The Design of Performance Guaranteed Autonomous Vehicle Control for Optimal Motion in Unsignalized Intersections

Balázs Németh,Péter Gáspár

doi:10.3390/app11083464

Abstract

The design of the motion of autonomous vehicles in non-signalized intersections with the consideration of multiple criteria and safety constraints is a challenging problem with several tasks. In this paper, a learning-based control solution with guarantees for collision avoidance is proposed. The design problem is formed in a novel way through the division of the control problem, which leads to reduced complexity for achieving real-time computation. First, an environment model for the intersection was created based on a constrained quadratic optimization, with which guarantees on collision avoidance can be provided. A robust cruise controller for the autonomous vehicle was also designed. Second, the environment model was used in the training process, which was based on a reinforcement learning method. The goal of the training was to improve the economy of autonomous vehicles, while guaranteeing collision avoidance. The effectiveness of the method is presented through simulation examples in non-signalized intersection scenarios with varying numbers of vehicles.

Highlights

Introduction and MotivationThe handling of intersection scenarios is an important challenge in the research field of autonomous vehicles
This paper focuses on autonomous vehicle control, the proposed control design method may be used for further applications, considering the similarities with the intersections
The reinforcement learning (RL)-based agent was trained through 500 simulation scenarios, in which the number of vehicles, their initial velocities and positions were selected randomly; i.e., the vehicle number was varied between 1 and 7, the initial positions were selected between −20 m . . . −40 m and the initial velocities were varied between 10 km/h . . . 50 km/h

Summary

Introduction

Introduction and MotivationThe handling of intersection scenarios is an important challenge in the research field of autonomous vehicles. The ordering of autonomous vehicles in intersections has an impact on the energy consumption of the vehicles, traveling time, emissions and traveling comfort (due to the acceleration/deceleration maneuvers, for example) [1]. This leads to a multi-objective optimization task, which generally has a Pareto-optimal solution. The solution of the optimization problem regarding autonomous vehicles in intersections can require lengthy computations. This poses the challenge of the minimization of the computational time, e.g., finding approximations of the optimal solution. Considering position errors caused by human participants, robust control strategies are needed with which collision avoidance can be guaranteed

Objectives

Methods

Results

Conclusion