Autonomous Vehicles

Summary:

This research investigates the path-planning and path-tracking methodologies for autonomous vehicles.

Autodriver algorithm (https://doi.org/10.1177/1077546309104467 ) aims at developing a path-following algorithm for autonomous vehicles using road geometry data and vehicle dynamics. In this research, a novel smart Autodriver algorithm is developed according to practical implications utilizing a more realistic vehicle model and consideration of real-time applicability through a collaborative research with University of Waterloo, Canada. A ghost-car path-following approach is introduced to define the desired location of the vehicle at every instance during various manoeuvres. Key steady-state characteristics of turning vehicles, namely the curvature, yaw rate, and side-slip responses are discussed and used to construct a path-following controller based on the Autodriver algorithm. A feedback control based on Sliding Mode Control (SMC) is also designed and applied to minimize transient errors between the road and the vehicle positions. Finally, simulations are performed to analyse the path-following performance of the proposed scheme compared to a Model Predictive Controller (MPC) as a widely accepted popular method for autonomous vehicles. Hardware-in-the-loop (HIL) tests are also performed to investigate real-time applicability of the controllers. The results show promising controller performance in terms of error minimization, passenger comfort, and low computational cost for the proposed method.

Keywords: Autodriver algorithm, autonomous vehicles, path-following, road curvature, real-time control.

Highlights:

The concept of Tangent Point shows how the vehicle heading is in cornering with different speeds (see the relevant publications)

The instantaneous centre of rotation can be mapped into the vehicle body frame and act as a lookup table for steering angle and velocity

Path-tracking is translated as a "ghost-car" following

A simplified bicycle model is used for closed-loop controller design

Schematic control system structure

Hardware-in-the-loop setup used to validate the real-time applicability of the controller

This video shows a vehicle model tracking the reference path in the global frame view. The red dot shows the centre of curvature of the road at each instance, and the blue dot shows the instantaneous centre of rotation of the vehicle. The Autodriver algorithm aims at coinciding these two points in a feedforward structure, and a feedback loop compensates the transient errors.

This video shows a moving frame and depicts the accuracy of the path following. The small scale at the back of the vehicle is an indicator of the roll angle. The vehicle model includes 8DOF, namely longitudinal, lateral, yaw, roll, and wheels' rotations.

Research Outputs:

In-depth study of vehicle planar dynamics.
Further development of Autodriver algorithm with full closed-loop control strategy.
Introduction of a computationally-efficient path-tracking algorithm for real-time application.
Hardware-In-the-Loop (HIL) experiments conducted to evaluate the controller-in-the-loop performance of the proposed method, compared to Model Predictive Control (MPC)
Development of a path-tracking simulation environment in MATLAB/Simulink.
Publication: Milani, S., Khayyam, H., Marzbani, H., L. Azad, N., Melek, W., Jazar, R.N. (2020) Smart Autodriver Algorithm for Real-Time Autonomous Vehicle Trajectory Control. IEEE Transactions on Intelligent Transportation Systems, IEEE. (https://doi.org/10.1109/TITS.2020.3030236)
Publication: Milani S., Marzbani H., Khazaei A., Jazar R.N. (2020). Vehicles Are Lazy: On Predicting Vehicle Transient Dynamics by Steady-State Responses. In: Jazar R., Dai L. (eds) Nonlinear Approaches in Engineering Applications. Springer, Cham. (https://doi.org/10.1007/978-3-030-18963-1_1)
Publication: Siddiqi, R., Milani, S., Jazar, R., Marzbani, H. (2020). Ergonomic Path Planning for Autonomous Vehicles - An investigation on the effect of transition curves on motion sickness. IEEE Transactions on Intelligent Transportation Systems, IEEE. (https://doi.org/10.1109/TITS.2021.3067858)