Improvement of the Autodriver Algorithm for Autonomous Vehicles Using Roll Dynamics (original) (raw)

Autonomous Vehicles, Autodriver Algorithm, and Vehicle Dynamics

IEEE Transactions on Vehicular Technology

A given road can be expressed mathematically in a global (or world) coordinate frame. Following the road can be substituted by following the loci of its curvature center and turning at the right circle of curvature. Considering that a vehicle in motion is always in turn about an instantaneous rotation center relative to the ground, an autonomous vehicle capable of following a given path by coinciding the rotation center of vehicle at every moment on the curvature center of the road could be designed. The dynamic reactions of the vehicle influence its path of motion and make its rotation center to depart from the desired path of the curvature center of the road. In this study the Autodriver algorithm control strategy to front-wheel-steering vehicles has been developed and a control loop is introduced to compensate the present errors generated by the differences of the desired locating on the road and the real position of the vehicle.

Path Following of Autonomous Vehicles in the Presence of Sliding Effects

IEEE Transactions on Vehicular Technology, 2012

In this paper, a new controller for the pathfollowing problem of a car-like autonomous vehicle is proposed. This controller is based on a new transformation that, for the case of roll with no slip, transfers the model to a chain form. However, in many practical cases, due to the strong effects of tire slip angles, a roll with no slip assumption is invalid. To cope with the presence of slip, a novel controller is proposed. The new method is based on a vehicle kinematical model, where the tire slip angles are taken into account. The control design method utilizes a peak-to-peak criterion and linear matrix inequality (LMI) tools to attenuate tire slip angle effects on the closed-loop performance.

Autodriver Autonomous Vehicles Control Strategy

Procedia Computer Science, 2018

Technically, a given geometric curve can be traced and recovered by its curvature center loci. We assume that a road is given mathematically and we are able to determine its curvature center. To follow a road, we make a vehicle to turn about the road curvature center at the right radius of curvature. We design an autonomous vehicle control that follows a given path by adjusting the vehicle rotation center to be on the road curvature center. The dynamics of the vehicle cause not to follow the road and the right path of motion. It means that its rotation center will move off from the road curvature center. We therefore develop and introduce an autodriver algorithm control to compensate the possible errors between the desired location on the road and the actual location of the vehicle.

A path following control architecture for autonomous vehicles

Bachelor Thesis, 2014

After planning, adequate actions have to be taken. This thesis presents a control architecture for an autonomous model car to achieve a stable and accurate path following behavior. This encompasses a steering controller to drive desired curvatures computed by a pure pursuit algorithm. Additionally, an open-loop controller was implemented to determine a safe speed for driving curves and stopping on the path’s end.

Path Following for Autonomous Vehicle Navigation based on Kinodynamic Control

Journal of Computing and Information Technology, 2008

This paper addresses the path following problem for autonomous Ackermann-like vehicle navigation. A control strategy that takes into account both kinodynamic and configuration space constraints of the vehicle, denoted as Traversability-Anchored Dynamic Path Following (TADPF) controller is presented. It ensures secure vehicle commands in presence of obstacles, based on traversability information given by a global navigation function. By additionally using a reference point on the global smooth path, the local vicinity path configuration with respect to the vehicle is taken explicitly into account to ensure smooth and stable path following. Furthermore, a previously developed Sliding Mode Path Following (SMPF) controller that results in fast convergence rate and low path following error but which does not consider kinodynamic constraints, is augmented by the the kinodynamic and configuration space constraints check of the TADPF controller. The new proposed control strategy denoted as TADPF-SMPF controller thus combines advantageous characteristics of both original control strategies for path following, yielding inherent safety and vehicle dynamics margin. All three control strategies are verified in simulation, whereas the TADPF and TADPF-SMPF path following schemes are also verified experimentally.

Modeling the Turning Speed and Car Following Behaviors of Autonomous Vehicles in a Virtual World

Ingeniería, Investigación y Tecnología, 2015

This article deals with mathematical models for controlling vehicles behavior in a virtual world, where two behaviors are considered: 1) curve turning and 2) car following situations, in this last is essential to provide a safety distance between the leader and the follower and at the same time keep the follower not delayed with respect to the leader, and in a curve turning the complexity is to provide a safety speed inside the curve and keep the car inside the lane. Using basic information as vehicles position, mathematical models can be developed for explaining the heading angle and the autonomous vehicles speed on curves, i.e. the controlled by the models. A model that predicts the autonomous vehicle speed on curves is developed considering previous data in other curves. Two models that control the acceleration/deceleration behavior of autonomous vehicles in a car following situation are proposed. In the first model, the parameters are calibrated with a proposed algorithm which enables accuracy in order to imitate the human behavior for accelerating and braking, and the second model provides a safety distance between the follower and the leader at sudden stops of the latter and employs the acceleration/deceleration top capabilities to follow the leader car similar to the human behavior.

Vehicle lane following achieved by two degree-of-freedom steering control architecture

2016 IEEE International Conference on Intelligent Transportation Engineering (ICITE), 2016

The Advanced Driver Assistance Systems (ADAS) continue to grow rapidly in recent years. The ADAS can be divided into two categories: (1) vehicle lateral control assist whose actuation comes from power steering system, and (2) vehicle longitudinal control assist whose actuation comes from powertrain propulsion system and braking system. This paper proposes a two degree-of-freedom (2-DOF) controller architecture for an electric power steering system to achieve the function of the active steering angle control, integrated with upper-level supervisory control strategy implemented in lane following system (LFS). Also, we demonstrate its advantages over a one degree-of-freedom (1-DOF) controller structure. The vehicle dynamics with different active steering angle control architectures are analyzed to improve the angle control performance in lane following system. Furthermore, the active angle control algorithm was realized with an embedded-DSP and validated in a prototype vehicle. Keywords-steering angle control; lane following system; two degree-of-freedom controller I.

Adaptive Control System for Autonomous Vehicle Path Following

2019 International Conference on Applied Electronics (AE), 2019

Autonomy in vehicles is a rapidly expanding technology that is of interest in many major car companies. Autonomous driving enables safer journeys by removing human error in driving, as well as reducing driving time and fuel consumption by optimizing the engine and brake actuation. While certain autonomous functions in cars have been in use for over a decade, such as lane keeping and automatic parking these features are subject to specific scenarios. A fully automated vehicle needs to take into account the unpredictable and complex environments that cars drive in. The idea of a self-driving car in a city environment needs specific requirements such as reliable path following, accurate sensor data and safe drive. The scope of this paper is to create a simple path following solution which enables an autonomous car to accurately traverse along any given path.

Development of a trajectory following vehicle control model

Advances in Mechanical Engineering, 2016

Determination of the handling properties of a vehicle may be restrictive in some situations. A vehicle model coupled with a driver model may be necessary and even unavoidable to analyse the real road behaviour in the most basic form. Therefore, a fuzzy logic–based controller has been investigated for potential application in modelling driver. Using some particular and limited number of information from characteristics of human driving operation, the model aims to provide any flexible vehicle path reliably. It generates the vehicle’s trajectory through a number of specified points through which the vehicle must pass. The controller was modified to account for peripheral vision characteristic of human eye, as an input. The simulation is carried out in the MATLAB© programming environment using a Simulink© vehicle model. Both longitudinal and lateral controls were applied in the study. This article adds novel approaches to the limited existing published work on driver steering model usi...

Trajectory Based Autonomous Vehicle Following using a Robotic Driver

An autonomous vehicle following system in- cluding control approaches is presented in this paper. An existing robotic driver is used to control a standard passenger vehicle such that no modications to the car are necessary. Only information about the relative position of the lead vehicle and the motion of the following ve- hicle is required, and methods are presented to construct a reference trajectory to enable ac- curate following. A laser scanner is used to detect the lead vehicle and the following vehi- cle's ego-motion is sensed using an IMU and wheel encoder. An algorithm was developed and tested to locate the lead vehicle with RMS position and orientation errors of 65mm and 5:8 respectively. Several trajectory-based lat- eral controllers were tested in simulation and then experimentally, with the best controller having an RMS lateral deviation of 37cm from the path of the lead vehicle. A new trajectory- based spacing controller was tested in simula- tion which allows the ...