Hierarchical Evasive Path Planning Using Reinforcement Learning and Model Predictive Control

Motion planning plays an essential role in designing self-driving functions for connected and autonomous vehicles. The methods need to provide a feasible trajectory for the vehicle to follow, fulfilling different requirements, such as safety, efficiency, and passenger comfort. In this area, algorith...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.187470-187482
Main Authors: Feher, Arpad, Aradi, Szilard, Becsi, Tamas
Format: Article
Language:English
Subjects:
advanced driver assistance systems
Algorithms
artificial neural networks
Automobiles
Feasibility
Heuristic algorithms
Human performance
Lane changing
Learning
machine learning
Mathematical models
Motion planning
Neural networks
Parameters
Passenger comfort
Performance evaluation
Planning
Predictive control
Reinforcement learning
Training
Trajectory
Trajectory control
Trajectory planning
Vehicle dynamics
Vehicles
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Motion planning plays an essential role in designing self-driving functions for connected and autonomous vehicles. The methods need to provide a feasible trajectory for the vehicle to follow, fulfilling different requirements, such as safety, efficiency, and passenger comfort. In this area, algorithms must also meet strict real-time expectations, since, especially in an emergency, the decision time is limited, which raises a trade-off for the feasibility requirements. This article proposes a hierarchical path planning solution for evasive maneuvering, where a Twin Delayed DDPG reinforcement learning agent generates the parameters of a geometric path consisting of chlotoids and straight sections, and an underlying model predictive control loop fulfills the trajectory following tasks. The method is applied to the automotive double lane-change test, a common emergency situation, comparing its results with human drivers' performance using a dynamic simulation environment. Besides the test's standardized parameters, a broader range of topological layouts is chosen, both for the training and performance evaluation. The results show that the proposed method highly outperforms human drivers, especially in challenging situations, while meeting the computational requirements, as the pre-trained neural network and path generation algorithm can provide a solution in an instant, based on the experience gained during the training process.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3031037