Mode Switching Control Using Lane Keeping Assist and Waypoints Tracking for Autonomous Driving in a City Environment

This paper proposes a mode switching supervisory controller for autonomous vehicles. The supervisory controller selects the most appropriate controller based on safety constraints and on the vehicle location with respect to junctions. Autonomous steering, throttle and deceleration control inputs are used to perform variable speed lane keeping assist, standard or emergency braking and to manage junctions, including roundabouts. Adaptive model predictive control with lane keeping assist is performed on the main roads and a linear pure pursuit inspired controller is applied using waypoints at road junctions where lane keeping assist sensors present a safety risk. A multi-stage rule based autonomous braking algorithm performs stop, restart and emergency braking maneuvers. The controllers are implemented in MATLAB® and Simulink™ and are demonstrated using the Automatic Driving Toolbox™ environment. Numerical simulations of autonomous driving scenarios demonstrate the efficiency of the lane keeping assist mode on roads with curvature and the ability to accurately track waypoints at cross intersections and roundabouts using a simpler pure pursuit inspired mode. The ego vehicle also autonomously stops in time at signaled intersections or to avoid collision with other road users.

Steering Wheel AGV Path Tracking Control Based on Improved Pure Pursuit Model

Aiming at the path tracking problem of the AGV transfer platform of an Optical module installing and calibrating system, this paper designs a pure pursuit control strategy in which the preview distance changes adaptively according to the current speed of AGV and the curvature of the reference path. Firstly, AGV kinematics model and pure pursuit model are established according to the geometric relationship. Then fitness function is established with tracking deviation and steering stability, and Particle swarm optimization (PSO) algorithm is used to optimize the preview distance of pure pursuit model of AGV under various working conditions. During the tracking process, AGV selects the optimal preview distance according to the curvature of the reference path and the current speed. The simulation experiment results show that the improved pure pursuit control strategy containing curvature information of reference path can improve the adaptability of AGV when it is tracking complex path, guaranteeing tracking accuracy and steering stability.

Navigation and motion control techniques for surface unmanned vehicle and autonomous ground vehicle: a review

An excellent navigation, guidance, and control (NGC) system had a high impact on trajectory tracking and the following scenarios. Both scenarios will include the heading, tangent, and velocity parameters in the computation. However, the control system design problem is not a new issue in the unmanned surface vehicle (USV) and autonomous ground vehivle (AGV) due to this constraint faced by many researchers since early these autonomy developments. Hence, this paper listed and emphasizing the techniques, including techniques implementation, strength, and the algorithm's constraints, a fusion of several techniques implemented for vehicle's stability, a turning ahead, and heading estimation. This paper concerns the similar algorithm used in the USV and AGV. Most of the selected techniques are basic algorithms and have been frequently implemented to control both vehicles' systems. Previous research shows pure pursuit guidance is the most popular technique in AGV to control the degree-of-freedom (DOF) velocity and the dynamic rate (sway, surge, and yaw). Simultaneously, the line of sight (LOS) controller is very compatible with controlling the movement of the USV. In conclusion, the technique's simulation test needs further research that will expose in the actual situation.