous vehicles in urban environments is presented. Based on system inversion, the steering angle causing the vehicle to follow the path in absence of disturbances is calculated. Then, the lateral distance and the orientation error w.r.t. the path are compensated by a state feedback controller. Further, a decoupling of the velocity is considered in the system-inversion and the feedback controller. Therefore, ideally, the velocity does not influence path tracking and, hence, the requirements on velocity control are relaxed. To reduce the effort for parameter identification, the controller is intentionally based on a kinematic vehicle model requiring less parameters compared to an elaborated dynamic model. It is assumed that the effects of unconsidered system components, e.g., tire slip, are then compensated by the state-feedback controller. The approach is validated on a closed proving-ground in a simulated urban scenario. Herein, for driving velocities up to 14 m/s and curve radii of down to 10 m, an RMS tracking error for the lateral distance to the path of 7.2 cm was achieved. The control system will be used in TomTom’s autonomous car ‘Trillian’ that serves as a validation and research platform to evaluate high definition maps of road networks.

A Bearings-Only Trajectory Shaping Guidance Law With Look-Angle Constraint

This paper addresses the problem of achieving a desired impact angle against a stationary target with seeker's field-of-view limits. A bearings-only information based guidance law is investigated as a prospective solution. Analyzing the look-angle and the line-of-sight angle relationship, closed-form expressions of the guidance gains are derived for the desired impact angle and maximum look-angle constraints. A detailed analysis is carried out for lateral acceleration boundedness resulting in a design solution expressed in impact angle-maximum look angle space. Validating the guidance law, numerical simulations are performed using a kinematic vehicle model and a realistic model with given thrust and aerodynamic characteristics. Overall, the work offers an easily implementable guidance method with simple structure and closed-form guidance gains.

Synthetic baseline navigation using phase-coherent acoustic communication signals.

The development of a synthetic baseline navigation technique that self-localizes an autonomous underwater vehicle (AUV) using intermittent acoustic communications signals received by a single transducer is described, along with field results from in-ocean tests. The method uses the phase measurement at the output of a second-order phase-locked loop to create fine-scale pseudorange estimates in addition to, or in the absence of, a one-way travel time measurement based on the arrival time of the acoustic data packet. These range measurements are incorporated by an adaptive particle filter. This technique allows the vehicle navigation system to take advantage of multiple phase-derived range measurements made over the duration of a communication packet. These measurements, when incorporated with an appropriate filter and vehicle kinematic model, improve vehicle navigation at no additional cost in navigation-specific acoustic transmissions. This approach was demonstrated and evaluated with data collected at-sea using a REMUS 100 AUV (Hydroid, Inc., Pocasset, MA).

Path tracking control based on tube MPC and time delay motion prediction

Path tracking as an essential function of an autonomous vehicle ensures it tracks the desired path. Its performance could be degraded by wind, road conditions, vehicle parameter uncertainty and signal delay. This study proposes a path tracking control framework for autonomous vehicle by combining tube model predictive control (MPC) and time delay motion prediction (TDMP). Tube MPC with N-step reachable set is proposed to handle disturbances. A robust invariant set is computed efficiently using supporting function and guarantees state constraints in the presence of disturbances. In TDMP, a path during the signal delay is presented and a vehicle kinematics model is adopted to predict changes of vehicle position and yaw. Simulations show that the computational cost with support function for a robust positively invariant set is less than that with Multi-Parametric Toolbox. In addition, the proposed control framework not only addresses disturbances like wind, road conditions and vehicle parameter uncertainty, but also handles the steering signal delay through motion prediction.

On the Stability and Robustness of Hierarchical Vehicle Lateral Control With Inverse/Forward Dynamics Quasi-Cancellation

This paper studies a control architecture for vehicle lateral dynamics based on the execution of optimal trajectories via feedforward inverse model control. The focus here is on assessing the robustness of this arrangement when the vehicle real forward dynamics is not identically cancelled by the inverse model due to model approximations and parameter uncertainties. The trajectories that are considered are analytic solutions of the minimum square jerk optimal control problem for a simplified kinematic vehicle model in curvilinear coordinates. Various hypotheses are made concerning the mismatch between the inverse model and the actual forward dynamics of the vehicle. Closed-loop stability analysis shows that the studied control scheme guarantees asymptotic stability of both the nominal and the perturbed kinematic model with significant robustness margins. In addition to the theoretical robustness analysis, the same control scheme is validated in simulation using the industry-standard software for virtual vehicle testing IPG CarMaker.

Automatic Parking Controller with a Twin Artificial Neural Network Architecture

We propose an artificial deep neural network- (ANN-) based automatic parking controller that overcomes a stubborn restriction prevalent in traditional approaches. The proposed ANN learns human-like control laws for automatic parking through supervised learning from a training database generated by computer-aided optimizations or real experiments. By learning the relationships between the instantaneous vehicle states and the corresponding maneuver parameters, the proposed twin controller yields lateral and longitudinal maneuvering parameters for executing automatic parking tasks in confined spaces. The proposed automatic parking controller exhibits a twin architecture comprising a main agent and its cloned agent. Before the main agent assumes a maneuvering action, the cloned agent predicts the consequences of the maneuvering action through a Collision Checking and Adjustment (CCA) system. The proposed parking agent operates like a human driver in a manner that is characterized by an unplanned trajectory. In addition, the kinematics of the subject vehicle is not exactly modelled for parking control. The simulation results demonstrate that the proposed twin agent emulates the attributes of a human driver such as adaptive control and determines the consequences of the tentative maneuvering action under varying kinematic models of the subject vehicle. We validate the proposed parking controller by simulating the software-in-the-loop architecture using a PreScan simulator in which the dynamics of the virtual vehicle’s behavior resemble a real vehicle.

A Novel Integrated Simulation and Testing Platform for Self-Driving Cars With Hardware in the Loop

Effective simulation and testing environment is a vital part in the research of self-driving vehicles. It is capable of testing self-driving software and hardware quickly in a variety of virtual environments at low cost. However, as for the current mainstream simulation platforms, a considerable gap exists between the constructed virtual environment and the actual self-driving platform, which decreases the efficiency of development and makes it difficult to complete the migration from the virtual scenario to the real environment. Therefore, in this paper, we proposed a novel hardware-in-the-loop (HiL) simulation system. It takes the real hardware electronic control unit (ECU) of the self-driving vehicle as a part of the simulation platform, which improves the efficiency of development and testing, and also, the verified algorithms can be implemented into self-driving cars faster than ever before. The proposed HiL simulation system mainly consists of four parts: the vehicle kinematic model simulation, the multi-sensor simulation, the environment simulation, and the ECU hardware. Simulation experiments on applying the HiL system are used to verify the validity of self-driving algorithms in virtual scenes, including perception, planning, decision making, and control. Furthermore, algorithms that are tested in the simulation environment can be rapidly deployed into the real self-driving vehicles. In this paper, we also presented the verification processes of various algorithms, such as planning and control. These algorithms are implemented in the HiL system, and the experimental results show the validity of our proposed platform.

Optimal control problem of multi‐vehicle cooperative autonomous parking trajectory planning in a connected vehicle environment

The development of intelligent network and intelligent vehicle technology makes it possible for multi-vehicle cooperative driving. In this study, the multi-vehicle cooperative autonomous parking condition is addressed by establishing a vehicle kinematics model combined with dynamic constraints and endpoint and collision avoidance constraints. The autonomous parking trajectory planning problem is transformed into an optimal control problem. The shortest parking completion time is set as the optimal cost function, and the optimal control problem is discretised by using the Gauss pseudo-spectral method. Then, the non-linear programming problem is solved. Moreover, an initial guess generation strategy is proposed, and the iteration method is used to solve the problem by adding collision avoidance constraints to the collision situation of the vehicle to improve the convergence and robustness of the problem. The simulation model is built in MATLAB/Simulink to simulate multi-vehicle cooperative autonomous parking under different working conditions. The simulation results show that the proposed method can solve the multi-vehicle cooperative autonomous parking trajectory planning problem uniformly and effectively. Compared with the traditional pseudo-spectral method, the proposed method has a faster convergence speed.

Laser‐based SLAM automatic parallel parking path planning and tracking for passenger vehicle

Automatic parking system (APS) plays an important role in alleviating drivers' burden and improving vehicle safety. This study proposes a laser-based simultaneous localisation and mapping (SLAM) automatic parallel parking and tracking control scheme, which include environment perception and reconstruction, parking path planning, and path following. First, the proposed approach selects short-range lidar to enlarge its perceptron on parking environment and senses available parking space to build neighbourhood mapping with the help of simultaneous localisation. The test evaluation indicates that laser-SLAM has achieved good performance for recognising free spaces. Then, scene-dependent B-spline parallel parking path planning algorithm is proposed to optimise the parking path curve and makes the path meet vehicle kinematics model constrains well, in which vehicle actuator's dynamic performance and parking path smoothness are taken as key attributes. The experiment results indicate that the designed parallel parking path is better smooth. At last, a path following control method based on model predictive control (MPC) algorithm is proposed. The authors built an intelligent vehicle APS platform, and the results indicate that the designed MPC algorithms has excellent robustness and can minimise the tracking error. All the proposed scheme is quantitatively evaluated using different parking scene simulation with satisfactory performance.

UKF-Based Navigation System for AUVs: Online Experimental Validation

Modern autonomous underwater vehicles (AUVs) are currently involved in complex tasks and scenarios, and require accurate and robust navigation systems to estimate their position. However, since the Global Positioning System cannot be exploited underwater, the AUV position is not directly measurable in real time (unless using dedicated acoustic-based sensors), making the availability of a reliable navigation system even more crucial. In this context, the main role is played by the filter used to estimate the AUV motion, usually relying on simple kinematic vehicle models and equations linearization. A navigation strategy specifically thought for AUVs and based on an unscented Kalman filter is proposed and experimentally validated by the authors. Preliminary tests of the developed strategy have been carried out by running the navigation filter on experimental data acquired during the FP7 European ARROWS project. This initial validation has been performed totally offline. The AUVs navigated in dead reckoning without using navigation filters, whereas the proposed strategy has been compared to standard extended Kalman filter based ones, highlighting encouraging performances. To further validate the proposed navigation system, suitable sea tests have been performed. The navigation filter has been implemented online on an AUV and the vehicle controller relied only on it to navigate. The new validation procedure, whose results are reported in this paper, showed again the good performance of the chosen strategy, yielding satisfying results in terms of accuracy of vehicle position estimation.

Three-dimensional trajectory tracking of a hybrid autonomous underwater vehicle in the presence of underwater current

In this work, a six degrees-of-freedom (DOF) nonlinear kinematic and dynamic model of a Hybrid Autonomous Underwater Vehicle (H-AUV) is derived for the two modes of locomotion, propelled and gliding modes. A comprehensive linearization algorithm is developed to include both modes of locomotion. Starting with a three-dimensional time-parameterized curve of position history, Frenet-Serret frames are used to specify the unknown nominal states, which in turn are used to obtain the nominal inputs. A linear time-variant (LTV) state-space model is obtained, and a linear quadratic regulator (LQR) is designed and applied to the nonlinear model. The performance of the devised controller is assessed via a metric that computes the error between the actual and the desired position. Simulation results show that the LQR provides accurate tracking performance, even in the presence of underwater currents with bounded flow velocity. Moreover, the controller autonomously switches modes between propulsion and gliding to ensure minimal trajectory tracking error and energy consumption.

Research on Lane a Compensation Method Based on Multi-Sensor Fusion.

The curvature of the lane output by the vision sensor caused by shadows, changes in lighting and line breaking jumps over in a period of time, which leads to serious problems for unmanned driving control. It is particularly important to predict or compensate the real lane in real-time during sensor jumps. This paper presents a lane compensation method based on multi-sensor fusion of global positioning system (GPS), inertial measurement unit (IMU) and vision sensors. In order to compensate the lane, the cubic polynomial function of the longitudinal distance is selected as the lane model. In this method, a Kalman filter is used to estimate vehicle velocity and yaw angle by GPS and IMU measurements, and a vehicle kinematics model is established to describe vehicle motion. It uses the geometric relationship between vehicle and relative lane motion at the current moment to solve the coefficient of the lane polynomial at the next moment. The simulation and vehicle test results show that the prediction information can compensate for the failure of the vision sensor, and has good real-time, robustness and accuracy.

A Hybrid Approach to Side-Slip Angle Estimation With Recurrent Neural Networks and Kinematic Vehicle Models

This paper presents a supervised machine learning state estimation scheme that is able to estimate the current side-slip angle of a vehicle. It consists of a recurrent neural network with gated recurrent units, an additional input projection and a regression head. This structure has been chosen to limit the computational complexity of the model while preserving the expressiveness of the overall system. It will also be shown how equations of a simplified vehicle model can be incorporated to make use of existing domain knowledge. The results show that the neural network is able to reach an excellent estimation quality while generalizing over different tires, surfaces, and driving situations. Comparisons of different model variants on selected data subsets allow us to draw conclusions on the adaptation to varying parameters and show a quality improvement through the physical model. Evaluations with the mean squared error are complemented by more expressive fit and error plots to give a better understanding of the model behavior. All data for this paper have been collected with a Porsche 911 Turbo (Type 991 II) with a series sensor setup and an additional reference sensor.

Model predictive driver model considering the steering characteristics of the skilled drivers

First, the experienced drivers with good driving skills are used as objects of learning and road steering test data of skilled drivers are collected in this article. To better simulate human drivers, skilled drivers’ steering characteristics are analyzed under different steering conditions. Vehicle trajectories of skilled drivers are fitted by general regression neural network, and the ideal path trajectory is obtained. Second, the model predictive control algorithm is used to build the driver model. According to the requirements of quickly and steadily tracking the track of skilled drivers, vehicle kinematics model is established. The objective function and the corresponding constraint conditions of the driver model based on model predictive control were determined. Finally, numerical simulations results demonstrate that the driver model based on model predictive control can accurately track the reference trajectory of skilled drivers under the four typical steering conditions, and the tracking effect is better than the traditional single-point preview driver model and path tracking method based on a β-spline curve.