Model-based Path Planning and Control for Autonomous Vehicles using Artificial Potential Fields

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This report presents the results of the graduation thesis from the TU Delft, performed at the Integrated Vehicle Safety department of TNO. The goal of this graduation project is to develop a unified path planning and tracking method for autonomous vehicles in highway driving scenarios, by making use of Artificial Potential Fields (APFs).

Most autonomous vehicles base their navigation control on first planning a path, which is then tracked by using a combination of feedback and feedforward control. The strength of using APFs is the possibility to integrate the path planning and tracking process. This concept has been extensively used in the field of robotics. The attractive and repulsive forces coming from the APF guide the robot towards the final goal while avoiding obstacles. This offers an intuitive way to represent the level of hazard experienced in the direct environment. However, considerably less research has been devoted to the application of APFs in the field of autonomous vehicles. Furthermore, the available research mostly treats the vehicle as a particle, thereby leaving out the more complicated vehicle dynamics.

Therefore, this research is aimed at including the vehicle dynamics into the path-planning process such as to generate feasible and desirable paths. A Model Predictive Control (MPC) framework is proposed to fulfill this task. The adopted vehicle model is given by the linear bicycle model, which represents the vehicle dynamics sufficiently well for highway applications. The two main manoeuvers of lane keeping and lane changing are executed with the aid of two different APFs that were designed for these specific purposes, respectively. A second order Taylor approximation is used to incorporate the APFs into the quadratic MPC cost function.

The Simulink model from TNO to simulate the controlled vehicle is modified by extending it to curved roads and by including the developed APF MPC algorithm. The resulting algorithm is capable of following curved highway lanes and overtaking slower vehicles at different velocities in the simulation environment. The results are compared with the previously developed path planner and lateral controller from TNO. In order to also deliver longitudinal control action, the model can be connected to the adaptive cruise control application of TNO.

It is concluded that the suggested method potentially offers a powerful solution to the navigation control of autonomous vehicles. Real-life experiments have to be done in the future to validate the performance of the controller.